EEG based Emotion Recognition of Image Stimuli

Abstract

Introduction: Emotion is playing a great role in our daily lives. Traditional approaches of emotion recognition are performed based on the features including facial images, measurements of heart rates, blood pressure, temperatures, tones of voice/ speech, etc. However, these features can potentially be changed to fake features by a person while it is recorded. To unhide the real features, the brain signal representing the response of a person is recorded directly from his/her brain. The various ways of measuring brain waves: electroencephalogram(EEG), Magnetoencephalography(MEG), functional magnetic resonance imaging (FMRI), etc. For this study, EEG is chosen for emotion recognition relying on cost effectiveness and performance trade-offs. Brain computer interface(BCI) based emotion recognition is used in a variety of applications including advertisement, patient treatment, depression management, music player, human computer interaction, detecting children learning disabilities, assistive technologies, game playing, automatic addition of emotional pictures during conversation, emotion enabled avatar, neuromarketing ,etc.[1]. The brainwave activity is broadly divided into five frequency bands. The boundary between the frequency bands is not strict but does not vary much. The frequency bands include delta(0.5-4Hz), theta(5-8Hz), alpha(9-12Hz), beta(13-30Hz) and gamma(above 30Hz) [2,3]. The main purpose of this study is to detect emotion based on EEG signal analysis recorded from brain in response to visual stimuli. Numerous studies demonstrated that even though it is possible to measure emotion from EEG signals recorded from stimulated brain in practice, the outputs of Brain Computer Interface(BCI) related research works are different with same stimuli and with brain response of same or different subjects [4]. The other problem is that parts of the brain that responds to emotion is not clearly identified or/and mixed up in research results. For example, emotion is responded either or both on frontal lobe or temporal lobe. Besides this, the brain wave containing emotion is not clearly understood to be in alpha frequency band or gamma frequency band. This problem inspires us to work on it. This study attempts to find out answers for the following research questions: (1) What regions of the brain are associated with visual emotion? (2) Which frequency bands of the brain waves are used for emotion recognition? (3) How accurately the chosen features were recognizing emotions using machine learning approaches? Methods: We collected three image data sets include: 90 sample images of Geneva Affective Picture Database (GAPED), 8 colour images and 36 Indian company logo. EMOTIV EPOC head sets, Emotiv EPOC TestBench Control panel software and EventIDE are used for EEG brain activity recording. The Spectrum Density (PSD) of