ArticleJournal of healthcare engineering2017
Development of Single-Channel Hybrid BCI System Using Motor Imagery and SSVEP.
Article in Journal of healthcare engineering, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Exploring the Potential of Electroencephalography Signal-Based Image Generation Using Diffusion Models: Integrative Framework Combining Mixed Methods and Multimodal Analysis.JMIR medical informatics · 2025Article
- An EEG-based asynchronous MI-BCI system to reduce false positives with a small number of channels for neurorehabilitation: A pilot study.Frontiers in neurorobotics · 2022Article
- Comparing between Different Sets of Preprocessing, Classifiers, and Channels Selection Techniques to Optimise Motor Imagery Pattern Classification System from EEG Pattern Recognition.Brain sciences · 2021Article
- [Research progress and prospect of collaborative brain-computer interface for group brain collaboration].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2021Article
- EEG-based hybrid QWERTY mental speller with high information transfer rate.Medical & biological engineering & computing · 2021Article
- Advances in Hybrid Brain-Computer Interfaces: Principles, Design, and Applications.Computational intelligence and neuroscience · 2019Review
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Authors and funding
4 authors.
Funding
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Abstract
Numerous EEG-based brain-computer interface (BCI) systems that are being developed focus on novel feature extraction algorithms, classification methods and combining existing approaches to create hybrid BCIs. Several recent studies demonstrated various advantages of hybrid BCI systems in terms of an improved accuracy or number of commands available for the user. But still, BCI systems are far from realization for daily use. Having high performance with less number of channels is one of the challenging issues that persists, especially with hybrid BCI systems, where multiple channels are necessary to record information from two or more EEG signal components. Therefore, this work proposes a single-channel (C3 or C4) hybrid BCI system that combines motor imagery (MI) and steady-state visually evoked potential (SSVEP) approaches. This study demonstrates that besides MI features, SSVEP features can also be captured from C3 or C4 channel. The results show that due to rich feature information (MI and SSVEP) at these channels, the proposed hybrid BCI system outperforms both MI- and SSVEP-based systems having an average classification accuracy of 85.6 ± 7.7% in a two-class task.
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