A hybrid sequential forward channel selection method for enhancing EEG-Based emotion recognition

In recent times, EEG-based emotion recognition has gained significant attention in affective computing. One of the major challenges in designing an efficient EEG-based emotion-recognition framework lies in handling the high dimensionality of the data recorded from a large number of EEG electrodes. This paper develops a hybrid sequential forward channel selection (HSFCSER) based emotion-recognition method, which aims to identify the most relevant subset of EEG channels and thus reduces the dimensionality of data. In HSFCSER, the Fisher score is used as the filter method, and the wrapper method includes Support Vector Machine (SVM). Wavelet-based features are extracted from the selected optimal EEG channels, followed by the feature selection using a multi-objective genetic algorithm. The proposed method is evaluated on the DEAP database. The methodology identifies 1) two classes of emotions viz. Low/High Valence with an average accuracy of 91.25 +/- 5.48% for subject-dependent and 89.38 +/- 4.76% for subject-independent, and Low/High Arousal with an average accuracy of 89.92 +/- 5.81% for subject-dependent and 86.32 +/- 5.34% for subject-independent, and 2) four classes of emotions viz. High Valence-Low Arousal (HVLA), High Valence-High Arousal, Low Valence-Low Arousal, and Low Valence-High Arousal with 75.94 +/- 11.07% for subject-dependent and 73.13 +/- 8.17% for subject-independent. The efficient electrode positions for emotion recognition are also depicted. The reported results are better compared to the existing results in the literature. The source code of the proposed work is made available at https://github.com/shyammarjit/HSFCS.