peter pazmany catholic university file2011.10.07.. tÁmop – 4.1.2-08/2/a/kmr-2009-0006 . 1....

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 1

Development of Complex Curricula for Molecular Bionics and Infobionics Programs within a consortial* framework**

Consortium leader

PETER PAZMANY CATHOLIC UNIVERSITYConsortium members

SEMMELWEIS UNIVERSITY, DIALOG CAMPUS PUBLISHER

The Project has been realised with the support of the European Union and has been co-financed by the European Social Fund ***

**Molekuláris bionika és Infobionika Szakok tananyagának komplex fejlesztése konzorciumi keretben

***A projekt az Európai Unió támogatásával, az Európai Szociális Alap társfinanszírozásával valósul meg.

PETER PAZMANY

CATHOLIC UNIVERSITY

SEMMELWEIS

UNIVERSITY

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 2

Peter Pazmany Catholic University

Faculty of Information Technology

ELECTROPHYSIOLOGICAL METHODS OF THE STUDY OF THE NERVOUS- AND MUSCULAR SYSTEM

Methods for analysing the bioelectric signals

www.itk.ppke.hu

(Az ideg- és izom-rendszer elektrofiziológiai vizsgálómódszerei )

(A bioelektromos jelek elemző módszerei)

RICHÁRD FIÁTH and GYÖRGY KARMOS

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 3

ELECTROPHYSIOLOGICAL METHODS OF THE STUDY OF THE NERVOUS- AND NUSCULAR SYSTEMHISTORY OF ELECTROPHYSIOLOGY

www.itk.ppke.hu

Contents

• Aims of the lecture• Types of bioelectric signals• Data acquisition – from analog to the digital domain• Noise• Filters• Basic signal processing methods• Analysis of single-unit activity(Spike sorting) and multi-unit activity• Advanced signal processing methods• Neurometrics

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 4

ELECTROPHYSIOLOGICAL METHODS OF THE STUDY OF THE NERVOUS- AND NUSCULAR SYSTEMELECTROMYOGRAPHY

AIMS:In this lecture the student will become familiar with the methods used for

the analysis of bioelectric signals. First of all the basics of data acquisition willbe introduced, namely how the analog electric signals are amplified andconverted into a digital form to became storable on computers and digestablefor the processing algorithms. This follows a short description of one of thegreatest enemies of signal analysis: noise. Filtering the signals can help us toget rid of some noise and artefacts from the recordings, and is used also toretrieve the signal of interest without the undesired frequency components.Basic methods for signal processing will be reviewed in the next section, likeaveraging, spectral analysis or correlation. After that the reader get acquaintedwith the procedure of spike sorting and other methods in single- and multi-unitanalysis. In the advanced methods section we can get an insight into a smallcollection of techniques frequently used in neuroscience. Lastly a quantitaveEEG method will be demonstrated, which makes it possible to detect andquantify abnormal brain states: Neurometrics.

www.itk.ppke.hu

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 5


www.itk.ppke.hu

TYPES OF BIOELECTRIC SIGNALS• Electrocardiogram (ECG)• Electromyogram (EMG)• Electroencephalogram (EEG)• Electroretinogram (ERG)• Local field potentials (LFP)• Multi-unit activity (MUA)• Single-unit activity (SUA)• Patch clamp recordings from ion channels• Optical recordings with voltage sensitive dyesBIOMAGNETIC SIGNAL• Magnetoencephalogram (MEG)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 6


www.itk.ppke.hu

DATA ACQUISITION

Signal processing in neuroscience means applying various algorithms tobioelectric signals that can be one-dimensional time series (e. g. ECG), multi-dimensional time series (e.g. EEG, LFP) or series of images (e.g. opticalrecordings). These type of signals are analog in nature: they are continuousboth in time and amplitude. Bioelectric signals are picked up usually byelectrodes and amplificated. Amplification has two steps: first the signal isamplified by an preamplifier that is followed by the main amplifier. Afteramplification the signal is filtered to remove undesired frequency components.This can be a band-pass filter or a notch filter to cut out the noise of the powerlines. The anti-aliasing filter is used to attenuate frequencies that are too highto be digitized by the analog-to-digital converter (ADC). The last step isconverting the signal to the digital domain: a sample-and-hold circuit samplesthe analog signal and the ADC performes the quantization. (Figure 1.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 7


Figure 1. The process of data acquisition from bioelectric sources.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 8


www.itk.ppke.hu

DATA ACQUISITION – A/D conversionMany tasks can be performed with analog processing (e.g. filtering,

rectifying), but digital technology has the real processing capability with itsrich variety of processing techniques. For digital processing of the signal, ithas to be converted into a discrete representation. Discretization of time meanssampling the continuous wave at a given interval. The rate at which the digitalvalues are sampled is called the sampling rate or sampling frequency. Theoriginal signal can be exactly reproduced if the sampling rate is at least twicethan the highest frequency of the signal (Nyquist sampling theorem). Theamplitude scale is made discrete by an analog-to-digital converter (ADC): thisprocess is called quantization and is performed by rounding or truncating ameasured real-value to an integer representation. The most important propertyof an ADC is the amplitude resolution which tells us the number of discretevalues (levels) that the ADC can produce over the range of analog values. Forexample an ADC with an 8-bit range has 2^8=256 levels. Another attribute ofthe ADC is the input range which is measured in Volts. (Figure 2.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 9


www.itk.ppke.hu

DATA ACQUISITION – A/D conversion – example

For example assume that we have a 16-bit A/D converter with an inputrange of 10 V. The preamplifier in the headstage amplifies the analog signal10x which is further amplified by the main amplifier by 1000. This results in atotal amplification of 10000x, so the range for the input of the acquisitionsystem is 10 V/10000 = 1 mV. The converter has 2^16 = 65536 levels whichgives a resolution at the input of 1 mV/65536 = 15 nV. This system has a veryhigh precision, but uses a lot of memory and storage capacity. To construct aproper ADC system we have to make a trade-off between the resolution, rangeand storage capacity depending on the needs of our application.

A/D conversion has several errors: quantization error (or quantizationnoise), aperture error and non-linearity.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 10


www.itk.ppke.hu

Figure 2. Schematic of analog-to-digital conversion with 3 bit resolution – Afterthe amplification of the analog signal a sample-and-hold circuit samples it indefined intervals. After that every sample will be quantized: the amplitudevalue of one of the A/D levels (in case of 3 bit resolution there are 8 levels)which is closest to the real amplitude of the signal at this timepoint will beassigned to the sample. Finally the results are stored on a storage device.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 11


www.itk.ppke.hu

NOISENoise is an undesired perturbation superimposed on the signal of interest. It

can originate from several sources: we can distinguish natural and human-made noises. Natural noises are random and can be biological or electric (ormagnetic). Biological noise is for example the activity recorded from neuronsfar from the extracellular electrode, the so called background activity, whichcan interfere with the signal of the neuron, which we would like to record andanalyse, and mask its activity. (Figure 3.)

Artifacts are unwanted alterations in the recordings and can be regarded assome noise-like phenomen. They originate from sources other than theelectrophysiological structure being studied. It can be electric like the hum ofthe power lines, mechanical (e.g. cabel movement) or biological such as theelectrical activity of the muscle or eye superposed on the EEG recording.Artifacts can be assigned to human-made noises and most of them can beavoided with careful experimental settings. (Figure 4., Figure 5.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 12


www.itk.ppke.hu

Figure 3. A1 – The strongbackground activity of neuronsmask the small amplitude singleunit activites and makes correctspike sorting impossible. A1 isrepresented in a greater timescaleon A2.B1 – A strong single unit activityoriginating from a neuron close tothe recording electrode can easilysorted because its amplitude ismuch higher than the amplitude ofthe background activity. B2 is B1displayed on a greater timescale.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 13


www.itk.ppke.hu

Figure 4. Examples of artifacts related to EMG.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 14


www.itk.ppke.hu

Figure 5. EOG (electro-oculogram) artifact present on all of the channels of a 2second segment of an EEG recording. The amplitude of the artifact is the highest atthe EOG electrode of course, where we record the activity of the eye directly.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 15


www.itk.ppke.hu

NOISEOn the other hand natural electric noises are always present and originate

mainly from the electric equipment of the recording chain (e.g. electrodes,amplifiers, A/D converters). Fortunately in modern devices the level of thenoise is minimal. Noise is usually characterized by its probability densityfunction (PDF) or by its power spectral density (PSD). Noises can be classifiedby their PSD, different type of noises are named after different colors: forexample there exist white, pink, brown etc. noise. The noise can be measuredalso as an electric power in watts or as voltage in volts.There are well-known examples for random electric noises:• Johnson–Nyquist noise or thermal noise: is generated by the random

thermal motion of charge carriers. The power spectral density of thermalnoise is nearly equal throughout the frequency spectrum so it is called awhite noise. The PDF of the amplitude is nearly Gaussian.

• Another examples for electric noises: shot noise, flicker noise (1/f noise),burst noise, avalanche noise.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 16


www.itk.ppke.hu

NOISEQuantization noise – The discretization error (due to truncation or

rounding) made during the analog-to-digital conversion of the bioelectricsignal. Its amplitude depends on the range and the resolution of the A/Dconverter, but usually it is a few μV or less.

The relation of the amplitude of some noise sources to the amplitude ofbioelectric signals recorded with different methods are presented on Figure 6.We can see, that fortunately the magnitude of most of the noises is so small,that it does not affect the signal of interest. Other types of noise can beprevented with proper tools, like for example the use of a Faraday cage toshield the bioelectric recordings from electromagnetic radiaton. The Faradaycage attenuates or completly blocks the electromagnetic waves outside it.Figure 7. shows a noisy local field potential recorded without applying aFaraday cage.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 17


www.itk.ppke.hu

Figure 6. The amplitude of different noises related to the amplitude ofbioelectric signals.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 18


www.itk.ppke.hu

Figure 7. Eightchannels of a local fieldpotential recordingcontaminated with 50Hz noise originatingfrom the power linesand with electrodemovement artifacts.Slow DC shifts are alsopresent.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 19


www.itk.ppke.hu

NOISE – SIGNAL-TO-NOISE RATIOSignal-to-noise ratio (SNR or S/N) is a signal quality measure and shows

how much a signal has been corrupted by noise. It is defined as the ratio ofsignal power (meaningful information) to the noise power (unwanted signal)corrupting the signal:

where P is the average power. Under certain conditions the SNR can becalculated as the square of the amplitude ratio. SNR is often expressed usingthe logarithmic decibel scale:

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 20


www.itk.ppke.hu

FILTERSFiltering in the field of bioelectric signals means removing of certain

frequency components from the signal, usually noise. The attenuatedfrequencies depend on the actual task: for the suppressing of the 50 or 60 Hzof the power lines a notch filter is used around the target frequencies or if wewant to extract the single unit activity from a wideband recording we can use aband-pass filter from 500 Hz to 5000 Hz, this will attenuate the frequencycomponents below 500 Hz and above 5000 Hz.

Filters can be studied either in the time domain or in the frequeny domain.Usually the operation of a filter is described in the frequency domain: itremoves the unwanted frequency components (stop band) while leaving theother intact (pass band) with a transition region (the border between the stopand pass bands) of zero width. This would be the ideal filter (Figure 8.). In realworld filters the gains in the pass and stop band are not constant, ripples maybe present and the width of the transition area is greater than zero. (Figure 9.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 21


www.itk.ppke.hu

Figure 8. Frequency responsefunction of an ideal band-passfilter. (on the right)

Figure 9. Simplified frequencyresponse function of a real low-pass filter. (on the left)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 22


www.itk.ppke.hu

FILTERSFilters can be classified upon many different bases:• analog (continuous-time) or digital (discrete-time )• linear or nonlinear• Analog filters can be: active or passive• Types of digital filters: infinite impulse response (IIR) or finite impulse

response (FIR)The best-known filter families:• Chebysev filter• Butterworth filter• Bessel filter• Elliptic filter

These modern linear filters are designed in the way of network synthesismethodology. All of them can be implemented on analog and also on digitalfilters. The difference between them is that they use a different polynomialfunction to approximate to the ideal filter response. (Figure 10.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 23


www.itk.ppke.hu

Figure 10. Frequency response functions of the four basic linearfilters and the ideal filter (dashed line). Each filter has its ownadvantage.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 24


www.itk.ppke.hu

FILTERSThe frequency response can be classified into a number of different band

forms describing which frequencies the filter passes and which it attenuates:• Low-pass filter – The low frequencies are passed, the high frequencies are

attenuated.• High-pass filter – The high frequencies are passed, the low frequencies are

attenuated.• Band-pass filter – Only frequencies in defined frequency band are passed.• Band-stop filter or band-reject filter – Only frequencies in a defined

frequency band are attenuated• Notch filter – Rejects just one specific frequency: it is an extreme band-

stop filter.The cutoff frequency is the frequency beyond which the filter will not

pass signals.The simplified frequency response function of the mentioned filter classes

are displayed on Figure 11.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 25


www.itk.ppke.hu

Figure 11. Simplified frequencyresponse functions of differentfilter classes.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 26


www.itk.ppke.hu

BASIC SIGNAL PROCESSING METHODS

• Properties of bioelectric signals• Classifying the methods• Signal averaging• Frequency (spectral) analysis – Fourier Transformations• Covariance• Correlation• Coherence• Laplace-transform• Z-transform

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 27


www.itk.ppke.hu

PROPERTIES OF BIOELECTRIC SIGNALSAmplitude – Means the values of the time-varying signal along the vertical-

axis. In case of bioelectric signals it is measured in mV, μV, mA, μA or fT.(MEG)

Duration – The duration means the length of the signal (time value -horizontal axis) or a particalur time interval of the signal. For example theduration of an action potential is around 1 ms.

Latency – The latency is the time delay between the onset of a stimulus andthe response it triggers. The term latency is used for example by evokedpotentials or by calculating the nerve conduction speeds.

Phase – In case of periodic signals the phase of the signal is the fraction of acomplete cycle elapsed as measured from a specified reference point andoften expressed as an angle.

The mentioned properties are displayed on Figure 12.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 28


www.itk.ppke.hu

Figure 12. – Properties of an EMG signal (stimulating the elbow and recordingthe response)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 29


www.itk.ppke.hu

CLASSIFYING THE METHODSSignal analysing methods can be grouped in several ways. For example

methods can be performed either in the time domain or in the frequencydomain. Another classification way is parametric versus nonparametric.• Methods (mainly) in the time domain:

Amplitude analysis, Period analysis, Auto- and cross-correlation, Hjorth slope descriptors, Phase analysis, Autoregression, Mimetic analysis, Signal averaging, Covariance, Current source density analysis

• Methods (mainly) in the frequency domain:Frequency analysis, Cross-spectrum, Coherence, Causality analysis, Inverse filtering, Kalman filtering, Complex demodulation

• Nonparametric methods:Amplitude analysis, Period analysis, Auto- and cross-correlation, Complex demodulation, Coherence, Spectral analysis, Hjorth slope descriptors

• Parametric methods:Inverse filtering, Kalman filtering, Mimetic analysis, Template matching

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 30


www.itk.ppke.hu

SIGNAL AVERAGINGSignal averaging is a technique used in the time domain to increase the

strength of the small amplitude signals of interest buried in noise. Withaveraging we can increase the signal-to-noise ratio (S/N) by a proportion to thesquare root of the number of measurements (N). Best examples of theapplications of signal averaging in electrophysioliogy are the evokedpotentials. (Figure 13.) For example lets assume that we have an EEGrecording with 40 μV background activity and an auditory evoked potentialwith 10 μV amplitude hidden in the background activity of the EEG. So in thiscase the S/N is ¼. To increase the S/N to 2/1 (8x increase) we have to average64 responses or epochs. (epochs are short segments cut out from thecontinuous recordings with defined time intervals, in case of the evokedpotentials this means usually a several hundred ms of the measurments beforeand after the stimuli) The ideal avering has to fulfil several conditions: thesignal and noise are uncorrelated, the timing of the signal is known, the signalis time locked to the stimulus and the noise is random with zero mean.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 31


www.itk.ppke.hu

Figure 13. How to createan average. Auditoryevoked potential of asleeping cat to 1/s clickstimuli. The local fieldpotential was recordedfrom the auditory cortexof the cat with a 24contact multielectrode.The signal averaging wasperformed on oneselected channel.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 32


www.itk.ppke.hu

FREQUENCY (SPECTRAL) ANALYSISSeveral methods for analyzing the bioelectric signals work in the time

domain, because these type of signals are mainly time series. However there isan other important approach to get out information from bioelectric recordings:analysis in the frequency domain or with another words, spectral analysis.

The Fourier series technique is used to decompose periodic functions intotheir cosine and sine components. For example a time domain signal, thesquare wave, can be decomposed into five sine waves, each with a differentfrequency and amplitude. From the complex Fourier series a transformationcan be derived, which transforms data from the time domain into the frequencydomain. This operation is called Fourier transform and with its help of it, wecan examine the signals in the frequency domain. The Fourier transform incontinuous time (analog signals) is referred to as the continuous Fouriertransform (CFT). In discrete time (digitized signals) it is called discrete Fouriertransform (DFT). An efficient algorithm used by computer programs, thatcalculates the DFT is the Fast Fourier Transform (FFT).

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 33


www.itk.ppke.hu

FREQUENCY (SPECTRAL) ANALYSISFourier series:

Complex Fourier series:

Continuous Fourier transform:

Discrete Fourier transform:

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 34


www.itk.ppke.hu

FREQUENCY (SPECTRAL) ANALYSISThe raw complex-valued output of the Fourier transforms is difficult to

interpret directly. Here are three approaches for interpretation:Power spectrum: It is computed by multiplying the FFT output with itscomplex conjugate. It can be normalized by dividing by the number of datapoints.Amplitude spectrum: It is the square root of the power spectrum.Phase spectrum: It is the arcus tangent of the quotient of imaginary and realparts of the FFT.

Spectral analysis is often used in EEG analysis to evaluate the classicalEEG frequency bands (delta, theta, alpha, beta, gamma). The frequencydomain characteristics in the EEG data are relevant because of the clinicalsignificance of the various rhythms. The spectrum in Figure 14. shows a clearpresence of the alpha rhythm, one of the most obvious components in the EEGin awake subjects with both eyes closed. Figure 15. shows an EMG recordingwith the calculated frequency spectrum.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 35


www.itk.ppke.hu

FREQUENCY (SPECTRAL) ANALYSIS

In physiological signals, interpretation of spectra requires caution becausethese time series are rarely stationary and usually contain both nonperiodic andperiodic components. Not all peaks in the spectrum directly correspond toactual physiological, periodic processes in the system at hand. It may containlow frequency components due to slow nonperiodic activity (e.g., trends) orhigh frequency components may be contaminated by high-frequencynonperiodic processes (e.g., sudden events). Because the periodic activity inphysiological signals is usually far from purely sinusoidal, spectralcomponents, called harmonics, can also appear at higher frequencies.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 36


www.itk.ppke.hu

Figure 14. Frequency spectrum of an EEG recording (Pz electrode). During the recording the eyes were closed, wich resulted in an increased alpha activity (red color).

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 37


www.itk.ppke.hu

Figure 15. EMG recording of aninterference pattern recorded fromthe biceps muscle. (upper image)The frequency spectrum of theinterference pattern is shown on thebottom image.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 38


www.itk.ppke.hu

COVARIANCECovariance is a measure of how much two random variables or sets of

random variables (for example the time series of bioelectric signals) changetogether. Between two random variables the covariance is:

Cov(X,Y) = E[(X-E[X])(Y-E[Y])],where E(X) and E(Y) are the expected values of X and Y. If X and Y arerandom vectors (with dimension m and n) than we get the mxn covariancematrix the following way:

Cov(X,Y) = E[(X-E[X])(Y-E[Y])’],where M’ is the transpose of M. The (i,j)-th element of this matrix is equal tothe covariance Cov(Xi, Yj) between the i-th scalar component of X and the j-thscalar component of Y. Random variables with zero coveriance are calleduncorrelated. The covariance matrix is used in several algorithms related tobioelectric signal analysis. (e.g. calculating the covariance matrix of theactivity recorded from two different locations of the brain)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 39


www.itk.ppke.hu

CORRELATIONThe correlation or Pearson product-moment correlation coefficient is a

measure of the degree of linear relationship between two variables X and Y orsets of random variable (for example the time series of bioelectric signals). Thecorrelation is defined as:

where cov(X,Y) is the covariance between X and Y, and σX and σY are thestandard deviations. The value of the Pearson correlation is +1 in the case ofmaximal positive correlation and -1 in the case of maximal negativecorrelation (anticorrelation). If the correlation is zero, than the two variablesare uncorrelated. Independent variables are always uncorrelated, but if twovariables are uncorrelated (i.e. they have a correlation value zero) we can notsay for sure that they are independent! The closer the coefficient is to either −1or 1, the stronger is the relationship between the two variables.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 40


www.itk.ppke.hu

CORRELATIONIf we have two time series of bioelectric signals X and Y with n samples

written as xi and yi where i = 1, 2, ..., n, recorded from two distinct locations,than the sample correlation coefficient is calculated the following way:

where x and y are the sample means of X and Y, sx and sy are the samplestandard deviations of X and Y. This method gives just one number as a result,so to gain more information about the relationship between two signals the useof the cross-correlation is suggested.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 41


www.itk.ppke.hu

CROSS-CORRELATIONCross-correlation is a measure of similarity of two waveforms as a function of a

time-lag applied to one of them. For continuous functions, f and g, the cross-correlationis defined as:

where f * denotes the complex conjugate of f. For discrete functions, the cross-correlation is defined as:

To understand how cross-correlation works, lets assume two real valued functions f andg, that differ only by an unknown shift along the x-axis. The cross-correlation findshow much g must be shifted along the x-axis to make it identical to f. The formulaslides the g function along the x-axis, calculating the integral of their product at eachposition. When the functions match, the value of is maximized. For examples seeFigure 16.-18. The auto-correlation is the cross-correlation of a signal with itself.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 42


www.itk.ppke.hu

Figure 16. Two sine waves with identical frequencies shifted by 5 time units along thex-axis. The bottom graph is the cross-correlation of the sine waves showing amaximum at T = 5 and repeating at multiples of the sine wave frequency. With thismethod we can study for example how much is the synchronicity between theactivites of different brain regions in the delta frequency band.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 43


www.itk.ppke.hu

Figure 17. Examples of two identical pulses shifted by 5 time units. Bottom graph isthe cross-correlation of the pulses showing a maximum at T = 5. These two pulsescan represent for example two action potentials generated by neurons.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 44


www.itk.ppke.hu

Figure 18. Examples of a wide-band, time series signals produced with a randomnumber generator and a smoothing filter. The two signals are shifted to the right by 5time units. Bottom graph is the cross-correlation of the signals showing a maximum atT = 5. These wide-band signals are good representations of real bioelectric signals.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 45


www.itk.ppke.hu

COHERENCECoherence is a normalized measure of linear correlation as a function of

frequency and is defined between two signals x(t) and y(t) as:

where Gxy is the cross-spectral density (or cross-spectrum) between x and y,and Gxx and Gyy the autospectral density of x and y respectively. The cross-spectrum is the DFT ot the cross-correlation, while the autospectral densitiesare the DFTs of the autocorrelations. Values of coherence are between zero andone. High coherence implies that amplitudes at a given frequency arecorrelated for example across EEG samples. On Figure 19. the coherence ofthree electrodes referenced to the Fz electrode is shown. The 1 minute lengthEEG data was recorded with 29 electrodes and the eyes were closed during therecording session. This resulted in the appearing of 8-13 Hz alpha oscillations,mainly on the occipital and pariatal recording sites.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 46


www.itk.ppke.hu

Figure 19. The coherence between Cz-Fz, T4-Fz and Oz-Fz. During the EEGrecording the eyes were closed, so a dominant alpha oscillation is present on most ofthe recording sites. The greater coherence values related to alpha activity can beobserved on all of the three coherence diagrams. (red color)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 47


www.itk.ppke.hu

LAPLACE TRANSFORMThe Laplace transform is used in the analysis of continuous time systems

and is related to the Fourier transform. The idea using transformations is thatin there are properties in the transformed domain that make some problemseasier. The Laplace transform is defined as:

It can be used to solve ordinary differential equations (ODE), for exampleit can be applied to the simplified ion channel (membrane) model:

where where R and C are constants corresponding to the membrane resistanceand capacitance, respectively.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 48


www.itk.ppke.hu

Z TRANSFORMThe z transform is the equivalent of the Laplace transform for discrete time

and it is useful for analyzing difference equations. Z-transform converts adiscrete time-domain signal into a complex frequency-domain representation:

The z transform has an another meaning too: standarization or auto-scaling.If we want to make two samples comparable (e.g. two normal distributions)than z transform converts values of samples into z-scores:

where zi are the z-transformed sample observations, xi are the original valuesof the sample, x is the sample mean and s is the standard deviation of thesample. Figure 20. shows an example for standardization by z transform.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 49


www.itk.ppke.hu

Figure 20. Z transform of two normal distributions with different means andstandard deviations. After the transformation the two new normal distributionsbecame comparable with zero mean and standard deviation with value one.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 50


www.itk.ppke.hu

ANALYSIS OF SINGLE-UNIT ACTIVITY

Means the analysis of neural action potential or spikes. To understand howthe nervous system functions, we have to understand first the spike activity ofthe indiviual neurons and after that how this activity is related to other neuralcells in the network. The neuronal spike activity can be regarded as a pointprocess. The Poisson process is a random point process where the events(action potentials) are identically distributed but the intervals between theevents are not independent. The function of neurons can be regarded as aPoisson process.

There are two types of single-unit activity recording: we can recordspontaneously from a given location of the brain (Figure 21.) or we record theevoked unit activity by sensory or electrical stimulation.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 51


www.itk.ppke.hu

Figure 21. Single-unit activity recorded with a tetrode

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 52


www.itk.ppke.hu

SPIKE SORTINGDuring extracellular recordings the electrode is inserted somewhere into the

extracellular matrix of the brain and records the activity of several neurons.The number of cells depends on several factors (electrode location in the brain,size of the recording sites, type of the electrode etc.), but in average the„eyeshot” of an electrode is about 150 μm. So theoretically we can record theaction potentials (AP) of neurons from the volume of a cylinder with a radiusof 150 micrometers. In the case of cortical targets this means approximatelythousand neural cells, mainly pyramidal cells. However the ampitude of theelectrical pulses of cells on the edge of the cylinder is so small, that it fadesinto the background activity. Because the aim of spike sorting is to determinewich spike (AP) corresponds to wich neuron, only the neural activity near tothe electrode can be considered practically useful for spike sorting. In practiceAPs with an amplitude of mininum 60 μV are big enough for reliable spikesorting, this means recording the activity of about 100 neurons from thevolume of a cylinder with 100 μm in diameter. (Figure 22.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 53


www.itk.ppke.hu

Figure 22. The „eyeshot” of a tetrode

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 54


www.itk.ppke.hu

Each neuron has action potentials of characteristic shape wich depends on thedistance and spatial localization of the electrode relative to the neuron and itsmorphology. Because the cells are at different distances from the electrode andalso have a different morphology, the features of the extracellular actionpotential can be used to differentiate the individual cells. The extracellular spikecan be approximated with the negative first derivative of the intracellular actionpotentials (at least its initial negative phase, Figure 23.). In an ideal case onlytwo neurons are visible, the signal is filtered, so no slow activity is present, andthe shape of the action potential differs (Figure 24.) Spike sorting can beperformed online, during the recordings with an amplitude or a windowdiscriminator or with offline analysis which means processing the data withspike sorting algorithms consisting of a few steps.Spike sorting allows the analysis of the activity of a few close-by neurons fromthe recording electrode, so the connectivity patterns of these neurons can bestudied which gives us valuable informations about the functioning of the brain.

WHY IS SPIKE SORTING POSSIBLE?

TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 55


www.itk.ppke.hu

0

-60mv

0

-60mv

A

B

Intracellular recording

Extracellular recording

A

B

A

B

Figure 23. Difference between extracellularly and intracellularly recorded action potentials.

TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 56


www.itk.ppke.hu

Intracellular recording

A

B

Extracellular recordingA B A A B B B A

Figure 24. Spike sorting in ideal case

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 57


www.itk.ppke.hu

THE ALGORITHM OF SPIKE SORTING1. Filtering the recordings2. Spike detection (voltage thresholding)3. Feature extraction or feature selection4. Cluster analysis5. Cleaning the cell clusters

Other issues: • Role of the interspike-interval (ISI) histogram• Analysing the cross-correlation histrograms• Tetrodes• Difficulties with spike sorting: Overlapping spikes, Bursting cells,

Electrode drift, Rarely spiking neurons etc.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 58


www.itk.ppke.hu

SPIKE SORTING – FILTERING

This is the first step of spike sorting: applying a band pass filter to therecorded wideband data to remove the unnecessary low frequency activity(like delta or gamma activity, artifacts etc.) and to make the action potentialsvisible. Noncausal filters with a band pass between 300 and 3000 Hz are usedusually, because the duration of spikes is about 1 ms long. The lower cutofffrequency is 300 Hz, which means that frequencies below 300 Hz (slowcomponents of the raw data) are filtered out. The upper cutoff frequency of thefilter (3000 Hz) is to remove noisy elements from the spike shapes.

An example of filtering is presented on Figure 25.: four channels of a rawwideband data are filtered offline with a FIR band pass filter between 300 and3000 Hz. After the filtering the spikes became clearly visible anddistinguishable.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 59


www.itk.ppke.hu

1-5000Hz

300-3000Hz

Wideband local field potential

Frequency band of action potentialsFiltering

Figure 25. - Filtering out the action potentials from local field potentials

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 60


www.itk.ppke.hu

SPIKE SORTING – SPIKE DETECTIONSpike detection is performed with a voltage (or amplitude) threshold. A

good choice on the threshold level is very important: if the threshold is to highthe action potentials with lower amplitudes are missed or if we use a lowthreshold for detection, noise components of the signal will be marked asspikes (false positives). The threshold level can be set manually orautomatically. Automatic thresholding levels can be calculated as a multiple ofthe standard deviation of the signal or with the method developed by QuianQuiroga:

where x is the band pass filtered signal and is an estimate of the standarddeviation of the background noise. After the detection the detected spikes(events) are stored (usually 64 datapoints are stored, which means a 2-3 mssegment of the data around the detected events). Finally the spike shapes haveto be aligned, in most of the cases they are aligned to their maximum. (Figure26.-27.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 61


www.itk.ppke.hu


Figure 26. Threshold detection and storing the events.

Voltage threshold

Event1 2 3 4 5 6 7 8

62


www.itk.ppke.hu

Figure 27. Aligning to the peaks.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 63


www.itk.ppke.hu

SPIKE SORTING – FEATURE EXTRACTIONThe aim of the feature extraction is dimensionality reduction: the decrease

of the dimension determined by the number of datapoints to a lowerdimensional space (usually two or three dimension) of a few features. Thechoice of the best features is not an easy task. The simplest way is to take thebasic characteristics of spikes: peak amplitude, peak-to-peak amplitude, widthof the spike, or its energy. However, in many cases these features do not givegood results in differentiating the spike shapes. The most used method forfeature selection is the principal component analysis (PCA).The first 2 or 3principal components are used as parameters, which contain more than 80% ofthe energy of the signal. Other possibilities to select the parameters forseparating the clusters of action potentials originating from different neuronsare the independent component analysis (ICA) or the use of wavelets (discretewavelet transformation - DWT). In an ideal case the two different spike shapesare easily distinguishable, so two features depending on the characteristics ofthe action potentials are good enough for a proper spike sorting (Figure 28.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 64


www.itk.ppke.hu

Figure 28. Feature extraction: two features depending on the characteristicsof the spike are choosen for spike sorting

e.g. peak amplitude(x-axis)

amplitude of the descendingleg

(y-axis)A

B

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 65


www.itk.ppke.hu

SPIKE SORTING – CLUSTERING THE DATAAfter we have selected the appropriate features, we have to group spikes

with similar features into different clusters. One cluster corresponds to oneindividual neuron. Clustering can be performed manually by drawing poligonsor ellipses around the clusters formed by the selected features in the 2-dimensional space or by delimiting the clusters with spheres in the 3D space.This low-level solution of clustering is time consuming and can have errors,for example if the clusters overlap than the manual selection of clusterboundaries is very subjective. Semi-automatic algorithms need just a smallintervention from the users, for example to set some initial parameters. The K-Means, the standard Expectation-Maximization (E-M) or the Super-Paramagnetic Clustering belong here. Automatic algorithms like the ValleySeeking or the T-distribution E-M algorithm are very fast, but work effectiveonly in the ideal cases, when the clusters are good separated. Figure 29. showsthe ideal case when two clearly distinguishable clusters are present.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 66


www.itk.ppke.hu

Figure 29. Displaying by the parameters - Classifying into groups (Clustering)

x

yA

BA

B

Clusteringamplitude of the descending

leg (y-axis)

e.g. peak amplitude(x-axis)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 67


www.itk.ppke.hu


Voltagethreshold

Event1 2 3 4 5 6 7 8

A B A A B B B A

Figure 30. The end result of the spike sorting in the ideal case

1....A2....B3....A4....A5....B6....B7....B8....A

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 68


www.itk.ppke.hu

SPIKE SORTING – CLEANING THE CLUSTERSIn real cases, the spike sorting is not an easy task, like it is in the ideal case

presented before, supervision of the clusters is advised after the clustering.Errors can be induced for example at the stage of threshold detection or bymanual or automatic clustering. Error is when a spike of a neuron is assignedto an another neuron because two clusters overlap in the feature space and thechoice to which cluster this spike belongs is not obvious. (Figure 31.) Wrongassignments of this type can be partly corrected by checking the interspikeinterval histograms of the clusters or by examining more feature spaces, whereperhaps this two clusters do not overlap. Artifacts, that are more or less similarto the action potentials, can also influence the sorting. Fortunately in most ofcases these artifacts are outliers in the feature space, but some clusteringalgorithms can assign these „false spikes” to some clusters despite this.Sometimes automatic algorithms separate one cluster of a neuron to twosmaller clusters, but by checking for similar clusters after clustering and bymergeing the separated clusters after that we can correct these type of errors.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 69


www.itk.ppke.hu

x

yA

B

x

yA

B

Figure 31. Two different solutions for clustering the spikes of the neurons, where the twoclusters overlap. In case 1, one of the spikes, that is at the edge of the two clusters bythe overlapping part, is assigned two cluster A. In case 2 the same spike is assignedtwo cluster B.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 70


www.itk.ppke.hu

SPIKE SORTING – OTHER ISSUESInterspike interval (ISI) histogram – The ISI histogram is a good help to

check whether there are incorrect spike assignments in the clusters. It displaysthe number of spikes following a given spike in certain time intervals. It iswell-known, that after a discharge there is a 1 ms long absolute refractoryperiod and a relative refractory period lasting for 2-3 ms, when the neuron„recovers” for the next action potential. So if a peak is present in the ISIhistogram before 5 ms, there is a big chance that we have spikes correspondingto other neurons in the examined cluster. (Figure 32. A)

Cross-correlogram – With this type of histogram the relationship betweenthe clusters can be examined. For example if a neuron B that is firing usuallyshortly after neuron A, than on their cross-correlogram we can observe a peakat the time where the probability of the discharge of neuron B is the highest.

Tetrodes – Tetrodes are microelectrodes with four contacts close to eachother, so the different contacts „see” the discharging neurons from differentdistances. This can be used to improve the sorting qualities. (Figure 33.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 71


www.itk.ppke.hu

Figure 32. A – ISI histogram of a cluster that was bad clustered: you can see apeak before 5 ms with 400 events. So about 15% of the spikes discharge in therefractory period. This is a too high percent to accept this cluster as good, but a fewpercent of the spikes are allowed in the refractory period, because perfect spikesorting in real cases is almost impossible. The ISI of a good cluster is presented on B.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 72


www.itk.ppke.hu

Figure 33. The side-view and front-view of a tetrode

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 73


www.itk.ppke.hu

DIFFICULTIES WITH SPIKE SORTINGOverlapping spikes – When two neurons discharge at the same time or

with a small delay, their spikes will overlap, resulting in a spike shape, that isthe sum of the action potentials of the two cells. (Figure 34.) Dealing withoverlapping spikes is a very challenging and an important task, severalsolutions were developed, but a major breakthrough is still needed.

Bursting cells – A bursting neuron is firing two or more spikes one afteranother, in a fast sequence. (Figure 35.) The amplitude of the spikes changesthrough the bursting: usually it decreases, but an increase is also possible.Several algorithms separate the spikes of bursting cells in different clusters,because of the difference in the amplitudes. The ISI histogram and the cross-correlogram can help to identify and manage bursting neurons. There is a peakpresent on the ISI histogram at 2 or 3 ms, or there can be more peaks when aburst consists of more than two spikes. If the bursting neuron was separated indifferent clusters, than the cross-correlogram of the two clusters shows astrong relationship between them (peak at 2 or 3 ms).

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 74


www.itk.ppke.hu

Figure 34. Examples of overlapping spikes (A, B, C) and a „good” spike (D)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 75


www.itk.ppke.hu

Figure 35. Example of twobursting neurons recorded fromthe thalamus of an anesthetizedrat. Both of the neuronsdischarge four times withdecreasing action potentialamplitudes. The shape of theAPs remain more or less similarduring the evolution of the burst.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 76


www.itk.ppke.hu

DIFFICULTIES WITH SPIKE SORTINGElectrode drift – The brain can slowly move around the electrode (the

brain pulsates for example when a too big window is drilled into the skull), soin this case the distance between the recording site and the discharging neuronis not constant. This results in the change of amplitude of the spikes. (Figure36.) If the electrode drift is continuous, than it can happen that the amplitudeof spikes suitable for sorting can decrease in such a rate, that no more accuratesorting is possible or we can also completely lose the signal of the nearbyneuron.

Non-Gaussian clusters – Most of spike sorting algorithms assume aGaussian distribution of the clusters, but usually this condition is not fulfilledin general because of several reasons (overlapping spikes, bursting neurons,electrode drift, multi-unit activity, non-stationary background noise,correlation between spikes and local field potentials), producing elongatednon-Gaussian clusters.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 77


www.itk.ppke.hu

Figure 36. Example of electrode drift (a 3 minute long segment of a tetroderecording from the somatosensory cortex of an anesthetized rat is shown at thetop) The initial amplitude of the action potentials of the cell are slowlydecreasing because of the electrode movement in the brain.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 78


www.itk.ppke.hu

ADVANCED SIGNAL PROCESSING METHODS

• Wavelet analysis• Principal component analysis (PCA)• Independent component analysis (ICA)• Current source density (CSD) analysis• Compressed EEG spectral array• EEG source localization methods• Quantitative EEG (Brain mapping)• Analysis of event-related potentials• Nonlinear techniques

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 79


www.itk.ppke.hu

WAVELET ANALYSISA wavelet is a wave-like oscillation with an amplitude that starts out at

zero, increases, and then decreases back to zero (Figure 37.). Wavelet analysisis very useful for analyzing physiological systems because, as opposed to mostclassical signal analysis approaches, it provides the means to detect andanalyze nonstationarity in signals. When performing spectral analysis on asampled time series, the spectrum reveals frequency components in the inputsignal. Because the spectrum represents the whole time domain epoch, it isuncertain where exactly any particular frequency component is located in time.In case of Fourier-based spectral analysis any choice of the epoch length isalways associated with a compromise between time and frequency resolution,it is impossible to choose an epoch length that will accommodate both a hightemporal and a high spectral resolution. A very high temporal resolution (smallepoch) is always associated with a low spectral resolution and vice versa. Thewavelet transformation eliminates this problem: it has an accurate time andfrequency resolution at the same time. (Figure 38- 39.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 80


www.itk.ppke.hu

Figure 37. – Classical wavelets: Morlet, Meyer and the mexican hat.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 81


www.itk.ppke.hu

Figure 38. – Spectra of single sweeps

single sweep EEG

single sweep TFR

absolutewavelet power

baseline

baseline correctedsingle sweep TFR

relativewavelet power

Time

Freq

uenc

y

100

50

1Hz

0 4sec

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 82


www.itk.ppke.hu

Figure 39. – Averaged spectra

-1500 -1000 -500 0 500 1000 1500

10

20

30

40

50

60

70

80

90

100 20

25

30

35

40

45

50

55

60

65

70

-1500 -1000 -500 0 500 1000 1500

10

20

30

40

50

60

70

80

90

100

1

-10

-8

-6

-4

-2

0

2

4

6

8

10

Averaged absolute wavelet power Averaged relative wavelet power

baseline

µV

100

200

-100

-200

Averaged ERP

µV

100

200

-100

-200

Averaged ERP

Freq

uenc

y (H

z)

Freq

uenc

y (H

z)

Time (ms) Time (ms)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 83


www.itk.ppke.hu

PRINCIPAL COMPONENT ANALYSIS (PCA)PCA can be used to reduce the number of variables (dimensionality

reduction) or to detect structure in the relationships among variables. It is anorthogonal transformation where the end results are uncorrelated, orthogonalvariables, called principal components. The first principal component has thehighest variance among the other principal components, the second principalcomponent has the second highest variance, etc. The first three principalcomponents contain appromixately 80% of the variance of the signal. PCA canbe performed by eigenvalue decomposition of a data covariance matrix or bysingular value decomposition of a data matrix. PCA has many applications onthe field of bioelectric signals: it can be used in EEG source localization, todetermine and remove unwanted background activities and artefacts to makedipole localizations more accurate. It is practical for removing artefacts (e.g.blink) from evoked potentials. In case of spike sorting it is used to reduce thedimension of the multidimensional spike shapes for faster and easier clusteringof action potentials. (from around 64 dimensions to 2 or 3 dimensions)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 84


www.itk.ppke.huFigure 40. The first three principalcomponents (pictures on the right) ofthree different spike clusters (pictureson the left, several hundred overlayedspikes). The thickness of the lines of theprincipal components represent themagnitude of the eigenvalues. The firsteight eigenvalues are displayed in theupper right corner of the pictures on theright. The principal components of thefirst two clusters, which were sortedfrom the same recording, are verysimilar, perhaps the spikes of these twoclusters originate from one neuron, thatbursts with changing spike amplitude.The third example at the bottom wasrecorded from a different brain location.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 85


www.itk.ppke.hu

INDEPENDENT COMPONENT ANALYSIS (ICA)ICA has similar applications related to bioelectric signals, than PCA. The

examples mentioned by the PCA are also good examples to the ICA method: itgives good features for the reduction of the dimensions in spike sorting andcan be used to detect and remove undesired artefacts (like ocular, movement,ECG artefacts) and background activities from the signals for a more accuratesource analysis. However ICA is a more sophisticated method compered toPCA: it generates patterns and loadings using a stricter criteria for statisticalindependence. A well-known principle is that different physical processesgenerate statistically independent signals. The EEG recorded from the scalp isthe summation of signals originating from multiple sources. ICA computesindividual signals that are statistically independent, and which are thereforelikely to have been generated by different physiological processes. Acommonly used algorithm for ICA is a gradient-descent method calledINFOMAX and is based on maximization of the entropy.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 86


www.itk.ppke.hu

COMPRESSED EEG SPECTRAL ARRAY (CSA)The compressed spectral array shows temporal changes in the power

spectrum of the recorded signals. Spectral analysis (FFT) on about 1 minutelong EEG segments (epochs) are performed at every minute, and thesespectrums are plotted before each other forming a semi-three-dimensionalgraph, with the frequency on the x-axis, time on the y-axis and power on the z-axis. (Figure 43.) This technique is used mainly during surgery to continuouslymonitor the deepness of the anesthesia. Other features like the heart rate, bloodpressure, somatosensory evoked potential, brain auditory evoked potential etc.are supervised together with the CSA.

On Figure 44. a CSA of a 15 minute sleep session of a cat is displayed. Wecan observe the decrease of the power of low frequencies related to slow-wavesleep (SWS) at the transition from SWS to rapid-eye movement (REM) sleep.The cat woke up for a couple of seconds and after that continued sleepingshortly reaching SWS state again.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 87


www.itk.ppke.hu

Figure 43. The scheme of a compressed spectral array.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 88


www.itk.ppke.hu

Figure 44. The compressed spectral array of a 15 minute sleep session of acat. Spectral analysis was made at every 45 seconds. The EEG was recordedfrom the surface of the auditory cortex.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 89


www.itk.ppke.hu

EEG SOURCE LOCALIZATION METHODSMany times there is a need to localize the intracranial sources of the scalp

EEG, for example in the case of a patient with focal epilepsy we want to knowthe exact locations of the epileptic foci in the brain or in scientific experimentsthe places of origin of the EEG signals can be very important. We could do thelocalization with the help of conventional EEG, but several factors limitaccurate localization including the large interelectrode distances or theattenuation and distortion of the signals by the volume-conducting medium,the scalp, the skull, the CSF and the brain itself.

To begin discover the area of source localization we have to familiarize uswith the forward problem. The forward problem is when we want to predictthe electric potential or magnetic field vector that would be externallymeasured with the electrodes from the activity of some sources were inside thebrain. To localize the EEG sources we have to solve the inverse problem:estimating the current density or activity values of the sources that generatedthe measured electric potential or magnetic field vector.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 90


www.itk.ppke.hu

EEG SOURCE LOCALIZATION METHODSThere are several difficulties that make source localization a challenging

task: for example artifacts and noise contamination of the EEG signal or thatan infinite number of sets of intracranial sources may produce exactly thesame potential distribution on the head surface. So the general inverse problemhas no unique solution, we have to make constraints on the number, type orlocation of the sources. This can be achieved by the use of different sourcemodels. These are called model-dependent methods.

MODEL-DEPENDENT METHODSThese are dipole localization methods and assume that the EEG or evoked

potential is generated by one or more intracranial dipole sources (equivalentdipoles). The methods can be classified into linear and nonlinear dipolelocalization methods.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 91


www.itk.ppke.hu

EEG SOURCE LOCALIZATION METHODSNONLINEAR DIPOLE LOCALIZATION METHODS

A single source dipole can be characterized by six parameters (3 positionparameters and the dipole moment vector). With these parameters and a modelof the volume conducting medium, the potential at each point on the scalpsurface can be calculated. A nonlinear least-squares minimization algorithm isused to calculate the 6 parameters of the source dipole, that fits to themeasured potentials. This method is sensitive to noise, artifacts, inaccuracies inthe head and is computationally demanding. Similar models related to this arethe fixed dipole model and the rotating dipole model. Other dipoles can beadded to the models up to a theoretical maximum that depends on the numberof electrodes.

One example for the clinical application is the equvivalent dipole modelingof epileptic spike potentials. It can determine propagation patterns of epilepticspikes.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 92


www.itk.ppke.hu

EEG SOURCE LOCALIZATION METHODSLINEAR DIPOLE LOCALIZATION METHODS

Linear methods assume a larger number of dipoles and the sources can bedistributed more widely within the cortex. The locations and orientations of thedipole sources are known, only the strength of each dipole source need to bedetermined. It is a faster compared to nonlinear methods. We can distinguishthree subgroups within linear methods. If there are N dipoles, and Melectrodes, than in case of:1. N<M - There is no solution to the inverse problem that matches the

recorded scalp potentials exactly. Example method: FOCUS.2. N=M - There is one unique solution to the inverse problem. Example

method: „spatial deconvolution” algorithm.3. N>M - There are infinite number of solutions to the inverse problem and

the one „minimum norm” solution is found. Example methods: corticalimaging technique (CIT), Low-resolution Electromagnetic Tomography(LORETA)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 93


www.itk.ppke.hu

EEG SOURCE LOCALIZATION METHODSIn case of linear source methods the temporal resolution of EEG can be

combined with the spatial resolution of imaging methods, like MRI or SPECTto achieve better accuracy.MODEL-INDEPENDENT METHODS

Model-independent methods do not require any assumptions about thenumber, type, or configuration of the sources in the brain.Topographic display methods (brain mapping): Algorithms that caninterpolate potentials to intermediate points between the scalp electrodepositions. Examples of such algorithms:• Nearest neighbor inverse distance-weighted• All-electrode inverse distance weigthed• Rectangular surface splines or rectangular three-dimensional splines• Spherical surface splines• Spherical harmonic expansion• Single dipole or multidipole source model

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 94


www.itk.ppke.hu

EEG SOURCE LOCALIZATION METHODSLaplacian methods are another group of model-independent methods, theseuse similar algorithms mentioned by topographic display methods.Multivariate Statistical Methods

PCA and ICA, that were described in a previous section, may be used todecompose an epoch of multichannel EEG into multiple linearly independentcomponents. The original EEG can be reconstructed as a linear combination ofall components. These components may be used as a starting point for dipoleanaysis or other source localization techniques.SOURCE LOCALIZATION IN MEG

The accuracy of localization of intracranial sources in case of MEG is notlimited by the smearing effects of the volume conducting medium on electricalpotentials, because all the tissues between sources and the magnetic fielddetectors are transparent to magnetic fields. So a simple homogenous spheremodel of the volume conductor is usually sufficient to obtain accurate sourcedipole localization.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 95


www.itk.ppke.hu

EEG SOURCE LOCALIZATION METHODSVOLUME CONDUCTOR MODELS

The only electrical property of the volume conductor that is usuallymodeled is the electrical conductivity. Models of the head as a volumeconductor may be classified as homogeneous or inhomogenous. Most commonmodels are based on spherical surfaces. The simplest model is the homogenoussphere model: the head is a perfect sphere with uniform electical conductivitythroughout. The three shell spherical model consists of the brain, the skull andthe scalp, all of the with different resistivities. (Figure 45.) The four spheremodel has an additional part: the layer of cerebrospinal fluid. A more realisticmodel can be achieved with the finite element method: important head regionsare decomposed into multiple small adjacent elements, approximately 1 to 2cm in length and thickness. The actual geometry of the sclap, skull and brainsurfaces may be obtained from MRI data. This volume conductor model isused for example in LORETA.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 96


www.itk.ppke.hu

Figure 45. The three shell spherical head model.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 97


www.itk.ppke.hu

QUANTITATIVE EEG (BRAIN MAPPING, QEEG)QEEG refers to a complex analysis of brainwave frequency bandwidths

that make up the raw EEG. The EEG data acquired are used to createtopographic color-coded maps that show electrical activity of the brain. Itprovides complex analysis of several brainwave characteristics like coherence,power, dominant frequency, symmetry, phase, and amplitude. The first brainmapping system the BEAM (Brain electrical activity mapping) was developedby Duffy in the 1970s. On Figure 46. the distribution of the power of differentfrequency bands on the scalp are displayed.

However, we have to be careful with brain maps, because in case EEGrecordings, recorded with a smaller number of electrodes (for example 19electrodes) the brain maps are generated with a lot of interpolation used,because the electrodes record just from a small area and the interelectrodedistances are relatively big. Interpolations give just assumptions what happensin brain areas not covered with electrodes, so it is recommended to record thebrain activity with as many electrodes as possible.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 98


www.itk.ppke.hu

Figure 46. Brain maps of the different frequency band powers. In parenthesesthere are the relative powers of the frequency bands related to EEG.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 99


www.itk.ppke.hu

Figure 47. Brain maps constructed on the basis of a nonlinear method(synchronization likelihood – introduced later). The distribution of synchronizationbetween the 33 electrodes to a reference electrode in the alpha1 band (8-11 Hz) inthe case of eyes closed (displayed on the left) and in the case of eyes opened (on theright side).

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 100


www.itk.ppke.hu

ANALYSIS OF EVENT-RELATED POTENTIALSEvent-related potentials (ERP) or evoked potentials (EP) are extracted from

the continuous EEG data (Figure 48.) using signal averaging. First, smallsegments (called epochs or sweeps) are cut out from the EEG. These epochsare usually several hundred milliseconds long and contain the start of thetask/stimulus and the brain activity that processes the actual task/stimulus.(Figure 49.) The amplitude of the ERPs are small compared to the backgroundactivity, so after a careful artifact rejection process, where the unusable sweepsare removed, the remaining ones are averaged to increase the signal-to-noiseratio. As a result the small-amplitude ERP components that were time-lockedto the onset of the tast/stimulus are revealed and can be further analized.(Figure 50.) For example differences of EP components between differentelectrode sites can be examined or the the ERPs originating from differentsubjects or subject groups can be also compered. (Figure 51.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 101


www.itk.ppke.hu

Figure 48. A 4 sec segment of a continuous EEG recording, recorded with 33 electrodes. The task of the subjects was to count in their head: they had to add numbers.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 102


www.itk.ppke.hu

Figure 49. – From the continuous EEG presented on the previous figure are cutted out 2second long segments called sweeps or epochs. These sweeps are time locked to the onsetof the actual tasks. So in this case the counting task began at zero time and for the analysisof the evoked potentials a 2 second long part of the EEG is used.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 103


www.itk.ppke.hu

Figure 50. – A one second long average of EEG sweeps from a GO/NOGO task. Afteraveraging the evoked potential buried in the background EEG activity can be examined.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 104


www.itk.ppke.hu

Figure 51. – The evoked potentials of two subjects doing a GO/NOGO task at the Fzelectrode compared to each other. A significant difference in the amplitudes of the latecomponent of the evoked potential can be observed (around 400 ms).

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 105


www.itk.ppke.hu

NONLINEAR TECHNIQUESLinear techniques were designed to detect properties or relationships within

or between time series generated by linear systems but these analysistechniques can fail when applied to nonlinear system. The numerous succesfulresults achieved with linear methods shows, that sometimes a linear process isa good approximation of the system’s behavior, but the brain can beconsidered as a nonlinear system, so there is a significant need for novel signalprocessing tools for studying nonlinear relationships in physiology as well as acritical necessity to evaluate the tools that have been developed over the pastdecades.

Nonlinear techniques can be used to the analysis of the EEG and there areattempts to predict and detect epileptic seizures or to automatically detect thedifferent stages of sleep with nonlinear methods.

Two nonlinear methods used in EEG analysis, Omega complexity andSynchronization likelihood, will be demonstrated in the following section.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 106


www.itk.ppke.hu

NONLINEAR TECHNIQUES – OMEGA COMPLEXITYΩ complexity is a measure of complexity of spatio-temporal dynamics of

the electric activity of the brain. It can be used with multichannel EEGrecordings. Lets assume we have K electrodes. The Ω complexity is calculatedfrom the K eigenvalues of the covariance matrix of the EEG data:

where λi (i=1…K) are the eigenvalues of the covariance matrix. Ω complexityis minimal (with value 1) if there is only a single generator present on all of theK measuring points and it reaches its maximum if K uncorrelated generatorsexist with equal power, one for each of the K electrodes. (Figure 52. left side)This complexity measure can be used to study brain macrostates withdurations of several minutes or longer, for example to isolate different sleepstages.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 107


www.itk.ppke.hu

NONLINEAR TECHNIQUES –SYNCHRONIZATION LIKELIHOOD

The synchronization likelihood (SL) gives a straightforward normalizedestimate of the dynamical interdependencies between two or moresimultaneously recorded time series. It is suitable for the analysis of non-stationary data, like the EEG or MEG. The SL is a measure which describeshow strongly channel k at time i is synchronized to all other M-1 channels. TheSL has a range between a reference value P and 1, where P << 1 (usually0.05). In case of SL is equal to P, than all M time series are uncorrelated. If SLis equal to one, than the synchronization between all M time series is maximal.(Figure 52. right side) Modifications of SL can be obtained by averaging overthe time index i, averaging over the channel index k or both.

Examples to the use of the SL are shown on Figures 53-55.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 108


www.itk.ppke.hu

NONLINEAR TECHNIQUES –SYNCHRONIZATION LIKELIHOOD

The synchronization likelihood has the following properties:• SL increases if the coupling between two systems increases

• SL can detect non-linear coupling between systems

• SL can detect a change between the dynamical systems with high timeresolution

• SL is fairly robust in the case of considerably noisy data

Application fields of SL to EEG and MEG data

• Epilepsy: synchronization between channels during and before the seizures

• Synchronization changes during eye opening and closing in the alpha band

• Gamma band synchronization in MEG data.

2011.10.07..TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 10

9


www.itk.ppke.hu

Figure 52. Values of Omega complexity and Synchronization likelihood in case of three differentsignal types. In case of white noise (a), the synchronization between the channels is minimal,so SL has a small value, while the Omega complexitiy is maximal. Using sine waves (b) withthe same frequency, that are also in the same phase, means maximal synchronization withSL=1 (maximal value) and „Omega complexity”=1 (minimal value). Applying the two measuresto EEG data (c), their values will be somewhere between the values shown by the white noise(no synchronization) and sine waves (maximal synchronization).

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 110


www.itk.ppke.hu

Figure 53. Maps of Synchronization likelihood in the gamma band (30-50 Hz)between all of the 33 electrodes, when the eyes were closed (left) or opened(middle). The SL is averaged over the time dimension. The difference of thetwo SL maps is displayed on the right side of the figure. Red areas meanhigher synchronization, while blue areas mean slight or no synchronizationbetween the appropriate channels. The SL between the same electrodes arealways maximal (red colored main diagonal on the first two images), so theirdifference is zero (blue colored main diagonal on the difference image). Thenumbers next to matrices are the electrode numbers.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 111


www.itk.ppke.hu

Figure 54. Topographic brain mapsconstructed from the SL differencemaps in different frequency bandsreferenced to the centralelectrodes (Fz, Cz, Pz). We getthese maps by selecting one row(or column, because the differencematrix is symmetrical) from thedifference matrix that correspondsto the actual reference electrode.This row contains how much thereference electrode issynchronized to the otherelectrodes. Red colors meanhigher synchronization, while bluecolors indicate slight or nosynchronization on the given brainregion.

www.itk.ppke.hu

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 112


Figure 55. The change of SL in time at every recording site according to areference electrode in the alpha1 band (8-11 Hz) in case of eyes closed (bottomleft image) or eyes open (bottom right image). The red colors indicate highersynchronization, while the blue colors mark slight or no synchronization at a giventime-point. The upper images are the averages of the rows of the bottom images:on the left, the change of the SL average over time in case of eyes closed isdisplayed, while on the left the time evolution of the average SL is shown, whenthe eyes were open.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 113


www.itk.ppke.hu

NEUROMETRICS (NM)

The method seveloped by Dr. E. Roy John (1924-2009) at the Brain Reserach Laboratories, New York University Medical Center. Neurometrics is a multivariate statistical method for the evaulation of EEG and ERP (event related potentials) changes. It uses standardized qualitative properties for describing the deviation from a normal database. It is a „statistical help” for diagnosing neurological and psychiatric disorders.

Statistics can be applied to the analysis of pathological EEG signals. In the EEG recorded at relaxed state the amplitude and frequency-changes happen seemingly random, but actually these are lead by statistically regular and törvényszerű processes.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 114


www.itk.ppke.hu

NEUROMETRICS

Advantages of Neurometrics against conventional EEG:• It gives a precise, quantitative and reproducable estimation of the

deviation from the normal brain activity• Converting the data into a standardized dimension makes it possible to

create topographical statitistical brain maps• Quantitative characterization of some brain disorders is also possible• Multivariate statistical techniques can be applied• Subgroups of the patients can be determined• The development of the disorders can be quantitatively characterized

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 115


www.itk.ppke.hu

STEPS OF NEUROMETRICS• Selecting at least a 2 minute long representative segment from a 20 minute

length EEG recording:The relative spectrum is calculated from a minimum 20 second long artifact-free EEG period.For frequency analysis a minimum 60 second length EEG segment is used.For coherency and measuring the asymmetry between hemispheres the whole 120 second long segment is used.

• Calculating the frequency-spectrum with FFT (with a frequency-resolution of 0.2-0.4 Hz)

• Calculating the quantitative parameters from the values of the FFT

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 116


www.itk.ppke.hu

NEUROMETRICS – CALCULATING THE PARAMETERS

1. Absolute power: Calculating the power density spectrum and integrating this.

2. Relative power: The absolute power of a given interval divided by the absolute power of the whole frequency range.

3. Hemispheric asymmetry: The quotients of absolute power values of the symmetrical recording electrode-pairs (right/left)

4. Coherency: The size of the consistency of phaseshifts between the recording sites.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 117


www.itk.ppke.hu

RECORDING TECHNIQUES USED IN NMMonopolar recording sites:

F1, F2, F3, F4, C3, C4, P3, P4, O1, O2, F7, F8, T3, T4, T5, T6, Fz, Cz, Pz

Combined monopolar features:Left side: F7, T3, T5 Right side: F8, T4, T6Left medial: F1, F3, C3, P3, O1 Right medial: F2, F4, C4, P4, O2Left frontal area: F1, F3, F7 Right frontal area: F2, F4, F8Left central area: C3, T3 Rigth central area: C4, T4Left posterior ares: P3, O1, T5 Right posterior area: P4, O2, T6Left hemisphere: F1, F3, C3, P3, Right hemisphere: F2, F4, C4, P4,

O1, F7, T3, T5 O2, F8, T4, T6Central area: Fz, Cz, Pz

Frontal area: F1, F2, F3, F4, F7, F8, FzMedial area: C3, C4, T3, T4, Cz

Posterior area: P3, P4, O1, O2, T5, T6, PzWhole head: all the 19 monopolar electrode recording sites

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 118


www.itk.ppke.hu

RECORDING TECHNIQUES USED IN NM

Bipolar recording setup:

Cz-C3, Cz-C4, T3-T5, T4-T6, O1-P3, O2-P4, T3-F7, T4-F8

Combined bipolar features:

Whole head: all the 8 bipolar recording electrode-pairsLeft hemisphere: Cz-C3, T3-T5, O1-P3, T3-F7

Right hemisphere: Cz-C4, T4-T6, O2-P4, T4-F8Frontal area: T3-T5, T4-T6, T3-F7, T4-F8

Posterior area: Cz-C3, Cz-C4, O1-P3, O2-P4

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 119


www.itk.ppke.hu

CONSTRUCTING THE DATABASE• EEG parameters Gaussian distribution• Regression analysis:

• Dependent variable: age• Independent variable: EEG parameters

• Normal averages Age dependent linear regression equations

• Different physical dimension Standardized(Z transformed) parameters

(Figure 56.)

(Figure 57.)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 120


www.itk.ppke.hu

Figure 56. Transform to Gaussian distribution

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 121


www.itk.ppke.hu

Z TRANSFORMqEEG – (normal) mean

(estimated) deviation

Properties of Z transformed values: Every parameter (of healthy people) has a zero expected value and a deviation with value 1.

The database has a relative characteristics!

Figure 57. Z transform of the EEG data.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 122


www.itk.ppke.hu

Figure 58. Multivariate probability estimation

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 123


www.itk.ppke.hu

NORMAL VALUESProcessing the EEG data of numerous healthy individuals: normal database

Who can not be considered as healthy?• Neurological or psychiatric disorder• Headtrauma• EEG deflection earlier• Taking medications within 3 weeks before the examination• Drinking alcohol or taking drugs• IQ not between the normal valuesDisplaying the results: Neurometrical matrixProbability brain maps

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 124


www.itk.ppke.hu

Figure 59. Example of a neurometric matrix from a healthy person. The numbersrepresent deviations. Deviations above 2 or below -2 can be considered pathological.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 125


www.itk.ppke.hu

Figure 60. Probabilitybrain maps of differentpatient groups (relativepower of frequnencybands)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 126


www.itk.ppke.hu

NM – DECREASING BRAIN ACTIVITY• First sign of the decreasing brain activity:

Theta is increasing (at Global deterioration score (GDS) 2, subjective complaints to forgetfulness: familiar names, places of objects, but no demonstrable sign)

• Next stage:Delta activity is increasing

• In advanced stage:Decrease of alpha and beta activity

• The correlation of GDS with coherence and the hemispheric asymmetry do not show any significant deflections.

• But there is a significant deflection in the case of both the absolute and relative powers (different brain regions, cognitive weaknesses)

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 127


www.itk.ppke.hu

Figure 61. Normal and patient groups in the multi-dimensional space.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 128


www.itk.ppke.hu

PERSPECTIVES OF NEUROMETRICS

• Optimal drug dosage

• Predicator functions: The reaction of the patient to different treatments

• Electrophysiological measurements (brain activity): where the EEG is inconsistent or not sensitive enough

• Solving challenging diagnostic tasks (for example the discrimination of the unipolar depression from old-age dementia)

• Same symptoms, with different physiological data: differentiation.

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 129


www.itk.ppke.hu

SOFTWARES FOR SIGNAL ANALYSISEEG• Compumedics – Neuroscan• Applied Neuroscience – Neuroguide• EGI – Net Station• Brain Products – Brain Vision Analyzer

Spike sorting• Plexon - Offline Sorter• CED - Spike 2• Alphaomega – Alpha-Sort• OpenEx – OpenSorter

Source localization• Compumedics – Curry• EGI – GeoSource• Applied Neuroscience - Neuroguide

• BESA Research• EEGLAB – Matlab Toolbox• PhiTools – PRANA Software Suite• …

• Axona – Tint• Klusters• Matlab Tools: OSort, Wave_clus

FIND, NeuroMAX, Mclust …

• EMSE Suite• Brain Innovation – BrainVoyager QX• …

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 130


www.itk.ppke.hu

RECOMMENDED LITERATURE• Signal Processing for Neuroscientists: An Introduction to the Analysis of

Physiological Signals; Wim van Drongelen; 2007, Academic Press • EEG Signal Processing ; Saeid Sanei, J. A. Chambers; 2007;Wiley• Electroencephalograpy: Basic Principles, Clinical Applications, and Related

Fields; E. Niedermeyer, F. Lopes da Silva;2004; Lippincott Williams & Wilkins• Functional Neuroscience vol 2 Neurometrics: Clinical Applications of

Quantitative Electrophysiology; E.R.John, John Wiley & Sons, 1977• More than 1000 books related to signal analysis on www.amazon.com

http://www.amazon.com/s/ref=nb_sb_noss?url=search-alias=aps&field-keywords=electrophysiology+analysis&x=0&y=0

2011.10.07.. TÁMOP – 4.1.2-08/2/A/KMR-2009-0006 131


www.itk.ppke.hu

REVIEW QUESTIONS1. What type of bioelectric signals do you know?2. What are artefacts? Can you mention some examples? 3. What is the signal-to-noise ratio? How can we measure it?4. What are the main filter classes?5. Why is signal averaging useful?6. In which domain is the correlation used? And the coherence?7. What are the steps of the spike sporting algorithm?8. What are the difficulties related to spike sorting?9. Where is current source density analysis used? What is it good for?10. What is brain mapping?11. What is the procedure for processing event-related potentials?12. What were the two mentioned nonlinear methods? In what application

fields are they useful?13. What is the aim of Neurometrics?

peter pazmany catholic university file2011.10.07.. tÁmop – 4.1.2-08/2/a/kmr-2009-0006 . 1....

Documents