Improving Multi-Class Motor Imagery EEG Classification Using Overlapping Sliding Window and Deep Learning Model

Improving Multi-Class Motor Imagery EEG Classification Using Overlapping Sliding Window and Deep Learning Model

Motor imagery (MI) electroencephalography (EEG) signals are widely used in BCI systems. MI tasks are performed by imagining doing a specific task and classifying MI through EEG signal processing. However, it is a challenging task to classify EEG signals accurately. In this study, we propose a LSTM-b...

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Bibliographic Details
Main Authors: Jeonghee Hwang, Soyoung Park, Jeonghee Chi
Format: Article
Language:English
Published: MDPI AG 2023-03-01
Series:Electronics
Subjects:
multi-class motor imagery
EEG classification
FBCSP
overlapping window
overlapping bandpass filter
LSTM
Online Access:https://www.mdpi.com/2079-9292/12/5/1186

Internet

https://www.mdpi.com/2079-9292/12/5/1186

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