Movie Oriented Positive Negative Emotion Classification from EEG Signal using Wavelet transformation and Machine learning Approaches

Electroencephalography (EEG) sensor plays an important role in developing brain-computer interfaces (BCI) to enhance human-computer interaction (HCI). Nowadays, various types of research works are performed to develop EEG-based HCI systems for controlling and monitoring systems. However, researchers are still facing challenges in developing this system due to noise from the physiological and internal and external artefacts. This study proposed a method to find useful electrodes and extract potential information from the brain nerves for the classification of positive or negative emotions. The collected emotion's EEG signal is recorded using 14 electrodes from the 30-younger people. Two movies were used for positive and negative emotions. In the proposed method, we first extracted the five bands wavelet transform from the EEG and then calculated the standard deviation (SD), average power (AVP) and mean absolute value (MAV) of the five bands wavelet information. Finally, we applied an extra tree classifier (ETC), random forest (RF), and support vector machine (SVM) to classify the emotion based on the feature vector. Among three classifiers ETC achieved higher performance accuracy in F3, FC5, T8, FC6, F8, and AF4 electrodes. This indicates that the F3, FC5, T8, FC6, F8, and AF4 electrodes carry potential information in positive-negative emotion classification.