Sparse Logistic Regression With L 1/2 Penalty for Emotion Recognition in Electroencephalography Classification.

Emotion recognition based on electroencephalography (EEG) signals is a current focus in brain-computer interface research. However, the classification of EEG is difficult owing to large amounts of data and high levels of noise. Therefore, it is important to determine how to effectively extract features that include important information. Regularization, one of the effective methods for EEG signal processing, can effectively extract important features from the signal and has potential applications in EEG emotion recognition. Currently, the most popular regularization technique is Lasso (L-1) and Ridge Regression (L-2). In recent years, researchers have proposed many other regularization terms. In theory,L-q-type regularization has a lowerqvalue, which means that it can be used to find solutions with better sparsity.L(1/2)regularization is ofL(q)type (0 更多