EEG based emotion recognition system using MFDFA as feature extractor

Abstract

Emotion is a complex set of interactions among subjective and objective factors governed by neural/hormonal systems resulting in the arousal of feelings and generate cognitive processes, activate physiological changes such as behavior. Emotion recognition can be correctly done by EEG signals. Electroencephalogram (EEG) is the direct reflection of the activities of hundreds and millions of neurons residing within the brain. Different emotion states create distinct EEG signals in different brain regions. Therefore EEG provides reliable technique to identify the underlying emotion information. This paper proposes a novel approach to recognize users emotions from electroencephalogram (EEG) signals. Audio signals are used as stimuli to elicit positive and negative emotions of subjects. For eight healthy subjects, EEG signals are acquired using seven channels of an EEG amplifier. The result reveal that frontal, temporal and parietal regions of the brain are relevant to positive emotion recognition and frontal and parietal regions are activated in case of negative emotion identification. After proper signal processing of the raw EEG, for the whole frequency bands the features are extracted from each channel of the EEG signals by Multifractral Detrended Fluctuation Analysis (MFDFA) method. We introduce an effective classifier named Support Vector Machine (SVM) to categorize the EEG feature space related to various emotional states into their respective classes. Next, we compare Support Vector Machine (SVM) with various other methods like Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA) and K Nearest Neighbor (KNN). The average classification accuracy of SVM for positive emotions on the whole frequency bands is 84.50%, while the accuracy of QDA is 76.50% and with LDA 75.25% and KNN is only 69.625% whereas, for negative emotions it is 82.50%, while for QDA is 72.375% and with LDA 65.125% and KNN is only 70.50%.

An Investigation of Deep Learning Models for EEG-Based Emotion Recognition

“…Murugappan et al ( 2010 ) used the “db4” wavelet function for deriving a set of conventional and modified energy-based features from EEG signals for classifying emotions. Paul et al ( 2015 ) used the multifractral detrended fluctuation analysis (MFDFA) method to extract features and used a support vector machine (SVM) to categorize the EEG feature space related to various emotional states into their respective classes. Jiang et al ( 2020a ) used transfer learning to reduce the differences in data distribution between the training and testing data (Yang et al, 2016 ; Jiang et al, 2017 ).…”

Global Cortical Network Distinguishes Motor Imagination of the Left and Right Foot

“…Brain activity patterns acquired through fNIRS, fMRI, EEG, or electrocorticography (ECoG) are decoded into an input signal to manipulate external devices such as cursors [16], [17], [18], wheelchairs [19], [20] or exoskeletons [21], [22], [23]. BCI have also been used to detect physiological states such as attention [24], drowsiness [25], [26] and various emotions [27], [28], [29]. To date, the three main techniques to acquire an interpretable signal for BCI are steady-state visually evoked potential (SSVEP), P300 oddball stimulus, and MI somatosensory rhythms.…”

Review on Stimuli Presentation for Affect Analysis Based on EEG

“…Other than stimuli, there are certain important issues such as display setup, participant's information and ambient conditions need to be considered in stimuli presentation and reported accordingly. Nevertheless, few studies have not revealed these important factors like, laboratory environment [73], [74], [78] (screen size, ambient conditions, etc.) and the participant information [32], [132]…”