Movement-Related Potential

• MRPs are small and slow potential drift before a voluntary movement 
• the potentials are different between left and right arm, so it's easy to distinguish left vs right movement intention.
• harder detection than alpha and beta rhythms
• however, can be more specific to supplementary and primary motor cortical areas

Experimentation

• Hiraiwa (1990)- backpropagation neural network to test voluntary utterance of vowels (a, e, i, o, u) and moving the joystick in 4 directions
• 53% patterns correctly classified for vowels, 96% patterns recognized by directions
• vowels require a higher level of cognitive activity, hence a harder pattern recognition
• asyncrhonous switch- BCI can detect from a user is idle to active control state
• low-frequency AS- record 1-4 Hz range

More Experimentation!

• Shenoy and Rao (2005) - dynamic Bayesian network
• probability distributions over brain and body-states during planning + execution
• continuous tracking, allows generation of control signals
• MRPs can estimate movement

Stimulus-Evoked Potentials

• stereotypical EEG responses but by specific stimuli
• rare stimulus triggers a P300 response (300 ms)
• The P300 response stimulus itself is unpredictable, but is always 300 ms after the stimulus.
• Generally in the parietal lobe, but sometimes certain components in the temporal and frontal lobes as well.
• oddball paradigm- spelling a word using a matrix of letters and are flashed in random orders.

Every time a letter flashed, the brain will generate a P300 reponse.

Steady State Visually Evoked Potential

• detecting SSVEP caused by a continuously fluctuating stimuli (hence the name steady state)
• Ex: decoding one of two possible choices, these two choices can be represented by a light at different frequency. Then we can see the difference in stimuli.

• Each button was lit up at a different frequency ranging from 6-14 Hz. Therefore, decoding SSVEP is possible.
• 8 out of 13 subjects used SSVEP subject to type desire phone number. Avg: 27.15 bpm for ITR.

Auditory Evoked Potentials

• Donchin and colleagues- used P300 in spoken commands (yes, no, pass, and end), achieved accuracy in 63%-80% in 3 ALS pateitns
• Hill and colleagues (2005)- ICA with support vector machines. 50 ms square-wave beeps of different frequencies either on the left or right ear. Subjects count the amount of beep on the left or right side.

More on Auditory Evoked Potentials

• Furdea and colleagues: letters in matrix coded with acoustically presented numbers
• Less accurate than a visual BCI- Halder (2010) found that ITR has an avg of 2.46 bpm and accuracy around 78.5%.
• Only reliable to patients who have lost all motor function and have short attention spans.

BCIs based on Cognitive Tasks

• requires higher levels of thinking, but each cognitive task can be mapped to a control signal
• can discriminate between activity patterns of different tasks
• Anderson (1996)- (1) relax, (2) letter composition (3) math, (4) visual counting, (5) geometric figure
• EEG recorded from 10-20 system. Autoregressive model with two/three-layer backpropagation neural networks

BCIs based on Cognitive Tasks

• average accuracy ranged from 71% to 38%
• Galan (2008) - 3 mental tasks to operate simulated wheelchair along specified path. (1) mentally search for words, (2) relaxing while fixating on the center of screen, (3) motor imagery of left hand
• used LDA classifier. 100% and 80% (different subjects) of the final goals.