Emotion recognition using brian signals based on time-frequency analysis and supervised learning algorithm
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Over the years many groundbreaking research involving Brain Computer Interface (BCI), has been conducted in order to study emotions of human beings, to build better-quality human-machine interaction systems. On the other hand, it is also quite possible to log the activities of brain in real-time and then use it to distinguish patterns related to emotional status. BCI creates a mutual understanding between the users and its environment for measuring emotions through brain activities. Electroencephalogram (EEG) is a well-accepted method to measure the brain activities. Once the system records the EEG signals, we analyze and process these activities to distinguish different emotions. Previous researchers used standard and pre-defined methods of signal processing area with fewer channels and participations to record their EEG signals. In this thesis, a novel method was proposed that extracted features from EEG signals based on time-frequencies analysis and supervised learning algorithm was used to classify different emotional states. Our proposed method provides 92.36% accuracy by using a benchmark dataset, where 32 participants were used to carry out this experiment.