Using minimal number of electrodes for emotion detection using noisy EEG data

Emotion is an important aspect in the interaction between humans. It is fundamental to human experience and rational decision-making. There is a great interest for detecting emotions automatically. A number of techniques have been employed for this purpose using channels such as voice and facial expressions. However, these channels are not very accurate and can be faked. Other techniques use physiological signals along with electroencephalography (EEG) for emotion detection. However, these approaches are not very practical for real time applications because they ask the participants to reduce any motion and facial muscle movement, reject EEG data contaminated with artifacts and rely on large number of electrodes. In this thesis, we propose an approach that analyzes highly contaminated EEG data produced from a new emotion elicitation technique. We also use a feature selection mechanism to extract features that are relevant to the emotion detection task based on neuroscience findings. Our main contribution is in the experimental methodology applied for building an automated system for emotion detection. First we experimented with a totally new emotion elicitation technique that is very close to real life situations. Second, We generate different feature sets from the prior art and compare the accuracies of different classifiers that use such different feature sets. We experimented with two feature sets that are based on some neuroscience findings. The first neuroscience fact is based on the finding the that emotions are most obvious in the alpha band which ranges from 7 to 13 Hz [1]. The second neuroscience finding is that positive emotions result in relatively greater left brain activity and negative emotions result in greater right brain activity. Hence, we decided to focus our experiments on the alpha band and making use of scalp asymmetries in case of positive and negative emotions. Finally, we experimented with different number of electrodes that were selected using two different methodologies. The first approach is to include the frontal electrodes because the alpha rhythm is most obvious in the frontal lobe. The second approach is not to include any frontal electrodes because EMG artifacts may contaminate the frontal lobe and we want to make sure that our classification results are mainly due to EEG and not EMG. Our work extends existing research in four principal ways. First, we are the first in the computer science field to use voluntary facial expression as a means for enticing emotions. Although this contaminates EEG with noise, it helps to test our approach on unconstrained environment where the users were not given any special instructions about reducing head motions or facial expressions which makes our dataset close to a real time application. Second, we used a new technique for selecting features that are relevant to the emotion detection task that is based on neuroscience findings. Third, since one of the drawbacks of emotion detection systems using EEG is the use of large number of electrodes, which hinders the portability of such systems, we applied our approach on different number of electrodes that range from 4 electrodes up to 25 electrodes using two methodologies for selecting the electrodes to be eliminated. This can make our system more portable and can be used in real application. Finally, we tested our approach on a large dataset of 36 subjects and we were able to differentiate between four different emotions with an accuracy that ranges from 51% to 61% using 25 electrodes and we reached an average classification accuracy of 33% for joy emotion, 38% for anger, 33% for fear and 37.5% for sadness using 4 or 6 electrodes only.