Comparison of cross-subject EEG emotion recognition algorithms in the BCI Controlled Robot Contest in World Robot Contest 2021
Open accessAbstract
Electroencephalogram (EEG) data depict various emotional states and reflect brain activity. There has been increasing interest in EEG emotion recognition in brain–computer interface systems (BCIs). In the World Robot Contest (WRC), the BCI Controlled Robot Contest successfully staged an emotion recognition technology competition. Three types of emotions (happy, sad, and neutral) are modeled using EEG signals. In this study, 5 methods employed by different teams are compared. The results reveal that classical machine learning approaches and deep learning methods perform similarly in offline recognition, whereas deep learning methods perform better in online cross-subject decoding.
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EEGNet has been proven its effectiveness in BCI competitions.
“Furthermore, the EEGNet has been proven its effectiveness in BCI competitions [41],[51],[52].”
From A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection (2024) · cites this paper as [52]
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A Cascade xDAWN EEGNet Structure for Unified Visual-Evoked Related Potential Detection
Wang, H., Wang, Z., Sun, Y., Yuan, Z., Xu, T., & Li, J. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3415474 · Cited by 20 · see citing works
“Compared with other methods like DeepConvNet or mere EEGNet, the proposed method achieved better ITR with fewer repetition rounds for the P300 speller and gained high UAR performance in the RSVP paradigm.”
Method
- Authors
- Tang, C., Li, Y., & Chen, B.
- Affiliations
- These authors contributed equally to this work.
- Corresponding author
- Chen, Badong
- Venue
- Brain Science Advances
- Publisher
- Tsinghua University Press
- Published
- 2022-06
- Volume
- 8
- Issue
- 2
- Pages
- 142-152
- ISSN
- 2096-5958 (ISSN-L)
- OpenAlex
- W4283719671
- Cited by
- 10
- References
- 31
- Citations by year
- 2025: 1 · 2024: 4 · 2023: 4 · 2022: 1
- FWCI
- 2.30
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Brain Science Advances (publishedVersion, cc-by-nc)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Emotion and Mood Recognition
- Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
- Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
- Keywords
- EEG-based emotion recognition, brain-computer interface, cross-subject decoding, deep learning, machine learning
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