The past decade has shown the importance of adapting spatial patterns of neural activity while decoding it in a Brain Machine Interface (BMI) framework. The common spatial patterns (CSP) algorithm tackles this problem as feature extractor in binary BMI setups in which a number of spatial projections are computed while maximizing the variance of one class and minimizing of the other. Recent advances in data acquisition systems and sensor design now make recording the neural activity of the brain with dense electrode grids a possibility. However, high density recordings also pose new challenges such as overfitting to data as the number of recording channels increases dramatically compared to the number of training trials. In this study, we tackle this problem by constructing a sparse CSP algorithm through recursive weight elimination (CSP RWE), in which the spatial projections are computed using a subset of the recording channels. The sparse projections are expected to yield increased robustness and eliminate overfitting. We show promising results towards the classification of multichannel Electrocorticogram (ECoG) and Electroencephalogram (EEG) datasets with CSP RWE for a BMI.
['Fikri Goksu', 'Firat Ince', 'Ibrahim Onaran']
Sparse common spatial patterns with recursive weight elimination