Decoding Finger Flexion from Band-Specific ECoG Signals in Humans - Liang, Nanying; Bougrain, Laurent (Frontiers in Neuroscience, 2012)
This article presents the method that won the BCI competition IV addressed to the pre- diction of the finger flexion from ECoG signals. ECoG-based BCIs have recently drawn the attention from the community. Indeed, ECoG can provide a higher spatial resolution, a higher signal quality and is more suitable for long-term use than classical EEG recordings. These characteristics allow to decode precise brain activities and to realize efficient ECoG-based neu- roprostheses. Signal processing is a very important task in BCIs research for translating brain signals into commands. Here, we present a linear regression method based on the amplitude modulation of band-specific ECoG including a short term memory for individual finger flexion prediction. The effectiveness of the method was proven by achieving the highest value of corre- lation coefficient between the predicted and recorded finger flexion values on data set 4 during the BCI competition IV.