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.

With the great advancement in robot technology, smart human-robot interaction is considered to be the most wanted success by the researchers these days. If a robot can identify emotions and intentions of a human interacting with it, that would make robots more useful. Electroencephalography (EEG) is considered one effective way of recording emotions and motivations of a human using brain. Various machine learning techniques are used successfully to classify EEG data accurately. K-Nearest Neighbor, Bayesian Network, Artificial Neural Networks and Support Vector Machine are among the suitable machine learning techniques to classify EEG data. The aim of this thesis is to evaluate different machine learning techniques to classify EEG data associated with specific affective/emotional states. Different methods based on different signal processing techniques are studied to find a suitable method to process the EEG data. Various number of EEG data features are used to identify those which give best results for different classification techniques. Different methods are designed to format the dataset for EEG data. Formatted datasets are then evaluated on various machine learning techniques to find out which technique can accurately classify EEG data according to associated affective/emotional states. Research method includes conducting an experiment. The aim of the experiment was to find the various emotional states in subjects as they look on different pictures and record the EEG data. The obtained EEG data is processed, formatted and evaluated on various machine learning techniques to find out which technique can accurately classify EEG data according to associated affective/emotional states. The experiment confirms the choice of a technique for improving the accuracy of results. According to the results, Support Vector Machine is the first and Regression Tree is the second best to classify EEG data associated with specific affective/emotional states with accuracies up to 70.00% and 60.00% respectively. SVM is better in performance than RT. However, RT is famous for providing better accuracies for diverse EEG data.