EEG-BASED EPILEPSY IDENTIFICATION USING MULTIPLE FEATURE SPACES CONSISTENT FUSION WITH LABEL RELAXATION

In recent years, the application of machine learning techniques for identifying epileptic electroencephalogram (EEG) signals has become increasingly prevalent. In this study, we propose a method called Multi-View Learning and Regularized Label Relaxation Linear Regression (MVRLR) for the recognition of epileptic EEG signals. To be specific, first, we generate multi-view data by employing three different feature extraction methods to capture the EEG features from different perspectives. Then, in the linear regression framework, we introduce a nonnegative label relaxation matrix to transform the strict binary label matrix into a relaxed variable matrix, allowing for more flexible label fitting and expanding the boundaries between different classes. Additionally, we utilize manifold learning to construct a class tightness graph, which serves as a regularization term to prevent overfitting. Finally, we employ Shannon entropy to quantify the overall uncertainty within the probability distribution. To validate the effectiveness of the proposed method, we conduct experiments using a multi-view epileptic EEG dataset and compare it with several other classification algorithms. The extensive experimental results demonstrate that the proposed MVRLR method achieves higher classification accuracy. By applying the proposed method, we can more accurately identify epileptic EEG signals, providing robust support for early diagnosis and personalized treatment of epilepsy.