Emotion is playing a great role in our daily lives. The necessity and importance of an automatic Emotion recognition system is getting increased. Traditional approaches of emotion recognition are based on facial images, measurements of heart rates, blood pressure, temperatures, tones of voice/speech, etc. However, these features can potentially be changed to fake features. So to detect hidden and real features that is not controlled by the person are data measured from brain signals. There are various ways of measuring brain waves: EEG, MEG, FMRI, etc. On the bases of cost effectiveness and performance trade-offs, EEG is chosen for emotion recognition in this work. The main aim of this study is to detect emotion based on EEG signal analysis recorded from brain in response to visual stimuli. The approaches used were the selected visual stimuli were presented to 11 healthy target subjects and EEG signal were recorded in controlled situation to minimize artefacts (muscle or/and eye movements). The signals were filtered and type of frequency band was computed and detected. The proposed method predicts an emotion type (positive/negative) in response to the presented stimuli. Finally, the performance of the proposed approach was tested. The average accuracy of machine learning algorithms (i.e. J48, Bayes Net, Adaboost and Random Forest) are 78.86, 74.76, 77.82 and 82.46 respectively. In this study, we also applied EEG applications in the context of neuro-marketing. The empirically demonstrated detection of the favourite colour preference of customers in response to the logo colour of an organization or Service. Emotion is playing a great role in our daily lives. The necessity and importance of an automatic 25 Emotion recognition system is getting increased. Traditional approaches of emotion recognition 26 are based on facial images, measurements of heart rates, blood pressure, temperatures, tones of 27 voice/speech, etc. However, these features can potentially be changed to fake features. Thus, to simple and portable device. The brainwave activity is broadly divided into five frequency bands. The boundary between the frequency bands is not strict but not varying much. The frequency bands 63 include delta(0.5-4Hz), theta(5-8Hz), alpha(9-12Hz), beta(13-30Hz) and gamma(above 30Hz). For this study, EEG data is collected using Emotiv EPOC device with 14 electrodes located at AF3, gamma band is also responsible for arousal. In other words, the EEG brain activity from parietal 90 and frontal lobe of the brain is more emotionally informative where as gamma, alpha and beta waves We collected and prepared three image data sets for stimuli presentation and classification. These To answer research question 1 and 2, the top ranked features for each subject are extracted using
This paper presents EEG based emotion detection of a person towards an image stimuli and its applicability on neuromarketing.