EEG Motor Imagery Classification Based on Multi-spatial Convolutional Neural Network

Li, Xin; Peng, Man; Chen, Siyu; Zheng, Wenyin; Zhang, Yunxia; Gao, Dongrui; Wang, Manqing

doi:10.1109/icaibd55127.2022.9820229

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…The deep learning (DL) methods [18] boomed with the coming out of Alexnet in 2015 [19] and have made a huge success in multiple fields, especially for computer vision and natural language processing, and also gained much attention in neural engineering [20][21][22]. Considering the boosting data size and advancing hardware support, more and more researchers preferred to apply the DL methods for EEG applications, including motor imagery classification [23][24][25], emotion recognition [26][27][28], data augmentation [29][30], etc. Recently, the use of DL methods in EEG artifact removal was introduced, which gained more favorable performance compared with the conventional ML-based methods.…”

Section: Introduction Lectroencephalography (Eeg) Can Directly Reflec...mentioning

confidence: 99%

An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network

Dong,

Tang,

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Electroencephalogram (EEG) recordings often contain artifacts that would lower signal quality. Many efforts have been made to eliminate or at least minimize the artifacts, and most of them rely on visual inspection and manual operations, which is time/labor-consuming, subjective, and incompatible to filter massive EEG data in real-time. In this paper, we proposed a deep learning framework named Artifact Removal Wasserstein Generative Adversarial Network (AR-WGAN), where the well-trained model can decompose input EEG, detect and delete artifacts, and then reconstruct denoised signals within a short time. The proposed approach was systematically compared with commonly used denoising methods including Denoised AutoEncoder, Wiener Filter, and Empirical Mode Decomposition, with both public and self-collected datasets. The experimental results proved the promising performance of AR-WGAN on automatic artifact removal for massive data across subjects, with correlation coefficient up to 0.726±0.033, and temporal and spatial relative root-mean-square error as low as 0.176±0.046 and 0.761±0.046, respectively. This work may demonstrate the proposed AR-WGAN as a high-performance end-to-end method for EEG denoising, with many on-line applications in clinical EEG monitoring and brain-computer interfaces.

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Section: Introduction Lectroencephalography (Eeg) Can Directly Reflec...mentioning

confidence: 99%

An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network

Dong,

Tang,

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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show abstract

Motor Imagery EEG Recognition Based on an Improved Convolutional Neural Network with Parallel Gate Recurrent Unit

Zhang,

Guo,

et al. 2023

Lecture Notes in Computer Science

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EEG Motor Imagery Classification Based on Multi-spatial Convolutional Neural Network

Cited by 2 publications

References 18 publications

An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network

An Approach for EEG Denoising Based on Wasserstein Generative Adversarial Network

Motor Imagery EEG Recognition Based on an Improved Convolutional Neural Network with Parallel Gate Recurrent Unit

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