View
227
Download
0
Category
Preview:
Citation preview
8/10/2019 Part v P300-Based Brain Computer Interfaces
1/111
8/10/2019 Part v P300-Based Brain Computer Interfaces
2/111
Prepared by Ozgen Sumer LACIN
Class: EE 517 Therapeutic and ProstheticDevices
Date: 11/12/2012
P300-based Brain Computer
Interface (P300 BCI)
8/10/2019 Part v P300-Based Brain Computer Interfaces
3/111
Outline of Presentation BCI Definition & Methods
Potential Users of BCI and brief explanation of disease
Measuring Brain Activity Invasive Methods (ECoG, Cortical Microelectrodes)
Non-invasive Methods ( EEG,MEG,fMRI,NIRS)
BCI Approaches to Communication
Slow Cortical Potentials (SCP)
Steady State Visual Evoked Potentials (SSVEP) Motor Imagery Tasks
Evoked potentials (EP)
Framework of P300 System
1) Signal Acquisition
2) Feature Extraction
3) Feature Selection
4) Feature Classification
Comparison of classification techniques
METU BCI Research
BCI Companies
References
8/10/2019 Part v P300-Based Brain Computer Interfaces
4/111
Brain Computer Interface (BCI)
Brain Computer Interface (BCI), is a system which allow people tocommunicate with their environment and control prosthetic or otherexternal devices by using only their brain activity.
As aformal definition BCI is: a communication system in which messages or commands sends to the
external world DO NOT PASS THROUGH THE BRAINSNORMALOUTPUT PATHWAYS OF PERIPHERAL NERVES AND MUSCLESmeaning BCI provides a new pathway for its user to communicate withan external world. [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
5/111
BCI methods
BCI can be divided into 2 subsections :
1) Dependent BCI: Doesnt use brains normal output pathwaysto convey messages but activity in these pathways is needed togenerate activity. [1]
2) Independent BCI: Does not depend on any way of the brainsnormal output pathways and the message is not carried withperipheral muscles or nerves. The activity in these neurons isnot needed to generate the signal. [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
6/111
BCI Approaches for
Communication
Slow Cortical Potentials (SCP) Anticipation tasks
Steady State Visual Evoked Potentials (SSVEP) Flickering light of specific frequency
Motor Imagery Tasks Changes of mu rhytm, alpha and beta activity over the
sensorimotor areas
Imagination of hand, foot, tongue, movement
Evoked Potentials (EP) Focus of attention to a visual or auditory stimulation
P300 Signals[5]
8/10/2019 Part v P300-Based Brain Computer Interfaces
7/111
BCI system is mainly designed for people sufferingfrom the following disease:
Amyotrophic Lateral Sclerosis (ALS)
Multiple Sclerosis
Muscular Dystrophy
Cerebral Palsy
Brainstem Stroke
Spinal Cord Injury
Other types of Stroke [5]
Potential users of BCI
8/10/2019 Part v P300-Based Brain Computer Interfaces
8/111
Amytrophic Lateral Sclerosis(ALS)
ALS is also known as motor neuron disease andoccurs due to the degeneration and lack of neuralcells in the Central Nervous Systems (CNS),
brainstem and spinal cord.Due to these missing neural cells, disease ischaracterized by rapidly progressive weakness,muscle atrophy and fasciculations, muscle spasticity,difficulty speaking (dysarthria), difficulty
swallowing (dysphagia), and difficulty breathing(dyspnea). [25]
ALS Disease
http://www.youtube.com/watch?v=pOvvW8gbWSAhttp://www.youtube.com/watch?v=pOvvW8gbWSAhttp://www.youtube.com/watch?v=pOvvW8gbWSAhttp://www.youtube.com/watch?v=pOvvW8gbWSA8/10/2019 Part v P300-Based Brain Computer Interfaces
9/111
Multiple Sclerosis (MS)
Multiple sclerosis (MS), also known as"disseminated sclerosis". It is aninflammatory disease in which the fattymyelin sheaths around the axons of thebrain and spinal cord are damaged, leading
to demyelination and scarring. Disease onset usually occurs in young
adults, and it is more common in women.It has a prevalence that ranges between 2and 150 per 100,000. [3]Fig. Multiple Sclerosis Explanation
[21]
Fig. 3 Demyelination [22]
8/10/2019 Part v P300-Based Brain Computer Interfaces
10/111
Muscular dystrophy (MD)
Muscular dystrophy (MD) is a group ofmuscle diseases that weaken themusculoskeletal system and hamperlocomotion. Muscular dystrophies arecharacterized by progressive skeletalmuscle weakness, defects in muscleproteins, and the death of muscle cells
and tissue.[4]
Fig.4 Cell condtionafterMuscular dystrophy [23]
Fig.5 demonstration of musculardystrophy on human body [24]
8/10/2019 Part v P300-Based Brain Computer Interfaces
11/111
Cerebral Palsy & Brainstem Stroke
Cerebral palsy (CP) is a group of non-progressive, non-contagious motor conditions that cause physicaldisability in human development, chiefly in the variousareas of body movement
A Brain stem stroke syndrome is a condition involving astroke of the brain stem. Because of their location, theyoften involve impairment both of the cranial nuclei and
of the long tracts.
8/10/2019 Part v P300-Based Brain Computer Interfaces
12/111
Ways to overcome the disabilities
1) Improving the capabilities of remaining pathways
2) Restoring function by detouring around breaks in
the neural pathways that control muscles.
3)BCI for conveying messages and commands to
external world. [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
13/111
The applications of BCI
Fig.6 Applications of BCI: i) Wheel chair, ii) robotic arm, iii)speller
8/10/2019 Part v P300-Based Brain Computer Interfaces
14/111
Framework of BCI System
Fig. 7: Frame work of BCI system [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
15/111
Measuring the brain activity Several ways to measure the brain activity which are electrical,
magnetic or hemodynamic activity measurements. Electrical measurements are preferred due to their practical usage whereas
other measuring methodologies are not practical due to their size and non-portability, MEG, fMRI etc.
There are two basic methods for brain activity measurement. i) Invasive methods i.e, require a surgical operation such as
electrocorticogram (ECoG) and microelectrode arrays
ii) Non-invasive method which does not require a surgical operation such as
electroencephalography(EEG), magnetoencephalography(MEG), near infraredspectroscopy(NIRS), functional magnetic resonance imaging(fMRI)
8/10/2019 Part v P300-Based Brain Computer Interfaces
16/111
Invasive Methods Electrocorticogram and Cortical Microelectrodes
Electrocorticogram (ECoG) is an invasive method in which theelectrical signals of the brain are measured under the skull, from thesurface of the cortex.
The electrodes are usually madeup of a conductive
biocompatible needle or a grid
of needles and are implementedon the cortex surface with asurgical operation.
Fig.8 ECoG demonstration ofthe position of electrodes [14]
8/10/2019 Part v P300-Based Brain Computer Interfaces
17/111
Cortical Microelectrodes Similar to ECoG but placed inside the cortex. Electrodes developed with VLSI technology.
The signal quality is improved by integrated analog circuitsdesign.
Possible to detect the activity of a single neuron with high spatialresolution and excellent signal-to-noise ratio (SNR)
Fig. 9 Cortical Microelectrodes [5]
8/10/2019 Part v P300-Based Brain Computer Interfaces
18/111
Non-invasive methods
1) Electroencephalography (EEG)
2) Magnetoencephalography (MEG)
3) Functional Magnetic Resonance Imaging (fMRI) 4) Near Infrared Spectroscopy (NIRS)
8/10/2019 Part v P300-Based Brain Computer Interfaces
19/111
1)Electroencephalography(EEG)
Electroencephalography (EEG) is the recording of electricalactivity along the scalp.
EEG measures voltage fluctuations resulting from ionic currentflows within the neurons of the brain. [26]
EEG has three main clinical usage:1) In neurology, the main diagnostic application of EEG is in the case
of epilepsy, as epileptic activity can create clear abnormalities on astandard EEG study.
2) Diagnosis of coma, encephalopathies (disorder of brain), and braindeath.
3) Investigating sleep and sleep disorders. [26]
8/10/2019 Part v P300-Based Brain Computer Interfaces
20/111
EEG and its instruments
Fig 10. The measurement system consists of a number of electrodes, abiopotential amplifier and recording/monitoring devices. [28]
8/10/2019 Part v P300-Based Brain Computer Interfaces
21/111
First usages of EEG
The first human EEG recordingobtained by Hans Berger in1924. The upper tracing is EEG,and the lower is a 10 Hz timingsignal.
EEG used to be a first-line method for the diagnosis of tumors, stroke andother focal brain disorders, but this use has decreased with the advent ofanatomical imaging techniques with high (
8/10/2019 Part v P300-Based Brain Computer Interfaces
22/111
Recording principle of EEG(1/2)
1) Electrodes are placed on a scalp with a conductive gel or paste afterpreparing the area by light abrasion i.e., corrosion and remove deadskins to reduce impedance. Cap is used for when high density array ofelectrodes are needed. [26]
2) Each electrode is connected to one input of a differential amplifier, acommon system reference electrode is connected to the other input ofeach differential amplifier. These amplifiers amplify the voltage betweenthe active electrode and the reference (typically 1,000100,000 times,or 60100 dB of voltage gain) because a typical adult human EEG signal
is about 10V to 100 V in amplitude when measured from the scalpand is about 1020 mV when measured from subdural electrodes. [26]
8/10/2019 Part v P300-Based Brain Computer Interfaces
23/111
Recording principle of EEG(2/2)
In analog EEG, the signal is then filtered, and the amplified signalis digitized via an analog-to-digital converter
Mainly there are 2 kinds of filters which are low-pass(LPF) andhigh-pass filters(HPF).
LPF: filters out high-frequency artifacts, such aselectromyographic signals
HPF: filters out slow artifact, such as electrogalvanic signals andmovement artifact.
8/10/2019 Part v P300-Based Brain Computer Interfaces
24/111
EEG CAP Specifications and
Electrode Positions
Fig. 12 The 10-20 international system is the standard naming and positioningscheme for EEG applications [27]
8/10/2019 Part v P300-Based Brain Computer Interfaces
25/111
Channel Selection
10 channels Fz, Cz, C3, C4, Pz, P3, P4, PO7, PO8, Oz
MeinickeKaper
Guger tech.
8/10/2019 Part v P300-Based Brain Computer Interfaces
26/111
Advantages vs. Disadvantages
1) Low spatial resolution2) Determines only the
activity occurs on theupper part of cortex
3) Unlike PET and MRS,cannot identifyspecific locations inthe brain
4) Takes long time toconnect
5) Low Signal to NoiseRatio (SNR)
1)Cheap, silent, portable.2) very high temporal resolution3) relatively tolerant of subject movement,unlike all other neuroimaging techniques
4) does not involve exposure to high-intensity (>1 Tesla) magnetic fields, as insome of the other techniques, especiallyMRI and MRS.5) studies can be conducted with relatively
simple paradigms
8/10/2019 Part v P300-Based Brain Computer Interfaces
27/111
2) Magnetoencephalography(MEG)
Magnetoencephalography (MEG) is a technique formapping brain activity by recording magnetic fieldsproduced by electrical currents occurring naturally inthe brain, using very sensitive magnetometers.
Due to its size, its just impracticalfor BCI applications.
Fig. 13 MEG device for clinical usage, [30]
8/10/2019 Part v P300-Based Brain Computer Interfaces
28/111
3) Functional Magnetic
Resonance Imaging (fMRI)
fMRI is a method to measure the amount of oxygen inthe blood flowing through brain. When the neuronsare active the consumption of the oxygen increases inthe cells. Therefore it gives an idea about neuralactivity in different regions of brain.
High spatial resolution
Low temporal resolution
Fig. 14 fMRI device for clinical usage[31]
8/10/2019 Part v P300-Based Brain Computer Interfaces
29/111
Near Infrared Spectroscopy (NIRS)
Similar to fMRI. The principle is to detect the
amount of blood oxygen in the brainfrom the reflection of the emitted
infrared light. As the hemodynamicactivity is measured, the temporalresolution is poor in NIRS systems,which makes the method impractical
for BCI applications.
Fig. 15 NIRS and its characteristics
8/10/2019 Part v P300-Based Brain Computer Interfaces
30/111
BCI Approaches to Communication
1) Slow Cortical Potentials
2) Steady State Visual Evoked Potentials
3) Motor Imagery Tasks 4) Evoked Potentials
8/10/2019 Part v P300-Based Brain Computer Interfaces
31/111
1) Slow Cortical Potentials
They are among the lowest frequency features of scalp recordedEEG.
These potential shifts occur over 0.510.0 s and are called slow
cortical potentials (SCPs). Negative SCPs are typically associatedwith movement and other functions involving cortical activation,while positive SCPs are usually associated with reduced corticalactivation .
They can be generalized as anticipationtasks[1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
32/111
1) Slow Cortical Potentials
Fig.16 SCP characteristics [1]
Success in patients in late stage ALS
8/10/2019 Part v P300-Based Brain Computer Interfaces
33/111
2) Steady state evoked potentials
(SSVEP)
SSVEPs are oscillating signals elicited in the brainaccording to frequency of presented visualstimulation.
These signals are more distinctive in occipital regions
of the brain that is related to visual activities. SSVEP is employed in BCI applications by the
presentation of several flickering light sources withdifferent frequencies. In such a paradigm, the focusedlight elicits a signal pattern of the same frequency orharmonics with that of the source. [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
34/111
2) SSVEPs
1) http://www.bsp.brain.riken.jp/~hova/ssvep_data_Bakardjian_LABSP.html
2) Show videos
http://www.bsp.brain.riken.jp/~hova/ssvep_data_Bakardjian_LABSP.htmlhttp://www.bsp.brain.riken.jp/~hova/ssvep_data_Bakardjian_LABSP.htmlhttp://www.bsp.brain.riken.jp/~hova/ssvep_data_Bakardjian_LABSP.htmlhttp://www.bsp.brain.riken.jp/~hova/ssvep_data_Bakardjian_LABSP.html8/10/2019 Part v P300-Based Brain Computer Interfaces
35/111
Sensory Motor Rhythms (SMR)
Idling activity can be called as mu-rhythm.(8-12 Hz during noengagement)
The amplitude of the signals may change during different brainactivities such as concentrating, voluntary muscle movement.
Movement or preparation for movement is typicallyaccompanied by a decrease in mu and beta rhythms andincrease in alpha rhythm called as event-related de-synchronization or ERD Its opposite, rhythm increase, orevent-related synchronization (ERS) occurs after movement
and with relaxation [1] ERD and ERS do not require actual
movement, they occur also with motor imagery. [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
36/111
SMR&ERD&ERS
Fig.17: Sensorimotor characteristics and ERD &ERS waves respectively [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
37/111
Wave Characteristics
Table 1: Wave frequencies and characteristics [3]
8/10/2019 Part v P300-Based Brain Computer Interfaces
38/111
P300 Speller
The idea in this paradigm is to detect the P300 responses elicitedby the subject and predict the focused character according to thestarting time of the P300 response.
The target character is at the intersection of 1 row and 1 column
intensification. When these two stimulations are found, it is easyto predict the target character
Fig.17 P300 Characteristic Wave [4]
8/10/2019 Part v P300-Based Brain Computer Interfaces
39/111
Oddball Paradigm P300 Signals
The oddball paradigmis a technique used in evokedpotential research in which trains of stimuli that areusually auditory or visual are used to assess the neuralreactions to unpredictable but recognizable events.
The subject is asked to react either by counting or bybutton pressing incidences of target stimuli that are hiddenas rare occurrences amongst a series of more commonstimuli, that often require no response. It has been found
that an evoked research potential across the parieto-central area of the skull that is usually around 300 ms andcalled P300 is larger after the target stimulus. [3]
8/10/2019 Part v P300-Based Brain Computer Interfaces
40/111
Spelling Paradigm
2 target 12nontarget visualstimulations
Counting the
targetintensificationswill elicit the socalled P300responses
Video 1: P300 speller [5]
8/10/2019 Part v P300-Based Brain Computer Interfaces
41/111
P300 Speller
Target (P300) responses Positive signal pattern peaking nearly 300 ms after the presentation of the target
stimulation Have latency of 300 - 400ms
Nontarget responses Have lower amplitute
Pattern similar to a sinusoidal of the same frequency with the stimulation
100 200 300 400 500 600 700
-50
0
50
time (ms)
Amplitude(
ADC
value) Channel FZ
100 200 300 400 500 600 70
-50
0
50
time (ms)
Amplitude(ADC
value) Channel FZ
Fig. 18 Target vs non-target amplitude in P300 [5]
8/10/2019 Part v P300-Based Brain Computer Interfaces
42/111
P300 Based BCI Systems
Spelling Application
Intelligent House Systems with VirtualReality (VR)
Controlling robotic or prostheticdevices.
8/10/2019 Part v P300-Based Brain Computer Interfaces
43/111
P300 Speller
Problems : The noise in EEG recordings
Factors in the cognitive process (fatigue, being unable to focus)
Repeating the intensification procedure for the focused
character Reducing the effect of noise by ensemble averaging of the
observations.
Main Problem:
Decreasing prediction time. [5]
8/10/2019 Part v P300-Based Brain Computer Interfaces
44/111
P300 Speller - Studies
Graz University of Technology, Pfurtscheller et al. Wadsworth Center - Albany, Wolpaw et al. Tsinghua University, Gao et al. Fraunhofer-FIRST - Berlin, Blankertz & Mller
University of Rome - La Sapienza, Babiloni et al. University of Tuebingen, Kbler & Birbaumer University of Gttingen, Meinicke GtecGuger Technologies Sabanc niversitesi, Argunsah et al. ... [5]
8/10/2019 Part v P300-Based Brain Computer Interfaces
45/111
P300 Signal Processing
8/10/2019 Part v P300-Based Brain Computer Interfaces
46/111
1) Signal Acquisition
The extracted signal has verylow amplitude which is in thelevel of microvolts.
Highly sensitive to external andinternal distortions. Need for pre-processing
technique (filter) to enhance thesignal rather than just amplifying
the signal.
Fig. 19 Signal AcquisitionBlock [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
47/111
Signal Enhancement (1/2)
Signal enhancement is applied prior to featureextraction to increase SNR. [2]
The use of a pre-processing technique has been proven
to be useful. [10] Number of electrodes, recording technology and
neuromechanism of BCI are some of factors todetermine for a suitable technique.
8/10/2019 Part v P300-Based Brain Computer Interfaces
48/111
Signal Enhancement(2/2)
Commonly used signal enhancement techniques are
1) Surface Laplacian (SL)
2) Common Average Referencing (CAR)
8/10/2019 Part v P300-Based Brain Computer Interfaces
49/111
1) Surface Laplacian
Smeared and intermixed current flow from brain to head. The spatial resolution of EEG decreases.
Surface Laplacian counters this effect by refocusing thesensitivity characteristic of the EEG electrodes to a smallvolume right below each electrode, thus eliminating theintermixing of the brain currents. [15]
Figure 20: Surface Laplacian[15]
8/10/2019 Part v P300-Based Brain Computer Interfaces
50/111
Common Average Referencing(CAR)
The common average reference spatial filter calculates the mean of all channels,and subtracts this value from the output channel of interest. [16]
If electrodes are equally spaced result is zero mean spatial distribution. [6]
Fig. 21 Different Filtering Techniques i) Ear Reference, ii) CommonAverage Reference, iii) Small Laplacian, iv) Large Laplacian [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
51/111
Which spatial filter provides the
highest SNR?
Since noise is highly complex, hence; there is a need for a filterwith high SNR.
1) Ear reference?
2) Common Average Reference? 3) Small Laplacian?
4) Large Laplacian?
8/10/2019 Part v P300-Based Brain Computer Interfaces
52/111
Small and Large Laplacian
Electrode numbers over entirescalp and the surroundingelectrodes of reference pointsare important.
The distances to the set of
surrounding electrodesdetermine the spatial filteringcharacteristics of theLaplacian.
Small distance is more
sensitive to higher spatialfrequencies and less sensitiveto lower spatial frequencies
Fig. 22: Demonstration of Small and LargeLaplacian [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
53/111
Results (1/2)
Fig. 23 Average voltage spectra for top targets (solid lines) and bottom targets (dashed lines) andaverage spectra of r^2 for the top/bottom difference for all sessions of all subjects for thelocations that controlled cursor movement online.[6]
8/10/2019 Part v P300-Based Brain Computer Interfaces
54/111
Results (2/2)
CAR and Large Laplacian have the highest SNR then Small Laplacian thenear reference. [6]
Table 2: SNR values of different filter techniques [6]
Signal Enhancement Techniques
8/10/2019 Part v P300-Based Brain Computer Interfaces
55/111
Signal Enhancement Techniques
used in literatureERN= event-related negativitySA-UK= Succesive averagingand / or considering choice ofunknownDSLVQ= Distinctive Sensitivelearning vector quantizationPCA= Principal ComponentAnalysis
GA= Genetic AlgorithmFreq-Norm= FrequencynormalizationCSSD= Common spatialsubspace decompositionCSP= Common spatialpatterns
ICA= Independent componentanalysisPCA= Principal componentanalysisSL= Surface LaplacianCAR= Common Average
Reference
Fig 24. Signal enhancement, feature selection /dimensionality reduction and post-processing methods in
BCI designs. [2]
8/10/2019 Part v P300-Based Brain Computer Interfaces
56/111
Signal Processing part
Since the raw data from signalacquisition block might containredundant information.(e.g.EEG data)
1) Signals are digitally filtered2) Unnecessary information iseliminated by data selection. Inthe preprocessing stage
3) Noise reduction, downsampling etc. is done
Fig. 25 Signal Processing Block and itscomponents. [1]
8/10/2019 Part v P300-Based Brain Computer Interfaces
57/111
1) Feature Extraction
The feature extraction is the stage inwhich the most relevant information forclassifying the EEG patterns is
investigated. Depending on thecomplexity of the BCI application, thefeature extraction is performed eithermanually or with the application ofoptimization algorithms. The aim of this
stage is to improve the classificationperformance of the BCI system and it isusually performed together with theclassification stage.
Fig 26. Feature Extraction
MATLAB [17]
Fig 27. FeatureExtraction of a face. [18]
Feature Extraction Methods used
8/10/2019 Part v P300-Based Brain Computer Interfaces
58/111
Feature Extraction Methods used
in literature
In literature, scientists dealing with P300 based BCIused the following techniques to extract features oftheir signals.
Table 3. Feature extraction methods in BCI designs. Refer to appendix B insupplementary data for a more detailed version of this table. [2]
8/10/2019 Part v P300-Based Brain Computer Interfaces
59/111
The distribution of the feature extraction
techniques with respect to application
Our interest is P300signals!!
Fig 27. Feature Extraction methods in BCI designs based on sensorimotor activity,VEP, P300, SCP, response to mental tasks, activity of neural cells and multiple
neuromechanisms. [2]
8/10/2019 Part v P300-Based Brain Computer Interfaces
60/111
Feature Extraction
Commonly used methodologies for BCI are:1. Time and/or frequency methods.
Time methods have great temporal resolution whereas frequency methodsare preferred due to simplicity in use and fast computation.
2. Combination of temporal content with spectral informationthe time-frequency (TF)
Short time Fourier Transform & Wavelet Transformation are well knownones.
8/10/2019 Part v P300-Based Brain Computer Interfaces
61/111
Time Frequency (TF) Analysis
The main approach of TF analysis is the combinationof time and frequency by using both of theiradvantages.
Time and frequency characteristics of ERD/ERS varyaccording to subject and yields a lot of temporal &spectral features.
8/10/2019 Part v P300-Based Brain Computer Interfaces
62/111
Time Frequency Analysis
temporal resolution Spectral resolution
Fig.28 Combination of temporal vs spectral resolution to obtain a TFfilter
8/10/2019 Part v P300-Based Brain Computer Interfaces
63/111
What is Time-Frequency Analysis
briefly?
Analysis providing time-varying spectralrepresentation of a signal which corresponds to thepower spectrum w.r.t time.
There are 2 methods:
Short Time Fourier Transform(STFT)
Morlet Wavelet Transform
8/10/2019 Part v P300-Based Brain Computer Interfaces
64/111
STFT- briefly
STFT is fundamental for analyzing the slowly timevarying signal.
In contrast to FT, it can give information on the timeresolution of the spectrum by analyzing the frequencyresponse at different time instant.
Most popular one is Fast Fourier Transform(FFT)based on STFT.
8/10/2019 Part v P300-Based Brain Computer Interfaces
65/111
The methodology of STFT
Signal is multiplied bya moving fixed lengthwindow functionwhich is non- zero for
a short period oftime[4]. Then FT isapplied within thewindow.
Fig. 29 STFT method for rectangular windowingwith 50% overlapping [10]
8/10/2019 Part v P300-Based Brain Computer Interfaces
66/111
Morlet Wavelet Transform
Wavelet Transform decomposes signals into waveletswhich are localized both in time & frequency domain.
It is suitable for non-stationary signals (EEG signals).
Wavelet Transform is more realistic than STFT. Varying window as a function of frequency (in STFT
fixed window).
8/10/2019 Part v P300-Based Brain Computer Interfaces
67/111
Why Morlet Wavelet Transform?
It is used in P300-based BCI, because EEG signal has aGaussian distribution in both time & frequencydomain and also suitable for motor imagery patterns
The width of its sliding windows varies as a function offrequency. [4]
Types of wavelet is determined according to thecharacteristics of the signal to be processed.
8/10/2019 Part v P300-Based Brain Computer Interfaces
68/111
Morlet Wavelet characteristics
Fig. 30 Morlet Wavelet characteristic Equation [4]
8/10/2019 Part v P300-Based Brain Computer Interfaces
69/111
Feature Selection
Algorithms are used to find the most informativefeatures for classification. [2]
Transformation of raw signal into a new structure toperform a better classification.
Remove the unnecessary information, keep thediscriminative ones.
Necessary in high dimension training data.
Higher classification accuracy and time saving.
Commonly used Feature Selection
8/10/2019 Part v P300-Based Brain Computer Interfaces
70/111
y
methods
There are two mainly used feature selection methodswhich are :
1) Principal Component Analysis (PCA)
2) Genetic Algorithms (GA)
3) Learning Vector Quantization (LVQ)
4) Common Spatial Pattern (CSP)
Principal Component
8/10/2019 Part v P300-Based Brain Computer Interfaces
71/111
p p
Analysis(PCA)
PCA is linear transformation that reducesdimensionality while retaining the ones thatcontributes to the variance most by keeping lowerorder principal components and ignoring high-order
ones. Since low-order components contain mostimportant aspects of the data.
8/10/2019 Part v P300-Based Brain Computer Interfaces
72/111
Genetic Algorithms
Heuristic (depends on exploring) search techniques. Typically maintain a constant-sized population.
Tries to minimize the features to be used in
classification and maximize the performance ofclassification..
Ideal for applications where domain knowledge andtheory is difficult or impossible to provide. (De Jong
1975)
8/10/2019 Part v P300-Based Brain Computer Interfaces
73/111
Feature Selection Results (1/2)
[11]
[11]
[11]
8/10/2019 Part v P300-Based Brain Computer Interfaces
74/111
Feature Selection Results (2/2)
As can be seen, the results using only the selected features are far better than thoseusing all features. This shows how important feature selection is in the context ofEEG classification where a lot of channels only partially contain information aboutthe studied phenomenon.[11]
Learning Vector Quantization
8/10/2019 Part v P300-Based Brain Computer Interfaces
75/111
g Q
(LVQ)
Neural Network Based Method. Aim is to find the proper reference vectors to be used
as the nearest neighbor classifier's reference set [4].
LVQ creates clusters of the training data and assignsthem to relevant classes.
The goal of LVQ is to find an optimal distribution ofthe clusters in the n-dimensional vector space.[4]
8/10/2019 Part v P300-Based Brain Computer Interfaces
76/111
Common Spatial Pattern (CSP)
The principal idea is to project the multi-channel EEGdata into a low-dimensional data by weighting thesignals measured from electrodes. [4]
The idea of CSP is to find a spatial filter such that theprojected signals have high power for one class andlow power for the other in order to provideseparability.
8/10/2019 Part v P300-Based Brain Computer Interfaces
77/111
Classification
Translating brain signals into device commands isachieved mainly by classification.
Understanding the features and their properties isnecessary to select the most appropriate classifier forgiven BCI system.
Amplitude of EEG signals, Band Power (BP), PowerSpectral Density(PSD), Auto-regressive
parameters(AR) should be determined for the designof BCI.
8/10/2019 Part v P300-Based Brain Computer Interfaces
78/111
Critical Features of BCI system
noise and outliers high dimensionality
time information
non-stationary small training sets
8/10/2019 Part v P300-Based Brain Computer Interfaces
79/111
Classifier Taxonomy
In order to choose the most appropriate classifier, theproperties of the available classifiers must be known.
1. Generative-discriminative
2. Static-dynamic3. Stable-unstable
4. Regularized
8/10/2019 Part v P300-Based Brain Computer Interfaces
80/111
Main classification problems
The curse of dimensionality Training data should be at least 5-10 times more than
feature vector.
Unfortunately this cannot be applied in all BCI systems
due to training data set size. The Bias-Variance trade-off
Classification error can be described under 3 majorpossible sources
noise: noise in the system. it is irreducible. bias: divergence between estimated and best mapping
variance: reflects the sensitivity to the training set.
Popular classification techniques
8/10/2019 Part v P300-Based Brain Computer Interfaces
81/111
in BCI research
1) Linear Classifiers
2) Neural Networks
3) Non-linear Bayesian classifiers
4) Nearest Neighbor Classifiers 5) Combination of Classifiers
8/10/2019 Part v P300-Based Brain Computer Interfaces
82/111
1) Linear Classifiers
Discriminant algorithms to distinguish the classes.
Probably most popular algorithms
There are two main classifier have been used:
Linear Discriminant Analysis (LDA) Support Vector Machine (SVM)
8/10/2019 Part v P300-Based Brain Computer Interfaces
83/111
Linear Discriminant Analysis (LDA)
Use hyperplanes to separate the different data.
One versus the rest.
For a two class problem:
Fig 32: A hyperplane which separates two classes: the circles andthe crosses [12]
8/10/2019 Part v P300-Based Brain Computer Interfaces
84/111
Pros and Cons
Pros 1) Low computational requirement
2) Simple to use
3) Provides good and accurate results
4) Great number of success in BCI system [12]
Cons
1) Provides poor results on complex non-linear EEG
data. [12]
8/10/2019 Part v P300-Based Brain Computer Interfaces
85/111
2) Support Vector Machine (SVM)
Also uses hyper-plane(s)
Good separation is achieved by the hyper-plane thathas the largest distance to the nearest training datapoint of any class (called functional margin).
The larger the margin the lower the generalizationerror of the classifier.
8/10/2019 Part v P300-Based Brain Computer Interfaces
86/111
SVM
H3 (green) doesn'tseparate the two classes.H1 (blue) does, with asmall margin and H2(red) with themaximum margin. [13]
Fig 33. Support vector machine representation [13]
8/10/2019 Part v P300-Based Brain Computer Interfaces
87/111
Neural Networks (NN)
Together with linear classifiers, they are mostly usedin BCI research.
NN is an assembly of artificial neurons.
NNs can be clustered under two categories: 1) Multilayer Perceptron (MLP)
2) Other Neural Network architectures
8/10/2019 Part v P300-Based Brain Computer Interfaces
88/111
Multilayer Perceptron (MLP)
MLP is composed of several layers of neurons : an input layer
several hidden layers
output layers
when composed of enough neurons, MLP canapproximate any continuous function.
Fig 34. Artificial Neural Network of agroup of interconnected nodes [20]
Other Neural Network
8/10/2019 Part v P300-Based Brain Computer Interfaces
89/111
Architectures
1) There is one that among all NN architectures whichhas been specially created for BCI: Gaussian Classifier[13].
This classifier has been applied with success to motorimagery and mental task classification.
BCI team in EPFL state that this NN outperformsMLP on BCI data. [14]
8/10/2019 Part v P300-Based Brain Computer Interfaces
90/111
Non- Linear Bayesian Classifiers
Bayesian classifiers produce nonlinear decisionboundaries.
Their generative characteristics enables them toperform more efficient rejection of uncertain samplesthan discriminative classifiers.
They are not widespread as linear classifiers or NeuralNetworks in BCI applications because they are not fast
enough for real time BCI applications. [12].
8/10/2019 Part v P300-Based Brain Computer Interfaces
91/111
4) Nearest Neighbor Classifiers
Supervised learning algorithm where classification ofnew coming signal is based on nearest neighborclassification.
The purpose is to sample the signal according to theattribute of training samples.
Assume Dtis the distance between the training sampleand the actual sample. Choosing the minimum
distance will allow us to choose the prediction of class.
8/10/2019 Part v P300-Based Brain Computer Interfaces
92/111
Nearest Neighbor Classification - NN
Fig. 35 Demonstration of the Nearest Neighbor Classification. Circlesand rectangles represent different classes. An unknown object (star) isclassified as a circle because the closest object is a circle.[12]
8/10/2019 Part v P300-Based Brain Computer Interfaces
93/111
5) Combinations of Classifiers
Recent trend is to combine different classifiers.Strategies are: 1) Boosting: Using several classifiers in cascade. Each
classifier focuses the errors committed by the previous
one. 2) Voting: Each different classifier assign the input
feature vector to a class. Majority will be the final class.It is simple and efficient. (like political voting)
3) Stacking: Each of several classifiers classify the inputfeature vector. Output of each of these classifiers isgiven as input to a so-called meta-classifier.
8/10/2019 Part v P300-Based Brain Computer Interfaces
94/111
Nominees for Classification
1) SVM 2) Dynamic classifiers
3) Combination of classifiers
Properties of Classifications
8/10/2019 Part v P300-Based Brain Computer Interfaces
95/111
p
Table 4: Accuracy of classifiers in movement intention based
BCI [2]
8/10/2019 Part v P300-Based Brain Computer Interfaces
96/111
Table 5: Accuracy of classifiers in pure motor imagery based BCI [2]
8/10/2019 Part v P300-Based Brain Computer Interfaces
97/111
Table 5: Accuracy of classifiers in pure motor imagery based BCI:multiclass and / or asynchronous case [2]
8/10/2019 Part v P300-Based Brain Computer Interfaces
98/111
Table 5: Accuracy of classifiers in P300 speller BCI [2]
The classification award for BCI
8/10/2019 Part v P300-Based Brain Computer Interfaces
99/111
goes to..
SVM
Classification Translation
8/10/2019 Part v P300-Based Brain Computer Interfaces
100/111
Algorithm
Fig. 35 Classification Translation Algorithm [37]
I METU B i R h LAB
8/10/2019 Part v P300-Based Brain Computer Interfaces
101/111
In METU Brain Research LAB
There has been made two P300 based research: 1) Hasan Balkar Erdogan: A DESIGN AND
IMPLEMENTATION OF P300 BASED BRAIN-COMPUTER INTERFACE, 2009
2) Berna Akinci: REALIZATION OF A CUE BASEDMOTOR IMAGERY BRAIN COMPUTERINTERFACE WITH ITS POTENTIAL APPLICATION
TO A WHEELCHAIR, 2010
METU BCI R h
8/10/2019 Part v P300-Based Brain Computer Interfaces
102/111
METU BCI Research
http://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdi
http://www.youtube.com/watch?v=gnWSah4RD2E
http://www.youtube.com/watch?v=ppILwXwsMng&feature=fvwrel
BCI C i i th ld
http://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://www.youtube.com/watch?v=gnWSah4RD2Ehttp://www.youtube.com/watch?v=ppILwXwsMng&feature=fvwrelhttp://www.youtube.com/watch?v=ppILwXwsMng&feature=fvwrelhttp://www.youtube.com/watch?v=ppILwXwsMng&feature=fvwrelhttp://www.youtube.com/watch?v=ppILwXwsMng&feature=fvwrelhttp://www.youtube.com/watch?v=ppILwXwsMng&feature=fvwrelhttp://www.youtube.com/watch?v=gnWSah4RD2Ehttp://www.youtube.com/watch?v=gnWSah4RD2Ehttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdihttp://video.cnnturk.com/2010/bilim-teknoloji/4/15/odtululer-beyin-sinyallerini-harekete-cevirdi8/10/2019 Part v P300-Based Brain Computer Interfaces
103/111
BCI Companies in the world
1) Gtec 2) Emotiv Epoc
Gt P j t
8/10/2019 Part v P300-Based Brain Computer Interfaces
104/111
Gtec Projects:
1) ALIAS: Adaptable Ambient Living Assistant -Mobile Robot System that interacts with elderly users,monitors physiology and uses BCI for control.
2) SM4ALL: smart homes for all - use BCIs to controlsmart homes
3) VERE: Virtual Embodiment and Robotic Re-Embodiment - BCIs for avatar control
Gt h
8/10/2019 Part v P300-Based Brain Computer Interfaces
105/111
Gtec research areas:
Emoti Epoc
8/10/2019 Part v P300-Based Brain Computer Interfaces
106/111
Emotiv - Epoc
Show video!
Thank you!!
8/10/2019 Part v P300-Based Brain Computer Interfaces
107/111
Thank you!!
References
8/10/2019 Part v P300-Based Brain Computer Interfaces
108/111
References [1] Wolpaw J.R., Birbaumer N., McFarland D.J., Pfurtscheller G., Vaughan T.M., Brain
Computer Interfaces for Communication and Control,Clinical Neurophysiology, 113:767-791, March 2002
[2] Bashashati A. , Fatourechi M. , Ward R, Asurvey of signal processing algorithms inbrain-computer interfaces based on electrical brain signals. J. Neural Eng. R32-R572007
[3] http://en.wikipedia.org/wiki/P300_(neuroscience)#P3a_and_P3b [4] Akinci,B. Realization of a cue based motor imagery brain computer interface with its
potential application to a wheel chair.,METU Library, 2010
[5] Erdoan H. B., A Design and Implementation of P300 Based Brain- Computer Interface,Metu Library, 2009.
[6] McFarland D.J., McCane L.M., David S.V., Wolpaw J.R., Spatialfilter selection for EEG-basedcommunication,Electroencephalogr. Clin. Neurophysiol,Vol. 103, pp. 386-394.
References
8/10/2019 Part v P300-Based Brain Computer Interfaces
109/111
References
[7] Chapin J.K., Nicolelis M.A. L., "Principle component analysis of neuronal ensemble activityreveals multidimensional somatosensory representations", J. Neurosci. Meth, Vol. 94, pp. 121-140,1999.
[8] Bayliss J.D., Ballard D.H., RecognizingEvoked Potentials in a Virtual Environment,NIPS, pp.3-9, 1999. 137
[9] Ramoser H., Muller-Gerking J., Pfurtscheller G., Optimalspatial filtering of single trial EEGduring imagined hand movement, Rehabilitation Engineering, IEEE Transactions on NeuralSystems and Rehabilitation, Vol. 8, No. 4, pp. 441-446, Dec. 2000.
[10] http://en.wikipedia.org/wiki/Short-time_Fourier_transform
[11] Pregenzer M., Pfurtscheller G., "Frequency component selection for an EEG-based braincomputer interface (BCI)", IEEE Trans. Rehab Eng. Vol. 7, No. 3, Sep. 1999.
[12] Cover T. M., Hart P. E., Nearest neighbor pattern classification, IEEE Transactions InformationTheory, Vol. No. 13, pp. 21-27, 1967.
[13] http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/
[14] https://wiki.engr.illinois.edu/display/BIOE414/ECoG
References
http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/http://www.engadget.com/2009/05/04/mind-controlled-wheelchair-prototype-is-truly-insanely-awesome/8/10/2019 Part v P300-Based Brain Computer Interfaces
110/111
References [15] http://ajatubar.feld.cvut.cz/bisig/research
[16]http://www.bci2000.org/wiki/index.php/User_Reference:SpatialFilter#CAR
[17] http://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.png
[18] http://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpg
[19]http://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/spatialresolution/
[20] http://en.wikipedia.org/wiki/Artificial_neural_network
[21] http://www.aafnh.org/wp-content/uploads/2012/07/17089nlm_nih_gov.jpg
[22]http://en.wikipedia.org/wiki/File:MS_Demyelinisation_CD68_10xv2.jpg [23]http://upload.wikimedia.org/wikipedia/commons/thumb/7/75/MuscularD
ystrophy.png/230px-MuscularDystrophy.png
[
References
http://ajatubar.feld.cvut.cz/bisig/researchhttp://www.bci2000.org/wiki/index.php/User_Reference:SpatialFilterhttp://www.bci2000.org/wiki/index.php/User_Reference:SpatialFilterhttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/spatialresolution/http://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/spatialresolution/http://en.wikipedia.org/wiki/Artificial_neural_networkhttp://www.aafnh.org/wp-content/uploads/2012/07/17089nlm_nih_gov.jpghttp://www.aafnh.org/wp-content/uploads/2012/07/17089nlm_nih_gov.jpghttp://en.wikipedia.org/wiki/File:MS_Demyelinisation_CD68_10xv2.jpghttp://upload.wikimedia.org/wikipedia/commons/thumb/7/75/MuscularDystrophy.png/230px-MuscularDystrophy.pnghttp://upload.wikimedia.org/wikipedia/commons/thumb/7/75/MuscularDystrophy.png/230px-MuscularDystrophy.pnghttp://upload.wikimedia.org/wikipedia/commons/thumb/7/75/MuscularDystrophy.png/230px-MuscularDystrophy.pnghttp://upload.wikimedia.org/wikipedia/commons/thumb/7/75/MuscularDystrophy.png/230px-MuscularDystrophy.pnghttp://upload.wikimedia.org/wikipedia/commons/thumb/7/75/MuscularDystrophy.png/230px-MuscularDystrophy.pnghttp://upload.wikimedia.org/wikipedia/commons/thumb/7/75/MuscularDystrophy.png/230px-MuscularDystrophy.pnghttp://upload.wikimedia.org/wikipedia/commons/thumb/7/75/MuscularDystrophy.png/230px-MuscularDystrophy.pnghttp://upload.wikimedia.org/wikipedia/commons/thumb/7/75/MuscularDystrophy.png/230px-MuscularDystrophy.pnghttp://en.wikipedia.org/wiki/File:MS_Demyelinisation_CD68_10xv2.jpghttp://en.wikipedia.org/wiki/File:MS_Demyelinisation_CD68_10xv2.jpghttp://en.wikipedia.org/wiki/File:MS_Demyelinisation_CD68_10xv2.jpghttp://www.aafnh.org/wp-content/uploads/2012/07/17089nlm_nih_gov.jpghttp://www.aafnh.org/wp-content/uploads/2012/07/17089nlm_nih_gov.jpghttp://www.aafnh.org/wp-content/uploads/2012/07/17089nlm_nih_gov.jpghttp://www.aafnh.org/wp-content/uploads/2012/07/17089nlm_nih_gov.jpghttp://en.wikipedia.org/wiki/Artificial_neural_networkhttp://en.wikipedia.org/wiki/Artificial_neural_networkhttp://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/spatialresolution/http://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/spatialresolution/http://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/spatialresolution/http://micro.magnet.fsu.edu/primer/java/digitalimaging/processing/spatialresolution/http://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://phucopierservice.com/nashuatec_micro/image-features-extraction-matlab-i0.jpghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.idiap.ch/~marcel/labs/faceverif/face-verif-for-dummies/facefeature-to-dct.pnghttp://www.bci2000.org/wiki/index.php/User_Reference:SpatialFilterhttp://www.bci2000.org/wiki/index.php/User_Reference:SpatialFilterhttp://www.bci2000.org/wiki/index.php/User_Reference:SpatialFilterhttp://ajatubar.feld.cvut.cz/bisig/research8/10/2019 Part v P300-Based Brain Computer Interfaces
111/111
References [24]http://www.humanillnesses.com/original/images/hdc_0001_0002_0_i
mg0181.jpg [25] http://en.wikipedia.org/wiki/Amyotrophic_lateral_sclerosis
[26] http://en.wikipedia.org/wiki/Electroencephalography
[27]https://wiki.engr.illinois.edu/download/attachments/44733162/ecog.jpg?version=1&modificationDate=1292382236000
[28] http://www.gtec.at/Research/Projects/ALIAS [29]http://www.bci2000.org/wiki/index.php/User_Tutorial:EEG_Measure
ment_Setup
[30]http://www.theredmenmovie.com/2009/11/magnetoencephalography-meg-scanner.html
[31]http://blogs.oem.indiana.edu/scholarships/index.php/2009/10/26/neu
rons-and-electrodes/fmri_groot/ [32] Hoffman, U. Bayesian Machine Learning Applied in a Brain-Computer
Interface for Disabled Users, EPFL, 2007
http://www.humanillnesses.com/original/images/hdc_0001_0002_0_img0181.jpghttp://www.humanillnesses.com/original/images/hdc_0001_0002_0_img0181.jpghttp://en.wikipedia.org/wiki/Amyotrophic_lateral_sclerosishttp://en.wikipedia.org/wiki/Electroencephalographyhttps://wiki.engr.illinois.edu/download/attachments/44733162/ecog.jpg?version=1&modificationDate=1292382236000https://wiki.engr.illinois.edu/download/attachments/44733162/ecog.jpg?version=1&modificationDate=1292382236000http://www.gtec.at/Research/Projects/ALIAShttp://www.gtec.at/Research/Projects/ALIAShttp://www.gtec.at/Research/Projects/ALIAShttps://wiki.engr.illinois.edu/download/attachments/44733162/ecog.jpg?version=1&modificationDate=1292382236000https://wiki.engr.illinois.edu/download/attachments/44733162/ecog.jpg?version=1&modificationDate=1292382236000https://wiki.engr.illinois.edu/download/attachments/44733162/ecog.jpg?version=1&modificationDate=1292382236000https://wiki.engr.illinois.edu/download/attachments/44733162/ecog.jpg?version=1&modificationDate=1292382236000http://en.wikipedia.org/wiki/Electroencephalographyhttp://en.wikipedia.org/wiki/Electroencephalographyhttp://en.wikipedia.org/wiki/Amyotrophic_lateral_sclerosishttp://www.humanillnesses.com/original/images/hdc_0001_0002_0_img0181.jpghttp://www.humanillnesses.com/original/images/hdc_0001_0002_0_img0181.jpghttp://www.humanillnesses.com/original/images/hdc_0001_0002_0_img0181.jpgRecommended