EEG-Based Hand Motion Pattern Recognition Using Deep Learning Network Algorithms

Jiang, Yongyu; Chen, Christine; Zhang, Xiaodong; Zhou, Wei; Chen, Chaoyang; Lemos, Stephen

doi:10.1145/3436369.3437433

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…Hossain K M, Islam M A, Hossain S, et al (2023): This review article discusses the latest advancements in deep learning based on electroencephalogram based brain computer interface applications, which may help develop neural rehabilitation strategies for patients with physical disabilities [24] . Jiang Y, Chen C, Zhang X, et al ( 2020): This study shows that hand motion pattern recognition can be extracted from EEG signals, which is very helpful for developing brain computer interface technology [25] . Al Nafjan A, Hosny M, Al Ohali Y, et al (2017): This article reviews the latest progress in emotion recognition based on EEG signals, which may help develop brain computer interface technologies for emotion detection and classification [26] .…”

Section: Introductionmentioning

confidence: 82%

EEG signal motion recognition based on GSA-SVM

Liu,

Chang,

Zhou

et al. 2024

Fourth International Conference on Machine Learning and Computer Application (ICMLCA 2023)

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This paper reviews the research progress of gait recognition and rehabilitation training control based on EEG signals. Firstly, the importance and application scenarios of gait recognition research were introduced, with a focus on discussing gait recognition methods based on EEG signals. Then, the basic characteristics of EEG signals and the main EEG signal analysis methods used for gait recognition were summarized, including time-domain analysis, frequency-domain analysis, and time-frequency analysis. Secondly, this article collected some motion EEG signals and selected corresponding recognition algorithms through data processing, feature extraction, and classifier selection to perform gait recognition on the EEG signals. The results show that the algorithm proposed in this paper has a high recognition rate.

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