Review and Classification of Emotion Recognition Based on EEG Brain-Computer Interface System Research: A Systematic Review

Abstract

Abstract:Recent developments and studies in brain-computer interface (BCI) technologies have facilitated emotion detection and classification. Many BCI studies have sought to investigate, detect, and recognize participants' emotional affective states. The applied domains for these studies are varied, and include such fields as communication, education, entertainment, and medicine. To understand trends in electroencephalography (EEG)-based emotion recognition system research and to provide practitioners and researchers with insights into and future directions for emotion recognition systems, this study set out to review published articles on emotion detection, recognition, and classification. The study also reviews current and future trends and discusses how these trends may impact researchers and practitioners alike. We reviewed 285 articles, of which 160 were refereed journal articles that were published since the inception of affective computing research. The articles were classified based on a scheme consisting of two categories: research orientation and domains/applications. Our results show considerable growth of EEG-based emotion detection journal publications. This growth reflects an increased research interest in EEG-based emotion detection as a salient and legitimate research area. Such factors as the proliferation of wireless EEG devices, advances in computational intelligence techniques, and machine learning spurred this growth.

Analysis of Personality and EEG Features in Emotion Recognition Using Machine Learning Techniques to Classify Arousal and Valence Labels

“…There is still the need to analyze how time, familiarity, interaction, and so on affect individuals' emotion recognition processes, and how the EEG features are correlated with these variables to describe individuals' emotional behavior when interacting with stimuli material. Classification accuracies vary between the different EEG traits used in the classification process and the number of participants in the experiment [11,14]. How to obtain good classification accuracies in cross-participants experiments, which can allow researchers to have more freedom in using different stimuli methods and degrees of interaction with systems to identify emotional states from EEG signals, remains unknown.…”

“…EEG signals have gained increasing attention owing to its promise of potential applications in brain-computer interface (BCIs) for assistive technological solutions to overcome physical and speech disabilities. Emotion recognition using EEG signals focuses its development on two main application fields: first, medical applications designed to provide assistance, enhancement, monitoring, assessment, and diagnosis of human psychiatric and neurological diseases; and, second, non-medical applications designed to entertain, educate, and monitor emotional states in a commercial or personal context [11,12].…”

“…EEG data lacks spatial resolution and requires several electrodes (around 8 to 128 depending on the experiment and the robustness of the equipment used) to be placed on the participant's head; however, it provides great time resolution, allowing researchers to study phase changes in response to emotional stimuli [13]. EEG signals are divided into specific ranges: delta (1-4 Hz), theta (4-7 Hz), alpha (8)(9)(10)(11)(12)(13), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (> 30 Hz) bands. To analyze the signal contribution in each band, power spectral densities (PSD) and power spectral asymmetry (PSA) were used.…”

“…Regarding emotions recognition using EEG signals, some surveys revealed how EEG signals are related with emotional behavior and used varied methodologies and traits to classify arousal-valence space or discrete emotions [11,13,14]. The main findings revealed that EEG signals and emotions are related to the frequency analysis in different bands.…”

Recognizing Human Emotion patterns by applying Fast Fourier Transform based on Brainwave Features

“…The type of the EEG output is in the way of electrical signals in the brain in the form of a graph with brainwave voltage on the time or frequency that can be seen using a computer [11]. Brainwave graphs on EEG vary depending on the condition of the human brain at the time of recording.…”