Narrow Results

Utilizing electroencephalography (EEG) for emotion recognition enables the direct collection of physiological signals, thereby circumventing potential deception associated with facial expressions and language cues. Concurrently, there has been a widespread adoption of deep learning techniques in this domain, aimed at achieving enhanced results through model and parameter adjustments. An innovative approach is introduced by incorporating the Fast Fourier Transform (FFT) for data preprocessing in conjunction with the CBi-GRU model, merging the Convolutional Neural Network (CNN) and the Bidirectional Gated Recurrent Unit (BiGRU). Comparative analysis underscores the significance of exploring the intrinsic characteristics of the data itself: the application of FFT not only substantially reduces processing time but also enhances model performance. The integration of CBiGRU and FFT leverages the strengths of both CNN and BiGRU methods, resulting in increased accuracy and rapid convergence.