ROBERTS MENCIS Predicting finger flexion from electrocorticography (ECoG) data
BCI competition BCI competition IV, Berlin 2008 Subjects – epilepsy patients ECoG electrode grid implanted Dataglove from 5DT
Goals of project Understanding neural basis of finger movement In-depth analysis of data Prediction model based on data analysis Better or comparable result with current winners I place 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 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 correlation between ECoG and finger flexion data
Data analysis Activity in frequency range 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 Subject #2, finger #1, channel #24, best 20 frequencies, correlation
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!