Lightweight Building of an Electroencephalogram-Based Emotion Detection System
Brain–computer interface (BCI) technology provides a direct interface between the brain and an external device. BCIs have facilitated the monitoring of conscious brain electrical activity via electroencephalogram (EEG) signals and the detection of human emotion. Recently, great progress ha...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Article |
| Published: |
Multidisciplinary Digital Publishing Institute
2020
|
| Online Access: | https://hdl.handle.net/1721.1/128264 |
| _version_ | 1826215004993486848 |
|---|---|
| author | Kurdi, Heba A. |
| author2 | Massachusetts Institute of Technology. Department of Mechanical Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Mechanical Engineering Kurdi, Heba A. |
| author_sort | Kurdi, Heba A. |
| collection | MIT |
| description | Brain–computer interface (BCI) technology provides a direct interface between the brain and an external device. BCIs have facilitated the monitoring of conscious brain electrical activity via electroencephalogram (EEG) signals and the detection of human emotion. Recently, great progress has been made in the development of novel paradigms for EEG-based emotion detection. These studies have also attempted to apply BCI research findings in varied contexts. Interestingly, advances in BCI technologies have increased the interest of scientists because such technologies’ practical applications in human–machine relationships seem promising. This emphasizes the need for a building process for an EEG-based emotion detection system that is lightweight, in terms of a smaller EEG dataset size and no involvement of feature extraction methods. In this study, we investigated the feasibility of using a spiking neural network to build an emotion detection system from a smaller version of the DEAP dataset with no involvement of feature extraction methods while maintaining decent accuracy. The results showed that by using a NeuCube-based spiking neural network, we could detect the valence emotion level using only 60 EEG samples with 84.62% accuracy, which is a comparable accuracy to that of previous studies. |
| first_indexed | 2024-09-23T16:15:20Z |
| format | Article |
| id | mit-1721.1/128264 |
| institution | Massachusetts Institute of Technology |
| last_indexed | 2024-09-23T16:15:20Z |
| publishDate | 2020 |
| publisher | Multidisciplinary Digital Publishing Institute |
| record_format | dspace |
| spelling | mit-1721.1/1282642022-10-02T07:17:55Z Lightweight Building of an Electroencephalogram-Based Emotion Detection System Kurdi, Heba A. Massachusetts Institute of Technology. Department of Mechanical Engineering Brain–computer interface (BCI) technology provides a direct interface between the brain and an external device. BCIs have facilitated the monitoring of conscious brain electrical activity via electroencephalogram (EEG) signals and the detection of human emotion. Recently, great progress has been made in the development of novel paradigms for EEG-based emotion detection. These studies have also attempted to apply BCI research findings in varied contexts. Interestingly, advances in BCI technologies have increased the interest of scientists because such technologies’ practical applications in human–machine relationships seem promising. This emphasizes the need for a building process for an EEG-based emotion detection system that is lightweight, in terms of a smaller EEG dataset size and no involvement of feature extraction methods. In this study, we investigated the feasibility of using a spiking neural network to build an emotion detection system from a smaller version of the DEAP dataset with no involvement of feature extraction methods while maintaining decent accuracy. The results showed that by using a NeuCube-based spiking neural network, we could detect the valence emotion level using only 60 EEG samples with 84.62% accuracy, which is a comparable accuracy to that of previous studies. 2020-10-30T11:30:15Z 2020-10-30T11:30:15Z 2020-10-26 2020-09 2020-10-26T14:22:43Z Article http://purl.org/eprint/type/JournalArticle 2076-3425 https://hdl.handle.net/1721.1/128264 Al-Nafjan, Abeer, Khulud Alharthi and Heba Kurdi. “Lightweight Building of an Electroencephalogram-Based Emotion Detection System.” Brain Sciences, 10, 11 (October 2020): 781 © 2020 The Author(s) PUBLISHER_CC http://dx.doi.org/10.3390/brainsci10110781 Brain Sciences Creative Commons Attribution https://creativecommons.org/licenses/by/4.0/ application/pdf Multidisciplinary Digital Publishing Institute Multidisciplinary Digital Publishing Institute |
| spellingShingle | Kurdi, Heba A. Lightweight Building of an Electroencephalogram-Based Emotion Detection System |
| title | Lightweight Building of an Electroencephalogram-Based Emotion Detection System |
| title_full | Lightweight Building of an Electroencephalogram-Based Emotion Detection System |
| title_fullStr | Lightweight Building of an Electroencephalogram-Based Emotion Detection System |
| title_full_unstemmed | Lightweight Building of an Electroencephalogram-Based Emotion Detection System |
| title_short | Lightweight Building of an Electroencephalogram-Based Emotion Detection System |
| title_sort | lightweight building of an electroencephalogram based emotion detection system |
| url | https://hdl.handle.net/1721.1/128264 |
| work_keys_str_mv | AT kurdihebaa lightweightbuildingofanelectroencephalogrambasedemotiondetectionsystem |