Inventive deep convolutional neural network classifier for emotion identification in accordance with EEG signals

Emotion identification is the current research concept as it obtains a significant role in interpersonal relationships and health care services. The electroencephalogram (EEG) is currently utilized in emotion identification as it captures the signals instantly from the brain and it remains the best modality. Even though a lot of work is concentrated on emotion recognition they still suffer from various issues, such as noisy and inconsistent EEG signals, high testing and training time, and low training efficiency. Hence, to mitigate these issues an optimized deep convolutional neural network (DCNN) is proposed to accurately recognize the emotions from EEG signals. The research emphasizes the implication Inventive brain optimization algorithm in spotting emotions. Further, the frequency features enhance the detection accuracy to indicate the optimal modality in emotion identification. The accuracy of Inventive DCNN model is 97.12% at 90% of training and 96.83% in accordance with K-fold analysis, which is higher when compared to existing models.