Hybrid feature integration model and adaptive transformer approach for emotion recognition with EEG signals

Nowadays, electroencephalogram (EEG) signals are the major part of recognizing emotions as the signals contain more brain-related information. Several traditional recognition methodologies have been developed, but still, exist with certain challenges that degrade and mislead the recognition process. Some models are facing issues based on insignificant features, poor generalization capability, and computational burden. To alleviate such issues, an effective emotion recognition model is proposed using a heuristic-aided transformer-based approach. Initially, the input EEG signals are gathered and decomposed via the 5-level Discrete Wavelet Transform (DWT). Then, the decomposed signals are fed to the feature extraction process, where the feature extraction is carried out by using Logistic Regression (LR) approach. From the decomposed signals, the Logistic Regression (LR) is used for getting the noteworthy features and then, the same decomposed signals are fed to the optimal weighted feature selection, which is carried out using Adaptive Bald Eagle Search Optimization (ABESO) algorithm. Subsequently, the hybrid weighted feature selection approach is introduced for getting the weighted features, where the weight is optimized with the adoption of the ABESO algorithm. At last, the selected hybrid weighted features are sent to the recognition phase, where the Optimized Block Recurrent Transformer (OBRT), where performance gets enhanced by tuning the parameter with the developed ABESO algorithm. The accuracy and precision rate of the designed approach are attained as 96% and 94%. The performance of the model is assessed and the findings have shown that better performance that makes the system more feasible to recognize human emotions.