Combined Weighted Feature Correlation Approach for Enhanced EEG-Based Emotion Recognition Across Diverse Datasets

Emotion recognition using EEG Signals is an active area of research in affective computing and neuroscience, aiding scientists into their quest to understand the brain processes involved in emotions. In contrast to the existing works which focused on linear EEG features, the present work concentrates on non-linear features in order to better characterize the finer changes of emotional responding. Inspired by this, in current paper, proposed method is called Combined Weighted Feature Correlation (CWFC) to investigate the effect of non-linear components on the performance of emotion detection. As a first step, the EEG data is collected and pre-processed using bandpass filters to separate the frequency bands such as beta, alpha, gamma, delta, and theta. To detect intricate emotional patterns, features extraction is performed by Independent Component Analysis (ICA), DWT (Discrete Wavelet Transform) and FFT (Fast Fourier Transform). These features can be built as combined features into the CWFCC model to improve its power as well as its overall predictive accuracy. Then apply a Random Forest classifier to see how significant are these combined features. Proposed work integrates GAN data augmentation after optimal feature selection (with an emphasis on LSTM). On the DEAP dataset, this augmentation results in astonishing 88% valence accuracy and 86% arousal accuracy, which improves the recognition accuracy of emotion remarkably. Proposed model achieved valence and arousal accuracy of 62% and 65%, respectively, on the early DEAP without GAN data augmentation. Moreover, this model can distinguish between SEED dataset during Neutral emotional states, Negative, and Positive with an average accuracy of 89%. The proposed paper proclaims the superior performance of CWFC model with GAN based data augmentation on EEG for emotion recognition over different datasets through in-depth analysis and comparative study.