soft introduction to brain-computer interfaces and machine learning
DESCRIPTION
Talk given at Spring School organized by Estonian Association of Psychology Students. Soft description of general BCI pipeline.TRANSCRIPT
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BRAIN-COMPUTER INTERFACE
Ilya Kuzovkin
7 June 2014
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Now I know how your brain
signal looks like when you think
“LEFT” and “RIGHT”
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Now I know how your brain
signal looks like when you think
“LEFT” and “RIGHT”
Try me — think or
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Now I know how your brain
signal looks like when you think
“LEFT” and “RIGHT”
Try me — think or It was
!!
wasn’t it?
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Now I know how your brain
signal looks like when you think
“LEFT” and “RIGHT”
Try me — think or
How would you use such technology?
It was !!
wasn’t it?
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BRAIN-COMPUTER INTERFACE
Mental intention
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Mental intention
Neuroimaging
BRAIN-COMPUTER INTERFACE
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Mental intention
Neuroimaging
Name some neuroimaging techniques
BRAIN-COMPUTER INTERFACE
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Mental intention
SignalNeuroimaging
BRAIN-COMPUTER INTERFACE
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Mental intention
Signal
Data
Neuroimaging
BRAIN-COMPUTER INTERFACE
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Mental intention
Signal
Data Algorithm
Neuroimaging
BRAIN-COMPUTER INTERFACE
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BRAIN-COMPUTER INTERFACE
Mental intention
Signal
Data Algorithm
With 87% certainty I can
say that you are thinking “LEFT”
right nowNeuroimaging
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NEURONS
http://biomedicalengineering.yolasite.com
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NEURONS
http://biomedicalengineering.yolasite.com
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NEURONS
http://www.conncad.com/gallery/single_cells.html
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NEURONS
http://en.wikipedia.org/wiki/Neural_oscillation
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NEURONS
http://en.wikipedia.org/wiki/Neural_oscillation
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NEURONS
http://en.wikipedia.org/wiki/Neural_oscillation
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NEURONS
http://en.wikipedia.org/wiki/Neural_oscillation
What is the frequency in this example?
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BRAINWAVES
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BRAINWAVESDelta 0-4 Hz
Theta 4-7 Hz
Alpha 7-14 Hz
Mu 8-13 Hz
Beta 15-30 Hz
Gamma 30-100 Hz
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BRAINWAVESDelta 0-4 Hz
Theta 4-7 Hz
Alpha 7-14 Hz
Mu 8-13 Hz
Beta 15-30 Hz
Gamma 30-100 Hz
slow wave sleep, babies, lesions
children, drowsiness, meditation, relaxed
closed eyes, relaxed
motor neuron in rest, mirror neurons
motor activity, anxious thinking, concentration
networking between populations of neurons
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EEG
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EEG
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EEG
TIME
CH
ANN
ELS
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EEG
TIME
CH
ANN
ELS
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EEG
Alpha 7-14 Hz
Beta 15-30 Hz
Gamma 30-100 Hz
?TIME
CH
ANN
ELS
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EEG
Alpha 7-14 Hz
Beta 15-30 Hz
Gamma 30-100 Hz
?TIME
CH
ANN
ELS
Jean Baptiste Joseph Fourier 1768 — 1830
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FOURIER TRANSFORM*
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FOURIER TRANSFORM*
*discrete
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FOURIER TRANSFORM*
*discrete
=
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FOURIER TRANSFORM*
*discrete
signal at time t frequency complex number
=
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FOURIER TRANSFORM*
*discrete
signal at time t frequency complex number
=
Amplitude of the component with
frequency k
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FOURIER TRANSFORM*
*discrete
signal at time t frequency complex number
=
Amplitude of the component with
frequency k
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DATA
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DATA
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DATA
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DATA
Why not like this?
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DATA
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TIME-FREQUENCY DOMAIN
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DATA
Are we done?
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DATA
Are we done?
Hint:
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DATA
300 MS
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DATA
300 MS
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DATA300 MS
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DATA300 MS
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DATA300 MS
11 channels 50 frequencies on each 3 seconds of data 300 ms window
• How many numbers to describe 1 reading of 300 ms?
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DATA300 MS
11 channels 50 frequencies on each 3 seconds of data 300 ms window
• How many numbers to describe 1 reading of 300 ms?
• How many numbers to describe all 3 seconds of data?
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DATASETIN
STAN
CES
FEATURES CLASSES
.!
.!
.
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MACHINE LEARNING
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MACHINE LEARNING
?
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set of sample objects (samples) is called training set
Machine Learning algorithm learns from examples,
MACHINE LEARNING
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Each object !!!!!!!!!!!!
can be described with a set of parameters called features
MACHINE LEARNING
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Tail length : ... f1
MACHINE LEARNING
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Furriness : ...
Tail length : ... f1
f2
MACHINE LEARNING
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Furriness : ...
Tail length : ... f1
f2
f = ( f1, f2 )Form a feature vector
MACHINE LEARNING
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Instance Feature 1 Feature 2 ClassCat 1 8 cm 546 h/cm MCat 2 7.5 cm 363 h/cm M
... ... ...Cat N 11 cm 614 h/cm F
Together feature vectors and corresponding classes form a dataset
MACHINE LEARNING
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Feature vectors live in a feature space
MACHINE LEARNING
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?
MACHINE LEARNING
Feature vectors live in a feature space
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MACHINE LEARNING
Feature vectors live in a feature space
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MACHINE LEARNING
Feature vectors live in a feature space
K-Nearest Neighbors
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!• Decision trees • C4.5 • Random forests • Bayesian networks • Hidden Markov models • Artificial neural network • Data clustering • Expectation-‐maximization algorithm • Self-‐organizing map • Radial basis function network • Vector Quantization • Generative topographic map • Information bottleneck method • IBSEAD • Apriori algorithm • Eclat algorithm • FP-‐growth algorithm • Single-‐linkage clustering • Conceptual clustering • K-‐means algorithm • Fuzzy clustering • Temporal difference learning • Q-‐learning • Learning Automata • Monte Carlo Method • SARSA
• AODE • Artificial neural network • Backpropagation • Naive Bayes classifier • Bayesian network • Bayesian knowledge base • Case-‐based reasoning • Decision trees • Inductive logic programming • Gaussian process regression • Gene expression programming • Group method of data handling (GMDH) • Learning Automata • Learning Vector Quantization • Logistic Model Tree • Decision trees • Decision graphs • Lazy learning
• Instance-‐based learning • Nearest Neighbor Algorithm • Analogical modeling • Probably approximately correct learning (PAC) • Symbolic machine learning algorithms • Subsymbolic machine learning algorithms • Support vector machines • Random Forests • Ensembles of classifiers • Bootstrap aggregating (bagging) • Boosting (meta-‐algorithm) • Ordinal classification • Regression analysis • Information fuzzy networks (IFN) • ANOVA • Linear classifiers • Fisher's linear discriminant • Logistic regression • Naive Bayes classifier • Perceptron • Support vector machines • Quadratic classifiers • k-‐nearest neighbor • Boosting
MACHINE LEARNING
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BRAIN-COMPUTER INTERFACE
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BRAIN-COMPUTER INTERFACE
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BRAIN-COMPUTER INTERFACE
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BRAIN-COMPUTER INTERFACE
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BRAIN-COMPUTER INTERFACE
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BRAIN-COMPUTER INTERFACE
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BRAIN-COMPUTER INTERFACE
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BRAIN-COMPUTER INTERFACE
?
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BRAIN-COMPUTER INTERFACE
?
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THE END