Emotion recognition using EEG signals with relative power values and Bayesian network

Abstract

Many researchers use electroencephalograms (EEGs) to study brain activity in the context of seizures, epilepsy, and lie detection. It is desirable to eliminate EEG artifacts to improve signal collection. In this paper, we propose an emotion recognition system for human brain signals using EEG signals. We measure EEG signals relating to emotion, divide them into five frequency ranges on the basis of power spectrum density, and eliminate low frequencies from 0 to 4 Hz to eliminate EEG artifacts. The resulting calculations of the frequency ranges are based on the percentage of the selected range relative to the total range. The calculated values are then compared to standard values from a Bayesian network, calculated from databases. Finally, we show the emotion results as a human face avatar.

Single Trial Classification of EEG and Peripheral Physiological Signals for Recognition of Emotions Induced by Music Videos

“…One approach to analysis and recognition of emotions is to directly assess the activity of the central nervous system, specifically brain electrical activity, and study the changes in this activity as the user experiences different emotional states. Several works exist that are related to emotion recognition from electroencephalogram (EEG) [7,14,10,2]. Furthermore, there are a number of experiments pointing to the fact that physiological activity is not an independent variable in autonomous nervous system patterns but reflects experienced emotional states with consistent correlates [1,17].…”

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“…In recent years, EEG (Electroencephalography) method has been applied to much research [13], such as the research of marketing strategy and advertising effectiveness. In our study, EEG method was used in study of learners' interest.…”

Technological Advancements in Affective Gaming: A Historical Survey

“…The electrical potentials related to emotion can be projected widely in an intricate pattern across the scalp, and can therefore overlap with potentials evoked by other activities. EEG has been applied for classification of emotions in various contexts [8], [43], [82], [88] and is progressively becoming a portable lightweight technology. Several commercially available EEG sets can be considered for affective gaming: OCZ Nia 1 , Neurosky Mindset 2 , Neurosky Mindwave 3 , Emotiv EPOC 4 .…”