ArticleSensors (Basel, Switzerland)2020
Separable EEG Features Induced by Timing Prediction for Active Brain-Computer Interfaces.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- [Research progress of brain-computer interface application paradigms based on rapid serial visual presentation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- [Recognition of high-frequency steady-state visual evoked potential for brain-computer interface].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Transformed common spatial pattern for motor imagery-based brain-computer interfaces.Frontiers in neuroscience · 2023Article
- Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces.Frontiers in neuroscience · 2023Article
- [Research advances in non-invasive brain-computer interface control strategies].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- The Dominance of Anticipatory Prefrontal Activity in Uncued Sensory-Motor Tasks.Sensors (Basel, Switzerland) · 2022Article
- Review of brain encoding and decoding mechanisms for EEG-based brain-computer interface.Cognitive neurodynamics · 2021Review
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Authors and funding
8 authors.
Funding
Abstract
Brain-computer interfaces (BCI) have witnessed a rapid development in recent years. However, the active BCI paradigm is still underdeveloped with a lack of variety. It is imperative to adapt more voluntary mental activities for the active BCI control, which can induce separable electroencephalography (EEG) features. This study aims to demonstrate the brain function of timing prediction, i.e., the expectation of upcoming time intervals, is accessible for BCIs. Eighteen subjects were selected for this study. They were trained to have a precise idea of two sub-second time intervals, i.e., 400 ms and 600 ms, and were asked to measure a time interval of either 400 ms or 600 ms in mind after a cue onset. The EEG features induced by timing prediction were analyzed and classified using the combined discriminative canonical pattern matching and common spatial pattern. It was found that the ERPs in low-frequency (0~4 Hz) and energy in high-frequency (20~60 Hz) were separable for distinct timing predictions. The accuracy reached the highest of 93.75% with an average of 76.45% for the classification of 400 vs. 600 ms timing. This study first demonstrates that the cognitive EEG features induced by timing prediction are detectable and separable, which is feasible to be used in active BCIs controls and can broaden the category of BCIs.
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