ArticleSensors (Basel, Switzerland)2019
Selective Feature Generation Method Based on Time Domain Parameters and Correlation Coefficients for Filter-Bank-CSP BCI Systems.
Article in Sensors (Basel, Switzerland), 2019. 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.
- A high-performance general computer cursor control scheme based on a hybrid BCI combining motor imagery and eye-tracking.iScience · 2024Article
- Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces.Frontiers in neuroscience · 2023Article
- [Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.PloS one · 2022Article
- 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
- The Ensemble Machine Learning-Based Classification of Motor Imagery Tasks in Brain-Computer Interface.Journal of healthcare engineering · 2021Article
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2 authors.
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Abstract
This paper presents a novel motor imagery (MI) classification algorithm using filter-bank common spatial pattern (FBCSP) features based on MI-relevant channel selection. In contrast to existing channel selection methods based on global CSP features, the proposed algorithm utilizes the Fisher ratio of time domain parameters (TDPs) and correlation coefficients: the channel with the highest Fisher ratio of TDPs, named principle channel, is selected and a supporting channel set for the principle channel that consists of highly correlated channels to the principle channel is generated. The proposed algorithm using the FBCSP features generated from the supporting channel set for the principle channel significantly improved the classification performance. The performance of the proposed method was evaluated using BCI Competition III Dataset IVa (18 channels) and BCI Competition IV Dataset I (59 channels).
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