Comparative Analysis of Neural Decoding Algorithms for Brain-Machine Interfaces

Accurate neural decoding of brain dynamics remains an open challenge in brain-machine interfaces. While various signal processing, feature extraction, and classification algorithms have been proposed, a systematic comparison of these is lacking. Accordingly, here we conducted one of the largest comparative studies to evaluate different combinations of state-of-the-art algorithms for motor neural decoding in order to find the optimal combination. We studied three signal processing methods (i.e., artifact subspace reconstruction, surface Laplacian filtering, and data normalization), four feature extractors (i.e., common spatial patterns, independent component analysis, short-time Fourier transform, and no feature extraction), and four machine learning classifiers (i.e., support vector machine, linear discriminant analysis, convolutional neural networks, and long short-term memory networks). Using a large-scale EEG dataset, we optimized each combination for individual subjects (i.e., resulting in 672 total experiments) and evaluated performance based on classification accuracy. We also compared the computational and memory storage requirements, which are important for real-time embedded computing. Our comparative analysis provides novel insights that can help inform the design of next-generation neural decoding algorithms for brain-machine interfaces.