Decoding of finger activation from ECoG data: a comparative study

Motor Brain-Computer Interfaces (BCIs) are systems that allow severely motor-impaired patients to use their brain activity to interact with their environment. Electrocorticography (ECoG) arrays may be profitably used to develop safe and chronic motor BCI systems. BCI signal processing pipelines generally include neuronal signal pre-processing, feature extraction and classification/regression. The article presents a comparative study addressing the problem of neural feature classification in asynchronous multi-limb ECoG-driven BCIs. Several conventional classifiers often reported in the BCI literature were coupled with two preprocessing techniques and with a conventional feature extraction approach. They were compared to artificial neural network (ANN) end-to-end classifiers which mimic conventional BCI signal processing pipelines. Different initializations of ANNs were particularly studied. The comparison study was carried out using publicly available datasets (BCI competition IV).