Design of Wearable EEG Devices Specialized for Passive Brain–Computer Interface Applications
Owing to the increased public interest in passive brain–computer interface (pBCI) applications, many wearable devices for capturing electroencephalogram (EEG) signals in daily life have recently been released on the market. However, there exists no well-established criterion to determine the electro...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-08-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/20/16/4572 |
| _version_ | 1797558058356834304 |
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| author | Seonghun Park Chang-Hee Han Chang-Hwan Im |
| author_facet | Seonghun Park Chang-Hee Han Chang-Hwan Im |
| author_sort | Seonghun Park |
| collection | DOAJ |
| description | Owing to the increased public interest in passive brain–computer interface (pBCI) applications, many wearable devices for capturing electroencephalogram (EEG) signals in daily life have recently been released on the market. However, there exists no well-established criterion to determine the electrode configuration for such devices. Herein, an overall procedure is proposed to determine the optimal electrode configurations of wearable EEG devices that yield the optimal performance for intended pBCI applications. We utilized two EEG datasets recorded in different experiments designed to modulate emotional or attentional states. Emotion-specialized EEG headsets were designed to maximize the accuracy of classification of different emotional states using the emotion-associated EEG dataset, and attention-specialized EEG headsets were designed to maximize the temporal correlation between the EEG index and the behavioral attention index. General purpose electrode configurations were designed to maximize the overall performance in both applications for different numbers of electrodes (2, 4, 6, and 8). The performance was then compared with that of existing wearable EEG devices. Simulations indicated that the proposed electrode configurations allowed for more accurate estimation of the users’ emotional and attentional states than the conventional electrode configurations, suggesting that wearable EEG devices should be designed according to the well-established EEG datasets associated with the target pBCI applications. |
| first_indexed | 2024-03-10T17:25:04Z |
| format | Article |
| id | doaj.art-20d6c69328e445a680e2795fa27a030d |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-10T17:25:04Z |
| publishDate | 2020-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-20d6c69328e445a680e2795fa27a030d2023-11-20T10:11:50ZengMDPI AGSensors1424-82202020-08-012016457210.3390/s20164572Design of Wearable EEG Devices Specialized for Passive Brain–Computer Interface ApplicationsSeonghun Park0Chang-Hee Han1Chang-Hwan Im2Department of Biomedical Engineering, Hanyang University, Seoul 04763, KoreaDepartment of Biomedical Engineering, Hanyang University, Seoul 04763, KoreaDepartment of Biomedical Engineering, Hanyang University, Seoul 04763, KoreaOwing to the increased public interest in passive brain–computer interface (pBCI) applications, many wearable devices for capturing electroencephalogram (EEG) signals in daily life have recently been released on the market. However, there exists no well-established criterion to determine the electrode configuration for such devices. Herein, an overall procedure is proposed to determine the optimal electrode configurations of wearable EEG devices that yield the optimal performance for intended pBCI applications. We utilized two EEG datasets recorded in different experiments designed to modulate emotional or attentional states. Emotion-specialized EEG headsets were designed to maximize the accuracy of classification of different emotional states using the emotion-associated EEG dataset, and attention-specialized EEG headsets were designed to maximize the temporal correlation between the EEG index and the behavioral attention index. General purpose electrode configurations were designed to maximize the overall performance in both applications for different numbers of electrodes (2, 4, 6, and 8). The performance was then compared with that of existing wearable EEG devices. Simulations indicated that the proposed electrode configurations allowed for more accurate estimation of the users’ emotional and attentional states than the conventional electrode configurations, suggesting that wearable EEG devices should be designed according to the well-established EEG datasets associated with the target pBCI applications.https://www.mdpi.com/1424-8220/20/16/4572wearable EEG devicepassive brain–computer interfaceelectroencephalographyaffective computing |
| spellingShingle | Seonghun Park Chang-Hee Han Chang-Hwan Im Design of Wearable EEG Devices Specialized for Passive Brain–Computer Interface Applications Sensors wearable EEG device passive brain–computer interface electroencephalography affective computing |
| title | Design of Wearable EEG Devices Specialized for Passive Brain–Computer Interface Applications |
| title_full | Design of Wearable EEG Devices Specialized for Passive Brain–Computer Interface Applications |
| title_fullStr | Design of Wearable EEG Devices Specialized for Passive Brain–Computer Interface Applications |
| title_full_unstemmed | Design of Wearable EEG Devices Specialized for Passive Brain–Computer Interface Applications |
| title_short | Design of Wearable EEG Devices Specialized for Passive Brain–Computer Interface Applications |
| title_sort | design of wearable eeg devices specialized for passive brain computer interface applications |
| topic | wearable EEG device passive brain–computer interface electroencephalography affective computing |
| url | https://www.mdpi.com/1424-8220/20/16/4572 |
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