A novel method for EEG-based motor imagery classification using feature fusion

This paper introduces a multi-scale feature fusion framework for EEG-based motor imagery (MI) classification, designed to leverage the spectral-temporal-spatial structure of EEG data, its nonlinear intrinsic characteristics, and convolutional features. Several proposed feature fusion models surpass current state-of-the-art classification systems for MI tasks. A support vector machine (SVM) model achieves an accuracy of 86.92% on the BCIC-IV-2a dataset. To mitigate redundancy, the proposed models incorporate dimensionality reduction via factor analysis (FA) and channel selection using common spatial pattern (CSP). Selecting 12 channels yields superior classification performance compared to using all 22or only 8 selected channels, achieving an accuracy of 88.17%.