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Umar Farooq

The Berlin Brain-Computer Interface: Machine Learning-Based Detection of User Specific Brain States. Berlin Brain Computer Interface. Umar Farooq. Lateralized Readiness Potential :. Negative Shift of the Brain Potential contralateral to the intention of hand movement. Advantages

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Umar Farooq

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  1. The Berlin Brain-Computer Interface: Machine Learning-Based Detection of User Specific Brain States Berlin Brain Computer Interface Umar Farooq

  2. Lateralized Readiness Potential : Negative Shift of the Brain Potential contralateral to the intention of hand movement • Advantages • Early Distinction of left and right hand movements • Refractory period is small enough to offer high speed commands • Disadvantages • Doesn’t last long, persistence is small • For patients, with long time disability they loose the ability to generate readiness potential • Classification resolution is small

  3. Subject’s Profile • 6 Subjects ( all male; age 27 – 46 years): • 2 had one session experience with previous BBCI setup • 1 had one session experience with current BBCI setup • 2 had 4 sessions experience with current BBCI setup • 1 subject had no prior experience with any BCI setup * 1 session means 25 trials

  4. To ensure only EEG based feedback • In addition to EEG, EMG ( at both forearms and right leg )and EOG ( for both horizontal and vertical eye movement) were recorded to ensure that they don’t offer any influence on generating feedback.

  5. Training Sessions • By training we mean Machine Learning, not Subject Learning Right Hand (R) Left Hand (L) Highlight time: 3.5 sec Highlight Interval time :1.75 to 2.25sec Right Feet (F) 3 subjects did 3 sessions each Other 3 got training only once

  6. Only Two classes are chosen that gave best discrimination in order to train a binary classifier Topographic display of the energy in specified frequency band Darker Shades indicate lower energy resp. ERD

  7. Feedback Sessions • 1D ‘absolute’ Cursor Control Representing Success of trials Display Refreshing Rate: 25fps With every new frame at t0, the cursor is updated to a new position (pt0,0) according to the classifier output 3 cm 15 cm 20 cm For the purpose of hint to the subject 3 cm Blue represents the target

  8. Feedback Sessions • 1D ‘relative’ Cursor Control Display Refreshing Rate: 25fps With every new frame at t0, position of cursor pt0 is old position pt0-1, shifted by an amount proportional to the classifier output Difference is that now we are controlling the direction and speed for the cursor position rather than the absolute position of cursor

  9. Basket Game Success and Failures 1200 to 3000 ms Smaller than the centre one as knack is easier at sides

  10. BCI control on x axis Time on y axis Erroneous trials are represented by dotted lines Left Trials Right Trials Erroneous Trials

  11. Information Transfer Rate (ITR) ------- bits per minute Cursor rate control

  12. Mental Typewriter Not based on EVOKED POTENTIAL Based on Right hand and Right Foot Movements • Imagining the right hand, turns the arrow clockwise • By Imagining the right foot movement, rotation stops and arrow starts extending • If this imagination is performed in longer period the arrow touches the hexagon and thereby selects it Average Speed 7.6 char/min including correction of all errors occurred during typing

  13. Using Multiple Features Improvement: 25% to 50% reduction of error rate

  14. LRP and ERD are independent

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