The proposed non-sparse MKL with label relaxation improved epileptic EEG signal recognition performance on a 23-patient benchmark dataset from Boston Children's Hospital.
A novel non-sparse Multiple Kernel Learning model with label relaxation improves the recognition of epileptic signals from EEG data.
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.
Fu et al. (2026) studied this question. The proposed non-sparse MKL with label relaxation improved epileptic EEG signal recognition performance on a 23-patient benchmark dataset from Boston Children's Hospital.