A Comparative Analysis of Machine and Deep Learning Techniques for EEG Evoked Emotion Classification

In the field of Brain Computer Interface (BCI), applications in real life like emotion recognition from recorded electrical activity from brain have become famous topic of research nowadays. Learning successful representations of consistent performances from electroencephalogram (EEG) signals is one of the difficulties in recognition tasks. This research is intended to propose a discriminative and efficacious classification approach for categorizing brain signals patterns depending on the level of activity or frequency for recognizing emotion states. The paper classifies three possible emotion states such as neutral, negative and positive emotional states by operating the Muse EEG headset with four electrode channels (AF7, AF8, TP9, TP10) captured while a subject was watching an emotional video clip on screen. In this experiment various statistical, linear and non linear features are extracted and then Machine and Deep learning based models are implemented to classify the EEG evoked emotions. In this work, a brief comparison study is carried out between the various implemented models with respect to train and test accuracy, recall, precision and F1 score. The highest average accuracy achieved are 98.13% for the proposed Convolutional Neural Network (CNN) model among all implemented Deep learning models and 98.12% for Random forest among the various machine learning techniques implemented. The proposed Long Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) model with 97.42 and 97.19% and Decision tree and Support Vector Machine with 96.25 and 96.42% have also provided comparable results for emotion classification respectively.