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Brain Computer Interfaces
or Krang’s Body
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What is an EEG?
An electroencephalogram is a measure of the brain's voltage fluctuations as detected from scalp electrodes.
It is an approximation of the cumulative electrical activity of neurons.
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What is it good for? Neurofeedback
treating ADHD guiding meditation
Brain Computer Interfaces People with little muscle control (i.e. not
enough control for EMG or gaze tracking) People with ALS, spinal injuries High Precision Low bandwidth (bit rate)
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EEG Background 1875 - Richard Caton discovered electrical
properties of exposed cerebral hemispheres of rabbits and monkeys.
1924 - German Psychiatrist Hans Berger discovered alpha waves in humans and invented the term “electroencephalogram”
1950s - Walter Grey Walter developed “EEG topography” - mapping electrical activity of the brain.
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Physical Mechanisms
EEGs require electrodes attached to the scalp with sticky gel
Require physical connection to the machine
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Electrode Placement Standard “10-20 System” Spaced apart 10-20% Letter for region
F - Frontal Lobe T - Temporal Lobe C - Center O - Occipital Lobe
Number for exact position Odd numbers - left Even numbers - right
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Electrode Placement
A more detailed view:
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Brain “Features”
User must be able to control the output: use a feature of the continuous EEG output
that the user can reliably modify (waves), or evoke an EEG response with an external
stimulus (evoked potential)
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Continuous Brain Waves Generally grouped by frequency: (amplitudes are about 100µV max)
Type Frequency Location Use
Delta <4 Hz everywhere occur during sleep, coma
Theta 4-7 Hz temporal and parietal correlated with emotional stress(frustration & disappointment)
Alpha 8-12 Hz occipital and parietal reduce amplitude with sensory stimulation or mental imagery
Beta 12-36 Hz parietal and frontal can increase amplitude during intense mental activity
Mu 9-11 Hz frontal (motor cortex) diminishes with movement or intention of movement
Lambda sharp, jagged
occipital correlated with visual attention
Vertex higher incidence in patients with epilepsy or encephalopathy
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Brain Waves Transformations
wave-form averaging over several trials
auto-adjustment with a known signal
Fourier transforms to detect relative amplitude at different frequencies
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Alpha and Beta Waves
Studied since 1920s Found in Parietal and Frontal Cortex Relaxed - Alpha has high amplitude Excited - Beta has high amplitude So, Relaxed -> Excited
means Alpha -> Beta
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Mu Waves
Studied since 1930s Found in Motor Cortex Amplitude suppressed by Physical
Movements, or intent to move physically
(Wolpaw, et al 1991) trained subjects to control the mu rhythm by visualizing motor tasks to move a cursor up and down (1D)
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Mu Waves
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Mu and Beta Waves
(Wolpaw and McFarland 2004) used a linear combination of Mu and Beta waves to control a 2D cursor.
Weights were learned from the users in real time.
Cursor moved every 50ms (20 Hz) 92% “hit rate” in average 1.9 sec
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Mu and Beta Waves
Movie!
QuickTime™ and aSorenson Video decompressorare needed to see this picture.
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Mu and Beta Waves How do you handle
more complex tasks?
Finite Automata, such as this from (Millán et al, 2004)
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P300 (Evoked Potentials) occurs in response to a significant but low-probability event 300 milliseconds after the onset of the target stimulus found in 1965 by (Sutton et al., 1965; Walter, 1965) focus specific
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P300 Experiments
(Farwell and Donchin 1988) 95% accuracy at 1 character per 26s
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P300 (Evoked Potentials)
(Polikoff, et al 1995) allowed users to control a cursor by flashing control points in 4 different directions
Each sample took 4 seconds
Threw out samples masked by muscle movements (such as blinks)
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(Polikoff, et al 1995) Results
50% accuracy at ~1/4 Hz 80% accuracy at ~1/30 Hz
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VEP - Visual Evoked Potential
Detects changes in the visual cortex Similar in use to P300 Close to the scalp
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Model Generalization (time)
EEG models so far haven’t adjusted to fit the changing nature of the user.
(Curran et al 2004) have proposed using Adaptive Filtering algorithms to deal with this.
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Model Generalization (users) Many manual adjustments still must be
made for each person (such as EEG placement)
Currently, users have to adapt to the system rather than the system adapting to the users.
Current techniques learn a separate model for each user.
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Model Generalization (users) (Müller 2004) applied typical machine
learning techniques to reduce the need for training data.
Support Vector Machines (SVM) and Regularized Linear Discriminant Analysis (RLDA)
This is only the beginning of applying machine learning to BCIs!
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BCI Examples - Communication
Farwell and Donchin (1988) allowed the user to select a command by looking for P300 signals when the desired command flashed
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BCI Examples - Prostheses
(Wolpaw and McFarland 2004) allowed a user to move a cursor around a 2 dimensional screen
(Millán, et al. 2004) allowed a user to move a robot around the room.
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BCI Examples - Music
1987 - Lusted and Knapp demonstrated an EEG controlling a music synthesizer in real time.
Atau Tanaka (Stanford Center for Computer Research in Music and Acoustics) uses it in performances to switch synthesizer functions while generating sound using EMG.
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In Review… Brain Computer Interfaces
Allow those with poor muscle control to communicate and control physical devices
High Precision (can be used reliably)
Requires somewhat invasive sensors Requires extensive training (poor generalization) Low bandwidth (today 24 bits/minute, or at most
5 characters/minute)
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Future Work
Improving physical methods for gathering EEGs
Improving generalization
Improving knowledge of how to interpret waves (not just the “new phrenology”)
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References http://www.cs.man.ac.uk/aig/staff/toby/research/bci/
richard.seabrook.brain.computer.interface.txt http://www.icad.org/websiteV2.0/Conferences/ICAD2004/concert_call.htm http://faculty.washington.edu/chudler/1020.html http://www.biocontrol.com/eeg.html http://www.asel.udel.edu/speech/Spch_proc/eeg.html
Toward a P300-based Computer InterfaceJames B. Polikoff, H. Timothy Bunnell, & Winslow J. Borkowski Jr.Applied Science and Engineering LaboratoriesAlfred I. Dupont Institute
Various papers from PASCAL 2004
Original Paper on Evoked Potential:https://access.web.cmu.edu/http://www.jstor.org/cgi-bin/jstor/viewitem/00368075/ap003886/00a00500/0?searchUrl=http%3a//www.jstor.org/search/Results%3fQuery%3dEvoked-Potential%2bPotential%2bCorrelates%2bof%2bStimulus%2bUncertainty%26hp%3d25%26so%3dnull%26si%3d1%26mo%3dbs&frame=noframe&dpi=3¤tResult=00368075%2bap003886%2b00a00500%2b0%2c07&[email protected]/01cce4403532f102af429e95&backcontext=page
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Invasive BCIs Have traditionally provided much finer control
than non-invasive EEGs (no longer true?)
May have ethical/practical issues
(Chapin et al. 1999) trained rats to control a “robot arm” to fetch water
(Wessberg et al. 2000) allowed primates to accurately control a robot arm in 3 dimensions in real time.