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!