Emotional State Detection Using Electroencephalogram Signals: A Genetic Algorithm Approach

Abstract

Emotion recognition based on electroencephalogram signals (EEG) has been analyzed extensively in different applications, most of them using medical-grade equipment in laboratories. The trend in human-centered artificial intelligence applications is toward using portable sensors with reduced size and improved portability that can be taken to real life scenarios, which requires systems that efficiently analyze information in real time. Currently, there is no specific set of features or specific number of electrodes defined to classify specific emotions using EEG signals, and performance may be improved with the combination of all available features but could result in high dimensionality and even worse performance; to solve the problem of high dimensionality, this paper proposes the use of genetic algorithms (GA) to automatically search the optimal subset of EEG data for emotion classification. Publicly available EEG data with 2548 features describing the waves related to different emotional states are analyzed, and then reduced to 49 features with genetic algorithms. The results show that only 49 features out of the 2548 can be sufficient to create machine learning (ML) classification models with, using algorithms such as k-nearest neighbor (KNN), random forests (RF) and artificial neural networks (ANN), obtaining results with 90.06%, 93.62% and 95.87% accuracy, respectively, which are higher than the 87.16% and 89.38% accuracy of previous works.

Characterisation of Cognitive Load Using Machine Learning Classifiers of Electroencephalogram Data

“…The features used to classify EEG data in driving-related research vary significantly across the literature. Some studies use frequency bands, such as Delta (0.5-4 Hz), Theta (4-8 Hz), Alpha (8-14 Hz), Beta (14-30 Hz), and Gamma (30-100 Hz), although the specific frequency ranges chosen may differ [17][18][19][20][21][22][23][24][25][26]. Other studies use features generated from Power Spectrum Density (PSD) [27,28].…”