ArticleFrontiers in neuroscience2018
A Study of the Effects of Electrode Number and Decoding Algorithm on Online EEG-Based BCI Behavioral Performance.
Article in Frontiers in neuroscience, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Robust neural decoding with low-density EEG.Scientific reports · 2026Article
- Individualized electrode subset improves the calibration accuracy of an EEG P300-design brain-computer interface for people with severe cerebral palsy.Frontiers in human neuroscience · 2026Article
- Pediatric Spinal Cord Injury: A Review.Children (Basel, Switzerland) · 2023Review
- Applying correlation analysis to electrode optimization in source domain.Medical & biological engineering & computing · 2023Article
- The Effects of Sensory Threshold Somatosensory Electrical Stimulation on Users With Different MI-BCI Performance.Frontiers in neuroscience · 2022Article
- Implantable brain-computer interface for neuroprosthetic-enabled volitional hand grasp restoration in spinal cord injury.Brain communications · 2021Article
- Long Multi-Stage Training for a Motor-Impaired User in a BCI Competition.Frontiers in human neuroscience · 2021Article
- [Recognition method of single trial motor imagery electroencephalogram signal based on sparse common spatial pattern and Fisher discriminant analysis].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2019Article
- Exploring Training Effect in 42 Human Subjects Using a Non-invasive Sensorimotor Rhythm Based Online BCI.Frontiers in human neuroscience · 2019Article
- Article
- HD-EEG Based Classification of Motor-Imagery Related Activity in Patients With Spinal Cord Injury.Frontiers in neurology · 2018Article
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Authors and funding
7 authors.
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Abstract
Motor imagery-based brain-computer interface (BCI) using electroencephalography (EEG) has demonstrated promising applications by directly decoding users' movement related mental intention. The selection of control signals, e.g., the channel configuration and decoding algorithm, plays a vital role in the online performance and progressing of BCI control. While several offline analyses report the effect of these factors on BCI accuracy for a single session-performance increases asymptotically by increasing the number of channels, saturates, and then decreases-no online study, to the best of our knowledge, has yet been performed to compare for a single session or across training. The purpose of the current study is to assess, in a group of forty-five subjects, the effect of channel number and decoding method on the progression of BCI performance across multiple training sessions and the corresponding neurophysiological changes. The 45 subjects were divided into three groups using Laplacian Filtering (LAP/S) with nine channels, Common Spatial Pattern (CSP/L) with 40 channels and CSP (CSP/S) with nine channels for online decoding. At the first training session, subjects using CSP/L displayed no significant difference compared to CSP/S but a higher average BCI performance over those using LAP/S. Despite the average performance when using the LAP/S method was initially lower, but LAP/S displayed improvement over first three sessions, whereas the other two groups did not. Additionally, analysis of the recorded EEG during BCI control indicates that the LAP/S produces control signals that are more strongly correlated with the target location and a higher R-square value was shown at the fifth session. In the present study, we found that subjects' average online BCI performance using a large EEG montage does not show significantly better performance after the first session than a smaller montage comprised of a common subset of these electrodes. The LAP/S method with a small EEG montage allowed the subjects to improve their skills across sessions, but no improvement was shown for the CSP method.
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