Band-specific features improve Finger Flexion Prediction from ECoG

Laurent Bougrain (1) , Nanying Liang (1)

1 CORTEX - Neuromimetic intelligence

Abstract—ECoG-based BCIs attract intensive attention recently. ECoG can provide a higher spatial resolution and signal quality compare to EEG recordings. These characteristics make possible to localize the source of neural signals precisely with respect to certain brain activities such that ECoG-based BCIs may realize a complex and apt neuroprosthesis. Signal processing is a very important task in the BCIs research for translating the brain signals into commands for a computer application or a neuroprosthesis. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including tap delay for individual finger flexion prediction. We especially study the influence of the frequency band decomposition on the prediction. An efficient feature selection can reduce the number of features by a factor greater than 10 without a strong impact on the prediction. According to the experimental results, the gamm band (60-100Hz) seems the carry more useful information than the others. This method won the BCI competition IV dedicated to this mapping.

Domaines: Interface homme-machine [cs.HC]

Laurent Bougrain, Nanying Liang. Band-specific features improve Finger Flexion Prediction from ECoG. Jornadas Argentinas sobre Interfaces Cerebro Computadora - JAICC 2009, Apr 2009, Paranà, Argentina. ⟨inria-00408675⟩