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The Recognition of Action Idea EEG with Deep Learning

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  • Guoxia Zou

Abstract

The recognition in electroencephalogram (EEG) of action idea is to identify what action people want to do by EEG. The significance of this project is to help people who have trouble in movement. Their action ideas are identified by EEG, and then robot hands can assist them to complete the action. This paper, with comparative experiments, used OpenBCI to collect EEG action ideas during static action and dynamic action and used the EEG recognition model Conv1D‐GRU to training and recognition action, respectively. The experimental result shows that the brain wave action idea is easier to recognize in static state. The accuracy of brain wave action idea recognition in dynamic state is only 72.27%, and the accuracy of brain wave action idea recognition in static state is 99.98%. The experimental result confirms that the action idea will be of great help to people with mobility difficulties.

Suggested Citation

  • Guoxia Zou, 2022. "The Recognition of Action Idea EEG with Deep Learning," Complexity, John Wiley & Sons, vol. 2022(1).
    • Handle: RePEc:wly:complx:v:2022:y:2022:i:1:n:5308885
    DOI: 10.1155/2022/5308885
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    1. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
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