Using Black Hole Algorithm to Improve EEG-Based Emotion Recognition

Abstract

Emotions are a critical aspect of human behavior. One widely used technique for research in emotion measurement is based on the use of EEG signals. In general terms, the first step of signal processing is the elimination of noise, which can be done in manual or automatic terms. The next step is determining the feature vector using, for example, entropy calculation and its variations to generate a classification model. It is possible to use this approach to classify theoretical models such as the Circumplex model. This model proposes that emotions are distributed in a two-dimensional circular space. However, methods to determine the feature vector are highly susceptible to noise that may exist in the signal. In this article, a new method to adjust the classifier is proposed using metaheuristics based on the black hole algorithm. The method is aimed at obtaining results similar to those obtained with manual noise elimination methods. In order to evaluate the proposed method, the MAHNOB HCI Tagging Database was used. Results show that using the black hole algorithm to optimize the feature vector of the Support Vector Machine we obtained an accuracy of 92.56% over 30 executions.

Multi-Modal Emotion Aware System Based on Fusion of Speech and Brain Information

“…The window size of 6 S was found to give the best AVR. We also compared our results to the other state-of-the-art results obtained using MAHNOB database, as shown in Table 10 [68][69][70]. The results indicate that the proposed model outperforms the state-of-the-art concerning EEG emotion recognition.…”

Real-Time Mobile Emotional Content Player Using Smartphone Camera-Based PPG Measurement

“…Studies have been conducted using the central nervous system responses or automatic nervous system responses from biological signals [18][19][20][21][22][23]. Electroencephalograph (EEG) signals are usually used in studies on central nervous system responses [19][20][21][22][23].…”

“…Studies have been conducted using the central nervous system responses or automatic nervous system responses from biological signals [18][19][20][21][22][23]. Electroencephalograph (EEG) signals are usually used in studies on central nervous system responses [19][20][21][22][23]. A study was conducted that implemented an emotion inference system by measuring, analyzing, and evaluating an EEG while a user was experiencing an audio and video stimulus [19][20][21][22][23].…”

“…Electroencephalograph (EEG) signals are usually used in studies on central nervous system responses [19][20][21][22][23]. A study was conducted that implemented an emotion inference system by measuring, analyzing, and evaluating an EEG while a user was experiencing an audio and video stimulus [19][20][21][22][23]. Furthermore, a study that measured the biological signals of an EEG and an electrocardiograph (ECG) displayed the emotional states of tension, irritation, anger, joy, and surprise on the power spectrum graph of the signals [24,25].…”

“…ECG, PPG, and skin temperature (SKT) signals are mainly used in automatic nervous system responses. A study was conducted on acquiring emotion information in real time by measuring the biological signals of a PPG, SKT, and galvanic skin response (GSR), and a real-time emotion information sharing system was developed [10][11][12][13][20][21][22][23][24][25][26][27][28][29][30][31][32]. However, to measure ECG signals, a sensor has to be attached close to the heart, and accordingly, there are disadvantages in that the user may feel repulsion and experience a limitation in their activities [32].…”