Multi-way feature selection for ECoG-based Brain-Computer Interface

Abstract The paper addresses the problem of designing Brain-Computer Interfaces. It investigates feature selection methods in regression, applied to ECoG-based motion decoding. The problem is to predict hand trajectories from the voltage time series of cortical activity. A special characteristic of this problem is the inherently multi-way structure of feature description. The feature description resides in spatial-spectra-temporal domain and includes the voltage time series and their spectral representation. Since electrocorticographic data is highly correlated in temporal, spectral and spatial domains, redundancy of the feature space as well as its dimensionality become a major obstacle for robust solution of the regression problem both in multi-way and flat cases. Feature selection reduces dimensionality and increases model robustness. It plays the crucial role in obtaining adequate predictions. The main contribution of this paper is the following. We propose a filtering feature selection for multi-way data. The proposed method extends quadratic programming feature selection (QPFS) approach. QPFS selects a subset of features by solving a quadratic problem. It incorporates estimates of similarity between features and their relevance to the regression problem. QPFS offers an effective way to leverage similarities between features and their importance. Our modification allows to apply QPFS to multi-way data. By taking the multi-way structure of features into account, the proposed modification reduces computational costs of optimization problem in QPFS. Experimental outcomes demonstrate that the proposed modification improves prediction quality of resultant models. The proposed method is model-free and provides interpretable results, which makes it relevant for knowledge extraction and domain analysis.