Multi-View Manifold-Adaptive Kernel Regression for Speech Classification From EEG Signals

Decoding speech intentions from electroencephalogram (EEG) data is the primary task in speech brain-computer interface (BCI) systems, which remains challenging due to the unclear discriminative task-aware features, and underlying nonlinear properties besides the well-known low signal-to-noise ratio of EEG data. Existing approaches typically rely either on single-domain features or performing feature learning by deep neural networks; therefore, they either fail to capture comprehensive signal patterns, or typically require large-sized EEG data to fit the parameter spaces and often have limited interpretability. To address these limitations, we propose a Multi-view Manifold-Adaptive Kernel Regression (MMKR) model for speech recognition from EEG signals in this paper. By treating temporal, spectral, and statistical EEG representations as complementary feature views, view-specific manifold-adaptive kernels are constructed in MMKR to incorporate local graph structure into kernel similarity; besides, a data-driven adaptive view weighting mechanism is used to characterize their contributions. We evaluate MMKR on both overt and imagined speech EEG datasets and the results demonstrate that MMKR achieves superior classification accuracy and robustness compared to some representative single-view, multi-view, and kernel-based baselines. Moreover, analysis on the local manifold-modulated kernel matrix and the learned view contributions are provided.