EEG-Based Human Emotion Prediction Using an LSTM Model

It is deemed essential to identify and classify human emotions via deep learning with computers. Therefore, electroencephalogram (EEG) is extensively used as a physiological source of emotions. In this paper, a long short-term memory (LSTM) model is proposed for classification of positive, neutral, and negative emotions. This model is applied to a dataset that includes three classes of emotions with a total of 2,100 EEG samples from two subjects. The proposed model is trained using TensorFlow library with a tuning method to achieve maximum accuracy for emotion prediction. To appraise the model performance, receiver operating characteristic (ROC) curve is utilized. Experimental results demonstrate that the proposed model attains a high performance in the classification of human emotions. Furthermore, the proposed LSTM model has a testing accuracy of 98.13%, a macro average precision of 98.14%, and the area under the ROC curve for this model is 100%.