A novel approach of decoding four-class motor imagery tasks via wavelet transform and 1DCNN-BiLSTM
Author:
Funder
Nature Science Foundation of China
Publisher
Springer Science and Business Media LLC
Subject
Computer Networks and Communications,Hardware and Architecture,Media Technology,Software
Link
https://link.springer.com/content/pdf/10.1007/s11042-023-17396-1.pdf
Reference41 articles.
1. Saibene A, Caglioni M, Corchs S et al (2023) EEG-based BCIs on motor imagery paradigm using wearable technologies: a systematic review. Sensors 23(5):2798. pp 221-237
2. Wang K, Zhai DH, Xiong Y et al (2021) An MVMD-CCA recognition algorithm in SSVEP-based BCI and its application in robot control. IEEE Trans Neural Netw Learn Syst 33(5):2159–2167
3. Khademi S, Neghabi M, Farahi M et al (2022) A comprehensive review of the movement imaginary brain-computer interface methods: challenges and future directions. Artif Intell Based Brain Comput Interface 23–74
4. Wu Y (2023) Applications of EEG-based brain-computer interface devices in rehabilitation. Highlights Sci Eng Technol 39:809–815
5. Tang X, Yang C, Sun X et al (2023) Motor imagery EEG decoding based on multi-scale hybrid networks and feature enhancement. IEEE Trans Neural Syst Rehabil Eng 31:1208–1218
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