adaptive brain interfaces

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ABSTRACT

Adaptive Brain Interfaces (ABI) is a part of European Union

Information Technologys ESPRIT program, with the central aim of

extending the capabilities of physically-impaired people to accessnew services and opportunities. The ABI is a portable brain-

computer interface based on the analysis of

electroencephalogram (EEG) signals and interface of P300 based

speller.

A cap with a few integrated electrodes acquires brain signals

that are pre-processed and sent to a computer for further

analysis. The portable brain interface has an embedded neural

network classifier that recognizes what mental task the wearer isconcentrating on. It does so by analyzing continuous variations of

EEG signals over several cortical areas of the brain. Each mental

task is associated to a simple command. This enables people to

communicate using their brain activity, as the interface only

requires users to be conscious of their thoughts and to

concentrate sufficiently on the mental expression of the

commands required to carry out the desired task. So, by

composing command sequences (thoughts), the user can read aweb page, interact with games, turn on appliances, or even guide

a wheelchair.

Brain interface will be most successful when it is adapted to

its owner. The approach is based on a mutual learning process

where the user and the ABI interface are coupled together and

adapt to each other. The neural network has been specifically

designed to cope with the challenging problem of recognizing

mental tasks from spontaneous on-line EEG signals. Although the

immediate application of ABI is to help physically disabled or

impaired people by increasing their independence and facilitating

access to the Information Society, the benefits of such a system

are extensive. Anyone can use it for other purposes, e.g. health

and safety concerns (e.g. monitoring a person's level of


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alertness). ABI could also contribute to the medical diagnosis of

brain disorders.

This report presents a subject-independentEEG(Electroencephalogram) classification technique and its

application to a P300 based word speller. It also presents use ofsignals recorded from the brain to operate robotic or prostheticdevices. Both invasive and noninvasive approaches have proveneffective. Achieving the speed, accuracy, and reliability necessaryfor real world applications.

INTRODUCTION

Adaptive Brain Interface (ABI) is a human computer interfacesystem that accepts voluntary commands directly from the brainto interact with the surrounding environment or to do a particulartask. Sometimes it is called a direct neural interface, Braincomputer interface or a brain-machine interface. I t is adirect communication pathway between a brain and an externaldevice. BCIs were aimed at assisting, augmenting or repairinghuman cognitive or sensory-motor functions. The approach, onwhich the ABI is based, as the name implies, is the adaptiveness.

That means that both the system and the user adapt to eachother as explained before. In ABI the adaptive part is the localneural classifier which is responsible for classifying input signal,and the user adapts by training in the chosen mental tasks whichhe/she finds most comfortable and effective to use. Secondimportant approach is that this system should also work reliablyoutside laboratory environment, i.e. in normal everyday life. Thiscalls for an easy to use, wearable (small and light) system.Whencompared with other BCIs, one of the ABIs areas of good

performance is the time required for training. User can acquiregood control over the system just in five days.Each mental task is associated to a simple command such as

"select right item". This enables people to communicate usingtheir brain activity, as the interface only requires users to beconscious of their thoughts and to concentrate sufficiently on themental expression of the commands required to carry out the


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desired task. So, by composing command sequences (thoughts),the user can read a web page, interact with games, turn onappliances, or even guide a wheelchair. For example, theinterface can be used to select letters from a virtual keyboard on

a computer screen and write a message. The ABI project seeks todevelop individual brain interfaces. The same system is notsuitable for everybody, as no two people are identical, eitherphysically or psychologically. This means that an interface will bemost successful when it is adapted to its owner. The approach isbased on a mutual learning process where the user and the ABIinterface are coupled together and adapt to each other.

RESEARCH HISTORY WITH FACTS

1990:

Monkeys in North Carolina have remotely operated a roboticarm 600 miles away in MIT's Touch Lab using their brain signals.

The feat is based on a neural-recording system. In that system,tiny electrodes implanted in the animals' brains detected theirbrain signals as they controlled a robot arm to reach for a piece offood.

According to the scientists from Duke University Medical

Center, MIT and the State University of New York (SUNY) HealthScience Center, the new system could form the basis for a brain-machine interface that would allow paralyzed patients to controlthe movement of prosthetic limbs. The Internet experiment "wasa historic moment, the start of something totally new,"Mandayam Srinivasan, director of MIT's Touch Lab, said in aNovember 15 story in the Wall Street Journal.The work also


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supports new thinking about how the brain encodes information,by spreading it across large populations of neurons and by rapidlyadapting to new circumstances.

In the Nature paper, the scientists described how they testedtheir system on two owl monkeys, implanting arrays of as manyas 96 electrodes, each less than the diameter of a human hair,into the monkeys' brains. The technique they used allows largenumbers of single neurons to be recorded separately, thencombines their information using a computer coding algorithm.

The scientists implanted the electrodes in multiple regions of thebrain's cortex, including the motor cortex from which movementis controlled. They then recorded the output of these electrodesas the animals learned reaching tasks, including reaching for

small pieces of food.2000:

Two monkeys have been trained toeat morsels of food using a robotic arm

A monkey controls a robotic arm using brain signals to pluck a marshmallow from a

skewer and put it into his mouth during an experiment at the University of

Pittsburgh


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controlled by thoughts that are relayedthrough a set of electrodes connecting theanimal's brain to a computer, scientistshave announced. The astonishing feat is

being seen as a major breakthrough in thedevelopment of robotic prosthetic limbsand other automated devices that can bemanipulated by paralysed patients usingmind control alone.

"The monkey learns by first observing the movement, whichactivates the brain cells as if he were doing it. It's a lot like sportstraining, where trainers have athletes first imagine that they areperforming the movements they desire," Dr Schwartz said. The

robotic arm used in the experiment had five degrees of freedom three at the shoulder, one at the elbow and one at the hand,which was supposed to emulate the movement of the humanarm.

The training of the monkeys took several days using food asrewards. Previous work by the group has concentrated on trainingmonkeys to move cursors of a Computer screen but the lateststudy using a robotic arm involved a more complicated system ofmovements, the scientists said.

After the above mentioned discovery P300 samples havebeen invented. P300 is an endogenous, positive polaritycomponent of the evoke-related-potential (ERP) developed in thebrain in response to infrequent/oddball auditory, visual or somato-sensory stimuli in a stream of frequent stimuli. Farwell andDonchin first demonstrated the use of P300 for brain computerinterfaces (BCIs) . In the paradigm, the computer displays amatrix of cells representing different letters, and flashes each rowand column alternately. A user trying to input a letter needs topay attention to the letter for a short while. In this process, whenthe row/column containing the intended letter flashes, a P300 willbe elicited in EEG, which can then be detected for word spellingby an appropriate algorithm. It is recognized that large inter-subject variations exist among people. For example, theamplitude and latency exists in both normals as well as clinicalpopulations. And this has been linked to individual differences in


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cognitive capability. Therefore, from the pattern recognitionviewpoint, computational P300 models build for one person wouldnot apply well to another person. To solve this problem existingP300-based BCIs all use a direct method to solve this problem by

training subject-specific P300 models. Thus, before a person canoperate the BCIs, he/she needs to go through a special trainingprocess. In that process, the person usually follows instructions tostare at a particular cell at a given time, while his concurrent EEGis recorded. With the recorded data, a computer algorithmperforms signal analysis and learns the subject-specific P300model. However, this process is normally tedious andcomplicated.

2004:

Brain interfacing has been tried on humans in 2004.Matt

Nagle was the first person to get this technology implemented. Hehas been paralysed and he used this technology to move arobotic arm to do different tasks.

WORKING OF ADAPTIVE BRAIN INTERFACE

Electrodes placed on the scalp or within the head acquiresignals from the brain, and the BCI system processes them toextract specific signal features that reflect the users intent. TheBCI translates these features into commands that operate adevicefor example, a word-processing program, speechsynthesizer, robotic arm, or wheelchair.


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Basic design of a brain interface system

It works because of the way our brains function. Our brains arefilled with neurons, individual nerve cells connected to oneanother by dendrites and axons. Every time we think, move, feelor remember something, our neurons are at work. That work iscarried out by small electric signals that zip from neuron toneuron as fast as 250 mph [source: Walker]. The signals are

generated by differences in electric potential carried by ions onthe membrane of each neuron.Although the paths the signals take are insulated by

something called myelin, some of the electric signal escapes.Scientists can detect those signals with the help of tools likeelectrodes in EEG, interpret what they mean and use them todirect a device of some kind. The electrodes measure minutedifferences in the voltage between neurons. The signal is thenamplified and filtered. In current BCI systems, it is theninterpreted by a computer program.


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STRUCTURE OF BRAIN

The brain is part of the central nervous system, whichconsists oflarge brain, little brain, brainstem and spinal cord.

Brainstem connects the brain to the spinal cord. If a fullydeveloped brain is viewed at the bottom, the first part to be seenis the myelencephalon, which means the spinal brain. Above themyelencephalon is the metencephalon, which means acrossbrain and consists of Cerebellum and fourth ventricle. There arefour lobes of the cortical surface, the frontal, temporal,parietaland occipital each having two halves over the longitudial fissure.

Structure of Brain

The frontal lobe controls complex cognitive function such asattention and language programmes and executes motorpatterns. Damage to the frontal lobe can lead to degenerativediseases, such as Parkinsons or Alzheimers disease. In thetemporal lobe the main functions are the auditory functions andother functions that are performed here include storage of visualmemory, processing of spoken and heard language. Medial in thetemporal lobe is a structure called hippocampus, which is relatedto memory, particularly to the consolidation of memory. Much ofthe past years research in learning and memory is concentratedto hippocampus and its surrounding areas. The parietal cortex isthe first of all involved in sensing touch and kinesthetics . It is alsoinvolved in integrating information from different senses, e.g.


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when we see and hear a car, parietal cortex in involved in forminga unitary phenomenon, a car. The occipital lobe contains themajor visual area of the brain. This is the place where the visualinformation is directed from the eye. It is also involved in other

visual functions, such as recognition.

CLASSIFICATION OF BRAIN INTERFACE TECHNIQUES

Brain interface technique can be broadly classified into 2categories:

1. Invasive2. Non-Invasive

Current noninvasive BCIs derive the users intent from scalp-recorded electroencephalographic (EEG) activity. They canprovide basic communication and control to people with severedisabilities. Current invasive BCIs derive the users intent fromneuronal action potentials or local field potentials recorded fromwithin the cerebral cortex or from its surface. Researchers havestudiedthese systems mainly in nonhuman primates and to a limited

extent in humans. Invasive BCIs face substantial technicaldifficulties and involve clinical risks. Surgeons must implant therecording electrodes in or on the cortex. The devicesmust function well for long periods, and they risk infection andother damage to the brain. The drive to develop invasive BCImethods is based in part on the widespread conviction that onlyinvasive BCIs will be able to provide users with real-timemultidimensional sequential control of a robotic arm.

A noninvasive BCI using scalp-recorded EEG activity (that is,sensori motor rhythms) can provide humans withmultidimensionalmovement control. More recent studies showedthat a noninvasive EEG-based BCI that incorporates an adaptivealgorithm and other technical improvements can give humans 2Dmovement control and sequential control that achieve movementtime, precision, and accuracy comparable to that achieved byinvasive BCIs in monkeys or humans. Most recently, researchers


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showed that an EEG-based BCI can provide 3D movement controlcomparable to that achieved by invasive methods.

Studies using both invasive and noninvasive BCIrecording of brain activity have successfully demonstratedthe feasibility of controlling robotic devices.However, for the most part, these studies have only

demonstratedthe potential use of BCI technology for roboticand prosthetic applications. The practical application ofinvasive approaches will require solutions to long-termrecording stability and safety problems. Noninvasivemethods require a recording methodology that can meet

ElectroencephalographyDemonstration of Electrocortico-graphy(EcoG)


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the users everyday needs when supported by caretakerswho arent skilled technicians. Both methodologies willneed improved speed, accuracy, and reliability to providetruly useful robotic or prosthetic control.

To date, most groups using invasive brain-computermethods to control movements have used direct kinematiccontrol. This is probably because of the basicscienceresearch tradition in these laboratories. Theyvetraditionally focused on understanding the neural basisof movement control by studying neuronal correlates ofkinematic parameters. Noninvasive studies have beenmore mixed. While some have developed kinematic control,others have pursued a goal-selection approach.Robotic and prosthetic applications in themselves

dont appear to represent a unique challenge to BCItechnology. Investigators using both invasive and noninvasivemethods achieved a smooth transition fromparadigms that control cursor movements to those thatcontrol actual mechanical devices. The major problemfor BCI applications is providing fast, accurate, and reliablecontrol signals. Certainly, developments in roboticswill be useful for systems used by actual patients, butthese developments wont by themselves solve the problems that

are unique to BCI development.The practical use of kinematic control will likelyrequire more independent control signals than are currentlyavailable from BCI technology. For example,robotic arms often have seven or more degrees of freedom,and the human hand and arm have even more. Aswe noted earlier, invasive methods have achieved threedimensions of movement control plus grasp control,3and noninvasive methods have achieved two dimensionsof movement control plus selection (that is, grasp) control12and three dimensions of movement control.16 Atthe same time, the goal-selection strategy (that is, reversekinematic control) is certainly applicable to roboticapplications with current BCI methodology and mightprovide a good alternative for controlling complex and

sequential movements.


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On the Generation of the EEG signalIn this section some basic principles aboutElectroencepephalogaphy or EEG for short and its measurementare described. As the neurons in our brain communicate with

each other by firing electrical impulses, this creates an electricfield which travel though the cortex, the dura, the skull and thescalp (see section 1.1.1). The EEG is measured from the surfaceof the scalp by measuring potential difference between the actualmeasuring electrode and a reference electrore. First, thegeneration of the electric potential is described, then the actualmeasurement techiques and issues influencing it.1.2.1 Communication between the brain cells (neurons)

The information between the braincells is relayed from dendrites,which is the input channels to cell, to axon, which is the output

channel. The cell wall is permeable for sodium and potassiumions, and this permeability is function of the electric potential ofthe cell wall. These ion currents make possible that anunattenuated electric field, action potential, can propagatethrough the cells. In order to relay information, the ionconsentrations outside and inside the cell must be different: onthe outside should be lots of potassium and inside the cell shouldbe lots of sodium ion. The amount of these ions in the cell iscontrolled by the Na-K pumps which 8 pump the unnecessary ions

out or in correspondingly. Pumping is done to upstream, i.e.from lower consentration to higher concentration.

The potential on the inside of the cell is about 70mV smaller thanthe potential on the outside. When the potential is decreasing, itis called depolarization and when increasing, hyperpolarization.When depolarization is big enough, a certain threshold is reached,and more sodium ions flood to cell depolarizing it further. Thenthe cell will discharge and send an action potential, which floodspotassium ions into the cell and turns its electric charge topositive. As the action potential propagates through the cells, itsamplitude remains constant. This phenomenon travels from cellto cell like is the domino-effect.No energy or material propagates in this process, just theinformation. If the depolarization does not reach the threshold, itwill be only a local change in potential. The action potential canpropagete as fast as 100m=s. The information processing in a


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network of braincells is based on connections between thebraincells. The actual connection is made by synapses, which arelocated at surfaces of the dendrites and axons. When comparingto the artificial neural networks used in statistical modelling,

synapses are bit similar to weights of a neuron. The rate in whichabraincell relays information depends on weighted sum of theinput signals. The weight of a synapse depends its distance fromsoma, which is the center of a cell. The cells affect each othereither excitatory i.e. increasing the action, or inhibitory i.e.inhibiting the action between the cells. On the surface of a cellthere is usually large amount synapses of both types. When signalarrives to a synapse, it releases a chemical transmitter substanceto a gap in the synapse, from where the transmitter diffuses to

the cell wall of the postsynaptic cell. In an excitatory synapse thetransmitter causes to positive ions to flood into the postsynapticcell to depolarize the cell wall. In an inhibitory synapse, thetransmitter tries to keep the potential of the cell wall below thethreshold.A neural network operates by individual cells as they dischargeaction potentials corresponding to synaptic information theyreceive. A cell sums all the inputs that it gets and if the differencebetween the excitatory and inhibitory is big enough, i.e. the

threshold is reached, it sends its own signal forward. The inputsignal can also change neurons growth, metabolism and theweight of a synaptic connections.

The fundamental assumption behind the EEG signal is that itreflects the dynamics of electrical activity in populations ofneurons. The crucial property of such populations is that they canwork in synchrony. In order to be able to work in synchrony,connections between the neurons must be formed to build up anetwork of neurons. The terminology 9 to describe such networkshas been developed by Freeman [Fre75]. A basic unit of such anetwork is referred as KI sets, which are populations of neuronswith mutual inhibitory (KIi) or excitatory (KIe) interaction. Aninteracting pair ofKIi and KIe set form KII set, and further KIII setis formed by two interacting KII sets. A group ofKIII sets usuallyoccupy an area of a few square millimeters in the cortical surfaceor a nuclear volume of a few cubic millimeters in the brainstem


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and a spinal cord. A neural mass usually consists between 104and 107 neurons.

These are the basic sets that generate the oscillatory phenomenameasured as EEG signal. The potential fluctuation that we

measure as EEG signal is generated by sum of the postsynapticpotentials in the cortex. Since a single postsynaptic potentialgenerates a potential of magnitude 1014, thousands of thesesynapses must activate synchronously in order to be detectablewith EEG. The EEG is supposed to be generated by oscillationsbetween the cortex and the thalamus.

These oscillations seems to be generated by physical propertiesof a neuron and by functions of the ionic channels in the cell wallsof the thalamic cells. Research seems to point out that functionalstates in the cortex such as (sleep, infomation processing or

relaxation are caused by changes in thalamic activity.The process which generates the EEG is very complex due to thelarge amount of the independent neurons. Therefore the researchfor the models of the EEG signal generation is a kind of dialogueof theory and experimentation, which can be descibed as two partprocess. First the theory makes assumptions about the processwhich is then tested by experimenting, i.e. testing different inputsand studying their outputs or changing some of the properties ofthe constituting elements. The hypotheses are formulated

concerning new elementary properties, relatoinships and overallbehaviour. These new hypotheses then predict new results, raisenew questions and suggest new experiments to validate givenhypotheses.

This dialogue has so far produced several theories and modelsabout EEG signal generation and in the last years it seems to bethat the following models have raised the most interest [Nie99a]

_The model describing the generation of the EEG of olfactory areasof the brain, proposed by Freeman in 1975

_The model of the alpha rhythm in the thalamus and cortex,proposed by Lopes da Silva et al. in 1974 10

_The series of models of the membrane and synaptic properties ofthalamic cells and circuits responsible of the generation of 7 - 14


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Hz spindle rhythmicity that occurs in the light stages of sleep,based on the experimantal findings of Steriade et al. in 1993 _

The model of the generation epilecticform transients proposed byTraub in 1982 _

The model of gamma rhythms develobed by Traub et al. in 1996and realistic simulations of synapses in 19961.2.2 Frequency bands of the EEGSome of the frequency bands found from the EEG are shown intable (1.2.2). Most of the brain research is consentrated in thesechannels and especially _ and _ bands are important for BCIresearch. The reason why the bands do not follow the greek letter

TypeFrequency (Hz)

Location Normally Pathologically

Delta up to 3

frontally inadults,posteriorlyinchildren;highamplitudewaves

adults slow wavesleep

in babies

subcorticallesions

diffuse lesions metabolic

encephalopathydrocephalu

deep midlinelesions.

Theta 4 - 7 Hz

young children drowsiness or

arousal in olderchildren andadults

idling

focal subcortic

lesions metabolic

encephalopat deep midline

disorders

some instanceof hydrocepha
http://en.wikipedia.org/wiki/Delta_wavehttp://en.wikipedia.org/wiki/NREMhttp://en.wikipedia.org/wiki/NREMhttp://en.wikipedia.org/wiki/Theta_wavehttp://en.wikipedia.org/wiki/NREMhttp://en.wikipedia.org/wiki/NREMhttp://en.wikipedia.org/wiki/Theta_wavehttp://en.wikipedia.org/wiki/Delta_wave


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Alpha 8 - 12 Hz

posteriorregions ofhead, bothsides,

higher inamplitudeondominantside.Centralsites (c3-c4) atrest .

relaxed/reflecting

closing the eyes coma

Beta12 - 30Hz

both sides,symmetricaldistribution, mostevidentfrontally;lowamplitudewaves

alert/working

active, busy oranxious thinking,activeconcentration

benzodiazepin

Gamma

34 100+

certain cognitiveor motorfunctions

Table:Comparison of different EEG signalsThe _ -rhythm is included to this list even though it is not actuallya band but an important rhythm in BCI research centeredbetween 9 - 11Hz magnitudely (alpha is not the lowest band) isthat this is the order in which they were discovered.

The EEG contains quite a wide spectrum of frequencies but it isnot just an even mixture of things. EEG has organization andrhythmicity but only to certain level. Too much rhythmicity mayindicate abnormality but chaotic and seemingly noisy signal maynot.

The overall bandwidth of the EEG is about 0.1 Hz - 100 Hz but thepractical limit is 0.3 and 70 Hz.
http://en.wikipedia.org/wiki/Alpha_waveshttp://en.wikipedia.org/wiki/Beta_wavehttp://en.wikipedia.org/wiki/Benzodiazepineshttp://en.wikipedia.org/wiki/Gamma_wavehttp://en.wikipedia.org/wiki/Gamma_wavehttp://en.wikipedia.org/wiki/Alpha_waveshttp://en.wikipedia.org/wiki/Beta_wavehttp://en.wikipedia.org/wiki/Benzodiazepineshttp://en.wikipedia.org/wiki/Gamma_wavehttp://en.wikipedia.org/wiki/Gamma_wave


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stronger in the right side. This blocking appears before actualmovement of the muscles, therefore it seems to related toconceptual planning of the movement. The _-rhythm is alsoblocked by light tactile i.e. touching the skin lightly, so some

researchers consider the _ -rhythm to be the idling state of thesensory cortex and its vincinity.12Beta Rhythm

_ -rhythm band consists of basicly all frequencies above 13 Hz butpractically it is limited to 50 Hz by the measurement limits and to30 Hz by functional findings. Spatially _ -rhythm can be foundfrontal and central regions. The central _ -rhythm is related toRolandic _ -rhythm (see (1.2.2)) and can be blocked by motoractivity and tactile simulation (planning to move)._ -rhythm rarely

exceed amplitudes of 30 _V and as a rule of the thumb whenfrequency increases the amplitude decreases and vice versa. _-rhythm is usually associated with increaced arousal and activity.Theta RhythmA band originally a part of the delta band is of frequencies from 4to 7 Hz. _ -rhythm has gotten its name from the presumed origin,the thalamus. EEG of a normal adult consists little _ -frequenciesand no organized _ -rhythm. However, the _- frequencies and _ -rhythm play important part during the childhood and in states of

drowsiness and sleep. _ -rhythm is associated with marking thematurity of the mechanism linking the cortex, the thalamus andthe hypothalmus. Also it is linked with feelings of disappointmentand frustration. For some people the _ -rhythm is present whenperforming mental tasks e.g. problem solving or visualization.Delta Rhythm

This rhythm is detected when the subject is in deep sleep at latersleep periods. _ -rhythm has a relatively high amplitude and lowfrequency, 3 Hz or less. _ -rhythm decreases with age and can bea sign of brain abnormality if detected in the awake state.1.2.3 Evoked Responses from the UserIn order to communicate via brain activity, the user must be ableto control the EEG signal. These types brain activities can bedivided to two groups: Evoked responses, which are evokedresponses by a sensory stimulus, such as flashing light, andspontaneous EEG signals which occur without stimulus, such as _


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or mu -rhythm, which the user can learn to control with thebiofeedback(see section 1.3.3).13

The evoked responses can be further divided to three main

classes:_Evoked potentials (EP) require specific sensory stimulus. Anexample of EP is visual evoked potential (VEP). If stimulus is givenis a form of a flashing light, the EEG over the visual cortex willhave the same frequency as the flashing light. A user can betrained to control the amplitude of the EEG over the visual cortexby biofeedback. VEP is very easy to detect, making the patternrecognition easy. But the training time required to improve thecontrol is long

Event-Related potentials (ERP) are DC changes to a discreteevent. The ERP is a response to a stimulus or an event and iteither coincides or follows it after a short delay. But the ERP canalso be detected in the absence of the stimulus if the actualstimulus is anticipated to happen or they may precede voluntarymotor responses, i.e. moving a hand without being directed to doso. ERPs are believed to be generated by the brain throughextracellular potentials associated with the activity of groups ofneurons firing in synchrony.

Examples of ERPs are P3 or P300 ERP, which occurs 300 ms aftera specific stimulus that to subject is told response. The stimulushas to be ofbernouilli type i.e. more rarely occuring alternativefrom the two alternatives.Another example is slow cortical potentials (SCP), which are alarge increase of surface-negative cortical DC potential, causedby cognitive processing in the brain lasting more than a second ortwo. When compared to the shorter latency ERP such as P300, theSCPs reflect more global task-related processes. In figure (1.5) isshown SCP developement for linquistic and mental visualisationtasks. Notice how linquistic task activates the frontal lobes(electrodes F3, F7) and the mental visualisation the posteriorparietal lobes (electrodes P3, P4)

_Event-Related Synchronization (ERS) and Event-RelatedDeSynchronization (ERD) are the AC changes to a discrete


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event. More accurately ERD/ERS is blocking of the _ -rhythms dueto sensory processing or blocking of the _ -rhythm due to motorbehavior. ERD/ERS occurs during the cortical informationprocessing due to the increased cellular excitability in

thalamocortical systems. The difference between ERD and ERS isthat a power decrease in the _ -band responds to ERD andincrease to ERS.ERD/ERS starts few seconds before the actual movement and itlasts few seconds and in order to restore the power levels to thereference level, the period between two stimulus shouldrandomized and no less than two seconds.14Figure 1.5: SCP developement for linquistic and mentalvisualisation tasks. [Nie99c] The depth of ERD is affected by the

complexity of the task or depth of the attention to the task.1.2.4 Measuring the EEG signal

The EEG stands for Electroencepephalogaphy and is measuringthe difference in electrical potential between various places onthe surface of the scalp. In an EEG measurement a potentialdifference between two electrodes is measured. Another one ofthe electrodes may be passive in a sense that it is not used tomeasure the brain activity, but the background electric field ofthe skin. These electrodes are called references and they are

attached to ear lobes or mastoids. The placement of thereferences is delicate, since a reference too close to the brain iscorrupted by brain activity and a reference on other parts of thebody may be corrupted by muscles (especially hearts) electricalactivity. The signals picked up by electrodes may be combined tochannels or a channel corresponds to a single electrode. Thesignal is then amplified and filtered from artifacts and displayedon computer screen.15Electrode Configurations

The first EEG measurements were done by Berger in 1929[Ber29]. In 1934 was found that EEG activity varied in differentlocatations of the head, which lead to several different electrodeplacement configurations in order to achive the best signal forcurrent experiment. These different electrode configurations ofcourse made the comparison of the results difficult, and therefore


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in 1958 a common standard, the 10-20 electrode configurationwas agreed to be the basic configuration. [Nie99d]. The 10-20system is shown in figure (1.2.4, left). In this system specific bodylandmarks are used to define electrode Figure 1.6: On the left: the

standard 10-20 electrode configuration. On the right: the 10-20system augmented to 75 electrode configuration. [Nie99d]placement, instead of using constant distance between theelectrodes. This body landmark based system is easy to replicatein different laboratories and when measuring the EEGof a child, the distances between electrodes grow as the childgrows, but the placement remains consistent.In 10-20 system the electrodes are coded by letter that indicatesthe anatomic area, and the numbering, in which the odd digits arefor the left hemisphere and the even for the right. An exception

are the midline electrodes, where the digit is changed to letter z.As can be seen from figure (1.2.4, left), space have been leftbetween the electrodes for additional electrodes if needed, e.g.electrode F5 can be placed between electrodes F3 and F7. Anexample of the modified 10-20 system is shown in figure (1.2.4,right), which is a system of 75 electrodes. The number of neededelectrodes depend of the type and location of brain activity andthe number of channels available.

On the Generation of the EEG signalIn this section some basic principles aboutElectroencepephalogaphyor EEG for short andits measurement are described. As the neurons in our braincommunicate with each otherby firing electrical impulses, this creates an electric field whichtravel though the cortex,the dura, the skull and the scalp (see section 1.1.1). The EEG ismeasured from the surfaceof the scalp by measuring potential difference between the actualmeasuring electrode anda reference electrore. First, the generation of the electricpotential is described, then theactual measurement techiques and issues influencing it.Communication between the brain cells (neurons)


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The information between the braincells is relayed from dendrites,which is the input channelsto cell, to axon, which is the output channel. The cell wall ispermeable for sodium

and potassium ions, and this permeability is function of theelectric potential of the cellwall. These ion currents make possible that an unattenuatedelectric field, action potential,can propagate through the cells.In order to relay information, the ion consentrations outside andinside the cell must bedifferent: on the outside should be lots of potassium and insidethe cell should be lots ofsodium ion. The amount of these ions in the cell is controlled by

the Na-K pumps which8pump the unnecessary ions out or in correspondingly. Pumping isdone to upstream, i.e.from lower consentration to higher concentration.

The potential on the inside of the cell is about 70mV smaller thanthe potential on theoutside. When the potential is decreasing, it is calleddepolarization and when increasing,

hyperpolarization. When depolarization is big enough, a certainthreshold is reached,and more sodium ions flood to cell depolarizing it further. Thenthe cell will dischargeand send an action potential, which floods potassium ions into thecell and turns its electriccharge to positive. As the action potential propagates through thecells, its amplituderemains constant. This phenomenon travels from cell to cell like isthe domino-effect.No energy or material propagates in this process, just theinformation. If the depolarizationdoes not reach the threshold, it will be only a local change inpotential. The actionpotential can propagete as fast as 100m=s.


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The information processing in a network of braincells is based onconnections betweenthe braincells. The actual connection is made by synapses, whichare located at surfaces

of the dendrites and axons. When comparing to the artificialneural networks used instatistical modelling, synapses are bit similar to weights of aneuron. The rate in which abraincell relays information depends on weighted sum of theinput signals. The weight ofa synapse depends its distance from soma, which is the center ofa cell.

The cells affect each other either excitatoryi.e. increasing theaction, or inhibitoryi.e.

inhibiting the action between the cells. On the surface of a cellthere is usually largeamount synapses of both types. When signal arrives to a synapse,it releases a chemicaltransmitter substance to a gap in the synapse, from where thetransmitter diffuses to thecell wall of the postsynaptic cell. In an excitatory synapse thetransmitter causes to positiveions to flood into the postsynaptic cell to depolarize the cell wall.

In an inhibitory synapse,the transmitter tries to keep the potential of the cell wall belowthe threshold.A neural network operates by individual cells as they dischargeaction potentials correspondingto synaptic information they receive. A cell sums all the inputsthat it gets andif the difference between the excitatory and inhibitory is bigenough, i.e. the threshold isreached, it sends its own signal forward. The input signal can alsochange neurons growth,metabolism and the weight of a synaptic connections.

The fundamental assumption behind the EEG signal is that itreflects the dynamics ofelectrical activity in populations of neurons. The crucial propertyof such populations is


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that they can work in synchrony. In order to be able to work insynchrony, connectionsbetween the neurons must be formed to build up a network ofneurons. The terminology

9to describe such networks has been developed by Freeman[Fre75]. A basic unit of sucha network is referred as KI sets, which are populations of neuronswith mutual inhibitory(KIi) or excitatory (KIe) interaction. An interacting pair ofKIi andKIe set form KII set,and further KIII set is formed by two interacting KII sets. A groupofKIII sets usuallyoccupy an area of a few square millimeters in the cortical surface

or a nuclear volume ofa few cubic millimeters in the brainstem and a spinal cord. Aneural mass usually consistsbetween 104 and 107 neurons.

These are the basic sets that generate the oscillatory phenomenameasured as EEG signal.

The potential fluctuation that we measure as EEG signal isgenerated by sum of thepostsynaptic potentials in the cortex. Since a single postsynaptic

potential generates a potentialof magnitude 1014V , thousands of these synapses must activate synchronously inorder to be detectable with EEG.

The EEG is supposed to be generated by oscillations between thecortex and the thalamus.

These oscillations seems to be generated by physical propertiesof a neuron and by functionsof the ionic channels in the cell walls of the thalamic cells.Research seems to pointout that functional states in the cortex such as (sleep, infomation

processing or relaxationare caused by changes in thalamic activity.

The process which generates the EEG is very complex due to thelarge amount of the


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independent neurons. Therefore the research for the models ofthe EEG signal generationis a kind of dialogue of theory and experimentation, which can bedescibed as two part

process. First the theory makes assumptions about the processwhich is then tested byexperimenting, i.e. testing different inputs and studying theiroutputs or changing someof the properties of the constituting elements. The hypotheses areformulated concerningnew elementary properties, relatoinships and overall behaviour.

These new hypothesesthen predict new results, raise new questions and suggest newexperiments to validate

given hypotheses.This dialogue has so far produced several theories and modelsabout EEG signal generationand in the last years it seems to be that the following modelshave raised the mostinterest [Nie99a]

_The model describing the generation of the EEG of olfactory areasof the brain,

proposed by Freeman in 1975_The model of the alpha rhythm in the thalamus and cortex,proposed by Lopes daSilva et al. in 197410

_The series of models of the membrane and synaptic properties ofthalamic cells andcircuits responsible of the generation of 7 - 14 Hz spindlerhythmicity that occursin the light stages of sleep, based on the experimantal findings ofSteriade et al. in1993

_


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The model of the generation epilecticform transients proposed byTraub in 1982_The model of gamma rhythms develobed by Traub et al. in 1996

and realistic simulationsof synapses in 19961.2.2 Frequency bands of the EEGSome of the frequency bands found from the EEG are shown intable (1.2.2). Most ofthe brain research is consentrated in these channels andespecially _ and _ bands areimportant for BCI research. The reason why the bands do notfollow the greek letterBand Frequency [Hz] Amplitude [_V ] Location

Alpha (_) 8 - 12 10 - 150 Occipital/Parietal regions_-rhythm 9-11 varies Precentral/Postcentral regionsBeta (_) 14 - 30 25 typically frontal regionsTheta (_) 4 - 7 varies variesDelta (_) < 3 varies varies

Table 1.1: The most important frequency bands and _ -rhythm ofbrain activity and theirdetails. The _ -rhythm is included to this list even though it is notactually a band but an

important rhythm in BCI research centered between 9 - 11Hzmagnitudely (alpha is not the lowest band) is that this is the orderin which they werediscovered.

The EEG contains quite a wide spectrum of frequencies but it isnot just an even mixtureof things. EEG has organization and rhythmicity but only tocertain level. Too muchrhythmicity may indicate abnormality but chaotic and seeminglynoisy signal may not.

The overall bandwidth of the EEG is about 0.1 Hz - 100 Hz but thepractical limit is 0.3and 70 Hz.

The EEG amplitude is measured peak to peak and its accuratedetermination is difficult.


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Therefore EEG amplitudes are usually noted as certain differenceor even verbaly, e.g.medium voltage or low to medium voltage.11

Alpha RhythmThe official definition of the _ -rhythm is: Rhythm at 8-13 Hzoccuring during wakefulnessover the posterior regions of the head, generally with highervoltage over occipital areas.

Amplitude is variable but mostly below 50_V in adults. Best seenwith (the patients) eyesclosed and under physical relaxation and relative mentalinactivity. Blocked or attenuatedby attention, especially visual and mental effort. (IFSECN, 1974)

The mean _ -rhythm of adult male is 10.2_0.9 Hz and it decreased as person ages, mostprobably due to the degeneration of cerebral. _ -Rhythm ischaracterized by sinusoidalwave. Spatially the _ -rhythm is a manifestation the posterior halfof the head and foundover occipital, parietal and posterior lobes.

_ -Rhythm is temporarily blocked by an eye opening (influx of

light) and mental activities,which are usually less effective than eye opening. It has beenproposed that _ -rhythm isa kind of a standing by-state of the brain, ready for switching toother activity bandsaccording to activity (sloop, drowsiness, cognitive tasks).

_-RhythmThe mu -Rhythm, sometimes called Rolandic _ -rhythm, is infrequency and amplituderelated to posterior _ -rhythm, but its topography andphysiological meaning is quitedifferent. The _ stands for motor and it is strongly related tomovement functions ofthe motor cortex. This rhythm is very asymmetric, the negativeside being very shrap and


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spiky and the positive being rounded. Most commonly detected atfrequencies between9-11 Hz and values of 8 Hz or below may indicate brainabnormality. This rhythm is

detected over the precentral and postcentral region, usingelectrodes ofC3 and C4 of thestandard 10-20 system.

_ -Rhythm is blocked by movement, which may be active, passiveor reflexive. The blockingeffect is bilateral but more stronger in contralateral side i.e. if lefthand is moved, theblocking is stronger in the right side. This blocking appears beforeactual movement ofthe muscles, therefore it seems to related to conceptual planning

of the movement. The _-rhythm is also blocked by light tactile i.e. touching the skinlightly, so some researchersconsider the _ -rhythm to be the idling state of the sensorycortex and its vincinity.12Beta Rhythm

_ -rhythm band consists of basicly all frequencies above 13 Hz butpractically it is limited

to 50 Hz by the measurement limits and to 30 Hz by functionalfindings. Spatially

_ -rhythm can be found frontal and central regions. The central _-rhythm is related toRolandic_-rhythm (see (1.2.2)) and can be blocked by motoractivity and tactile simulation(planning to move)._ -rhythm rarely exceed amplitudes of 30 _Vand as a rule ofthe thumb when frequency increases the amplitude decreasesand vice versa. _ -rhythm isusually associated with increaced arousal and activity.Theta RhythmA band originally a part of the delta band is of frequencies from 4to 7 Hz. _ -rhythm hasgotten its name from the presumed origin, the thalamus. EEG of anormal adult consists


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little _ -frequencies and no organized _ -rhythm. However, the _-frequencies and _ -rhythm play important part during the childhood and in states ofdrowsiness and sleep.

_ -rhythm is associated with marking the maturity of themechanism linking the cortex,the thalamus and the hypothalmus. Also it is linked with feelingsof disappointment andfrustration. For some people the _ -rhythm is present whenperforming mental tasks e.g.problem solving or visualization.Delta Rhythm

This rhythm is detected when the subject is in deep sleep at latersleep periods. _ -rhythm

has a relatively high amplitude and low frequency, 3 Hz or less. _-rhythm decreases withage and can be a sign of brain abnormality if detected in theawake state.1.2.3 Evoked Responses from the UserIn order to communicate via brain activity, the user must be ableto control the EEG signal.

These types brain activities can be divided to two groups: Evokedresponses, which are

evoked responses by a sensory stimulus, such as flashing light,and spontaneous EEGsignals which occur without stimulus, such as _ or mu -rhythm,which the user can learnto control with the biofeedback(see section 1.3.3).13

The evoked responses can be further divided to three mainclasses:

_Evoked potentials (EP) require specific sensory stimulus. Anexample of EP isvisual evoked potential (VEP). If stimulus is given is a form of aflashing light, theEEG over the visual cortex will have the same frequency as theflashing light. A


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user can be trained to control the amplitude of the EEG over thevisual cortex bybiofeedback. VEP is very easy to detect, making the patternrecognition easy. But

the training time required to improve the control is long_Event-Related potentials (ERP) are DC changes to a discreteevent. The ERPis a response to a stimulus or an event and it either coincides orfollows it aftera short delay. But the ERP can also be detected in the absence ofthe stimulus ifthe actual stimulus is anticipated to happen or they may precedevoluntary motor

responses, i.e. moving a hand without being directed to do so.ERPs are believedto be generated by the brain through extracellular potentialsassociated with theactivity of groups of neurons firing in synchrony.Examples of ERPs are P3 or P300 ERP, which occurs 300 ms aftera specific stimulusthat to subject is told response. The stimulus has to be ofbernouilli type i.e.

more rarely occuring alternative from the two alternatives.Another example is slow cortical potentials (SCP), which are alarge increase ofsurface-negative cortical DC potential, caused by cognitiveprocessing in the brainlasting more than a second or two. When compared to the shorterlatency ERPsuch as P300, the SCPs reflect more global task-relatedprocesses. In figure (1.5)is shown SCP developement for linquistic and mental visualisationtasks. Noticehow linquistic task activates the frontal lobes (electrodes F3, F7)and the mentalvisualisation the posterior parietal lobes (electrodes P3, P4)

_


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Event-Related Synchronization (ERS) and Event-RelatedDeSynchronization(ERD) are the AC changes to a discrete event. More accuratelyERD/ERS is blocking

of the _ -rhythms due to sensory processing or blocking of the _-rhythm dueto motor behavior. ERD/ERS occurs during the cortical informationprocessing dueto the increased cellular excitability in thalamocortical systems.

The difference betweenERD and ERS is that a power decrease in the _ -band responds toERD andincrease to ERS.ERD/ERS starts few seconds before the actual movement and it

lasts few secondsand in order to restore the power levels to the reference level, theperiod betweentwo stimulus should randomized and no less than two seconds.14Figure 1.5: SCP developement for linquistic and mentalvisualisation tasks. [Nie99c]

The depth of ERD is affected by the complexity of the task ordepth of the attention

to the task.1.2.4 Measuring the EEG signal

The EEG stands for Electroencepephalogaphyand is measuringthe difference in electricalpotential between various places on the surface of the scalp. In anEEG measurementa potential difference between two electrodes is measured.Another one of the electrodesmay bepassive in a sense that it is not used to measure the brainactivity, but the backgroundelectric field of the skin. These electrodes are called referencesand they are attachedto ear lobes or mastoids. The placement of the references isdelicate, since a referencetoo close to the brain is corrupted by brain activity and areference on other parts


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of the body may be corrupted by muscles (especially hearts)electrical activity. The signalspicked up by electrodes may be combined to channels or achannel corresponds to a

single electrode. The signal is then amplified and filtered fromartifacts and displayed oncomputer screen.15Electrode Configurations

The first EEG measurements were done by Berger in 1929[Ber29]. In 1934 was foundthat EEG activity varied in different locatations of the head, whichlead to several differentelectrode placement configurations in order to achive the best

signal for currentexperiment. These different electrode configurations of coursemade the comparison ofthe results difficult, and therefore in 1958 a common standard,the 10-20 electrode configurationwas agreed to be the basic configuration. [Nie99d]. The 10-20system is shownin figure (1.2.4, left). In this system specific body landmarks areused to define electrode

Figure 1.6: On the left: the standard 10-20 electrodeconfiguration. On the right: the 10-20system augmented to 75 electrode configuration. [Nie99d]placement, instead of using constant distance between theelectrodes. This body landmarkbased system is easy to replicate in different laboratories andwhen measuring the EEGof a child, the distances between electrodes grow as the childgrows, but the placementremains consistent.In 10-20 system the electrodes are coded by letter that indicatesthe anatomic area, and thenumbering, in which the odd digits are for the left hemisphereand the even for the right.An exception are the midline electrodes, where the digit ischanged to letterz. As can be


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seen from figure (1.2.4, left), space have been left between theelectrodes for additionalelectrodes if needed, e.g. electrode F5 can be placed betweenelectrodes F3 and F7. An

example of the modified 10-20 system is shown in figure (1.2.4,right), which is a systemof 75 electrodes. The number of needed electrodes depend of thetype and location ofbrain activity and the number of channels available.

adaptive brain interfaces

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