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논문 기본 정보
- 자료유형
- 학술저널
- 저자정보
- 저널정보
- 대한의용생체공학회 Biomedical Engineering Letters (BMEL) Biomedical Engineering Letters (BMEL) Vol.11 No.3
- 발행연도
- 2021.8
- 수록면
- 235 - 247 (13page)
- DOI
- https://doi.org/10.1007/s13534-021-00190-z
이용수
초록· 키워드
오류제보하기
A novel approach of preprocessing EEG signals by generating spectrum image for eff ective Convolutional Neural Network(CNN) based classifi cation for Motor Imaginary (MI) recognition is proposed. The approach involves extracting the VariationalMode Decomposition (VMD) modes of EEG signals, from which the Short Time Fourier Transform (STFT) of allthe modes are arranged to form EEG spectrum images. The EEG spectrum images generated are provided as input image toCNN. The two generic CNN architectures for MI classifi cation (EEGNet and DeepConvNet) and the architectures for patternrecognition (AlexNet and LeNet) are used in this study. Among the four architectures, EEGNet provides average accuracies of91.37%, 94.41%, 85.67% and 90.21% for the four datasets used to validate the proposed approach. Consistently better resultsin comparison with results in recent literature demonstrate that the EEG spectrum image generation using VMD-STFT is apromising method for the time frequency analysis of EEG signals.
목차
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참고문헌 (45)
참고문헌 신청
Abiri R / 2019 / A comprehensive review of eeg-based brain-computer interface paradigms / J Neural Eng 16(1):011001
Anam K / 2019 / Comparison of eeg pattern recognition of motor imagery for finger movement classification / 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) :24~27
Bashivan P / 2015 / Learning representations from eeg with deep recurrent-convolutional neural networks / arXiv preprint arXiv: 1511. 06448
Bhattacharyya S / 2018 / Reactive frequency band based movement imagery classification / Journal of Ambient Intelligence and Humanized Computing :1~14
Bizopoulos P / 2019 / Signal2image modules in deep neural networks for eeg classification / 201941st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) :702~705
Anam K / 2019 / Comparison of eeg pattern recognition of motor imagery for finger movement classification / 2019 6th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI) :24~27
Bashivan P / 2015 / Learning representations from eeg with deep recurrent-convolutional neural networks / arXiv preprint arXiv: 1511. 06448
Bhattacharyya S / 2018 / Reactive frequency band based movement imagery classification / Journal of Ambient Intelligence and Humanized Computing :1~14
Bizopoulos P / 2019 / Signal2image modules in deep neural networks for eeg classification / 201941st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) :702~705
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