A Unified Biologically-Inspired Prediction Framework for Classification of Movement-Related Potentials Based on a Logistic Regression Model

The classifications for movement-related potentials (MRPs) are used to provide control signals for many motor-related brain–computer interfaces (BCIs). A discriminative spatial pattern (DSP) algorithm has been shown to be effective for extracting MRPs. However, the spatial filtering and feature extraction of DSP are not exactly consistent. In addition, DSP filtering and subsequent classifiers, such as support vector machines, are optimized toward different goals. These two drawbacks may degrade overall classification performance. In this paper, inspired by the multilayer perceptron, we propose a hierarchical framework based on a logistic regression model. Our framework directly extracts the distance between each pair of time series as a feature, and unifies spatial filtering and classification under a regularized empirical risk minimization problem. Experimental results from three BCI datasets recorded from five subjects demonstrate that our method can make more accurate classifications.

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