Version 1
: Received: 11 July 2023 / Approved: 12 July 2023 / Online: 13 July 2023 (04:53:51 CEST)
How to cite:
khoonbani, S.; Ramezanian, H. Unveiling the Unexplored Realm: Novel Approach for Emotion Recognition using Auditory Stimulation and EEG Signals. Preprints2023, 2023070834. https://doi.org/10.20944/preprints202307.0834.v1
khoonbani, S.; Ramezanian, H. Unveiling the Unexplored Realm: Novel Approach for Emotion Recognition using Auditory Stimulation and EEG Signals. Preprints 2023, 2023070834. https://doi.org/10.20944/preprints202307.0834.v1
khoonbani, S.; Ramezanian, H. Unveiling the Unexplored Realm: Novel Approach for Emotion Recognition using Auditory Stimulation and EEG Signals. Preprints2023, 2023070834. https://doi.org/10.20944/preprints202307.0834.v1
APA Style
khoonbani, S., & Ramezanian, H. (2023). Unveiling the Unexplored Realm: Novel Approach for Emotion Recognition using Auditory Stimulation and EEG Signals. Preprints. https://doi.org/10.20944/preprints202307.0834.v1
Chicago/Turabian Style
khoonbani, S. and Hassan Ramezanian. 2023 "Unveiling the Unexplored Realm: Novel Approach for Emotion Recognition using Auditory Stimulation and EEG Signals" Preprints. https://doi.org/10.20944/preprints202307.0834.v1
Abstract
Emotions play a vital role in understanding human behavior and interpersonal relationships. The ability to recognize emotions through Electroencephalogram (EEG) signals offers an alternative to traditional methods, such as questionnaires, enabling the identification of emotional states in a non-intrusive manner. Automatic emotion recognition holds great potential, eliminating the need for clinical examinations or physical visits, thereby contributing significantly to the advancement of Brain-Computer Interface (BCI) technology. However, one of the key challenges lies in effectively selecting and extracting relevant features from the EEG signal to establish meaningful distinctions between different emotional states. The process of feature selection is often time-consuming and demanding. In this research, we propose a groundbreaking approach for automatically identifying three emotional states (positive, negative, and neutral) by leveraging auditory stimulation of EEG signals. Our novel method directly applies the raw EEG signal to a Convolutional Neural Network-Long Short Term Memory (CNN-LSTM) architecture, bypassing the conventional feature extraction and selection steps. This unconventional approach offers a significant departure from existing literature. Our proposed network architecture comprises ten convolutional layers, followed by three LSTM layers and two fully connected layers. Through extensive simulations and evaluations on 12 active channels, our algorithm demonstrates exceptional performance, achieving an accuracy of 97.42\% and 95.23\% for the binary classification of negative and positive emotions, as well as a Cohen's Kappa coefficient of 0.96 and 0.93 for the three-class classification (negative, neutral, and positive), respectively. These promising results highlight the efficacy of our novel methodology and its potential implications in advancing emotion recognition using EEG signals.
Keywords
emotion recognition, auditory stimulation, EEG signals, convolutional neural network (CNN), long short term memory (LSTM), brain-computer interface (BCI)
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
