Emotion Recognition from EEG Signals using Machine Learning

• Main Authors: Moshfeghi, Mohammadshakib, Bartaula, Jyoti Prasad, Bedasso, Aliye Tuke
• Format: Others
• Language: English
• Published: Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap 2013
• Subjects: EEG data classification; Emotion recognition; Affective computing; Computer Sciences; Datavetenskap (datalogi); Signal Processing; Signalbehandling
• Online Access: http://urn.kb.se/resolve?urn=urn:nbn:se:bth-4147

The beauty of affective computing is to make machine more emphatic to the user. Machines with the capability of emotion recognition can actually look inside the user’s head and act according to observed mental state. In this thesis project, we investigate different features set to build an emotion recognition system from electroencephalographic signals. We used pictures from International Affective Picture System to motivate three emotional states: positive valence (pleasant), neutral, negative valence (unpleasant) and also to induce three sets of binary states: positive valence, not positive valence; negative valence, not negative valence; and neutral, not neutral. This experiment was designed with a head cap with six electrodes at the front of the scalp which was used to record data from subjects. To solve the recognition task we developed a system based on Support Vector Machines (SVM) and extracted the features, some of them we got from literature study and some of them proposed by ourselves in order to rate the recognition of emotional states. With this system we were able to achieve an average recognition rate up to 54% for three emotional states and an average recognition rate up to 74% for the binary states, solely based on EEG signals.