Epileptic Signal Recognition Based on Non-sparse Multiple Kernel Learning with Label Relaxation

Electroencephalogram (EEG) signals typically contain multiple types of features, including temporal, frequency, and time-frequency domains. Effectively integrating these diverse features has become a key challenge in tasks such as EEG-based disease diagnosis. Non-sparse Multiple Kernel Learning (MKL), which leverages the information from multiple kernels, has been widely and successfully applied in multi-modal feature fusion. However, most existing models rely on the strong assumption that a non-sparse combination of multiple kernels can infinitely approximate a strict binary label matrix, which overly restricts the freedom of label fitting. To address this limitation, this paper proposes a novel non-sparse MKL model for multi-modal feature fusion. Specifically, we introduce a label relaxation strategy to relax the binary label matrix, thereby enhancing the flexibility of label fitting. Meanwhile, a regularization term based on manifold learning is constructed to mitigate the potential risk of overfitting caused by label relaxation. Experimental results on a benchmark dataset containing EEG recordings from 23 patients at Boston Children's Hospital demonstrate the superior performance and promising application prospects of the proposed model.