Brain_Computer_Interfaces ppt

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Brain Computer Interfaces:

Brain Computer Interfaces or Krang’s Body

What is an EEG?:

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.

What is it good for?:

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)

EEG Background:

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.

Physical Mechanisms:

Physical Mechanisms EEGs require electrodes attached to the scalp with sticky gel Require physical connection to the machine

Electrode Placement:

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

Electrode Placement:

Electrode Placement A more detailed view:

Brain “Features”:

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)

Continuous Brain Waves:

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

Brain Waves Transformations:

Brain Waves Transformations wave-form averaging over several trials auto-adjustment with a known signal Fourier transforms to detect relative amplitude at different frequencies

Alpha and Beta Waves:

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

Mu Waves:

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)

Mu Waves:

Mu Waves

Mu and Beta Waves:

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

Mu and Beta Waves:

Mu and Beta Waves Movie!

Mu and Beta Waves:

Mu and Beta Waves How do you handle more complex tasks? Finite Automata, such as this from (Millán et al, 2004)

P300 (Evoked Potentials):

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

P300 Experiments:

P300 Experiments (Farwell and Donchin 1988) 95% accuracy at 1 character per 26s

P300 (Evoked Potentials):

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)

(Polikoff, et al 1995) Results:

(Polikoff, et al 1995) Results 50% accuracy at ~1/4 Hz 80% accuracy at ~1/30 Hz

VEP - Visual Evoked Potential:

VEP - Visual Evoked Potential Detects changes in the visual cortex Similar in use to P300 Close to the scalp

Model Generalization (time):

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.

Model Generalization (users):

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.

Model Generalization (users):

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!

BCI Examples - Communication:

BCI Examples - Communication Farwell and Donchin (1988) allowed the user to select a command by looking for P300 signals when the desired command flashed

BCI Examples - Prostheses:

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.

BCI Examples - Music:

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.

In Review…:

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)

Future Work:

Future Work Improving physical methods for gathering EEGs Improving generalization Improving knowledge of how to interpret waves (not just the “new phrenology”)

References:

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 Interface James B. Polikoff, H. Timothy Bunnell, & Winslow J. Borkowski Jr. Applied Science and Engineering Laboratories Alfred 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&currentResult=00368075%2bap003886%2b00a00500%2b0%2c07&userID=80020b32@cmu.edu/01cce4403532f102af429e95&backcontext=page

Invasive BCIs:

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.

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