ROBERTS MENCIS

Predicting finger flexion from electrocorticography

(ECoG) data

BCI competition

BCI competition IV, Berlin 2008Subjects – epilepsy patientsECoG electrode grid implantedDataglove from 5DT

Goals of project

Understanding neural basis of finger movement

In-depth analysis of dataPrediction model based on data analysis

Better or comparable result with current winners I place - 0.46 II place – 0.42 III place – 0.27

Experimental setup

3 subjects Each experiment – 10 minutes 2 seconds cue, 2 seconds rest ECoG data from 48-62 channels Finger flexion data, 5 channels Sampling rate 1000 Hz

Neuroscience of finger movement

Brodmann area 4 (primary motor cortex)

Fingers – overlapping areas with hotspot for each finger, somatotopic arrangement

Cora-and-surround organisation, typical movements together

Small distance between neural hotspots (few mm)

Data analysis

For most subjects&fingers at least on channel with 0.3-0.4 correlation between ECoG and finger flexion data

Data analysis

Activity in frequency range 60-200 Hz corresponds to finger flexion (for some subjects&fingers)

Subject #2, finger #1, channel #24, window size 1000 ms

Data analysis

Subject #2, finger #1, channel #24, frequency 110 Hz, correlation 0.4058

Subject #2, finger #1, channel #24, best 20 frequencies, correlation 0.6869

Prediction model

For each subject and finger – find best channel-frequency pairs with highest correlation between ECoG and finger flexion training data

Determine top N channel-frequency pairs with highest scores whose combination gives best correlation on training data

Use those channel-frequency pairs to predict finger flexion from test ECoG data

Smooth predicted finger flexion data (moving average)

Top channel-frequency pairs:

Way forward…

That could be done to improve results? More advanced techniques for feature selection Different machine learning algorithms Making use of finger flexion data structure (differences

between cue-rest phase; fact that generally only one finger is flexed simultaneously etc.)

More time and effort…THANK YOU FOR ATTENTION!