The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network

Yang, Banghua; Ma, Jun; Qiu, Wenzheng; Zhang, Jian; Wang, Xiaofan

doi:10.1016/j.bspc.2022.103855

Biomedical Signal Processing and Control

2022

DOI: 10.1016/j.bspc.2022.103855

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The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network

Banghua Yang

Jun Ma

Wenzheng Qiu

et al.

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Cited by 3 publications

(1 citation statement)

References 38 publications

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“…A time-distributed attention network (TD-Atten) was proposed in [31] to adaptively assign different weights to different classes and frequency bands of the input multiband Common Spatial Pattern (CSP) features, and obtained the accuracies of 46.8% in the 5-class scenario and 53.4% in the 4-class scenario. A novel fMRI-weighted Convolutional Neural Network (CNN) was designed in [32] to reassign each channel's weight based on brain activation areas to improve classification accuracy, and the average classification accuracy of fMRI-weighted CNN is 47.0%. In the multi-class scenario, the current study has achieved good results in the MI-BCI.…”

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confidence: 99%

TDLNet: Transfer Data Learning Network for Cross-Subject Classification Based on Multiclass Upper Limb Motor Imagery EEG

Bi,

Chu

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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The limited number of brain-computer interface based on motor imagery (MI-BCI) instruction sets for different movements of single limbs makes it difficult to meet practical application requirements. Therefore, designing a single-limb, multi-category motor imagery (MI) paradigm and effectively decoding it is one of the important research directions in the future development of MI-BCI. Furthermore, one of the major challenges in MI-BCI is the difficulty of classifying brain activity across different individuals. In this article, the transfer data learning network (TDLNet) is proposed to achieve the cross-subject intention recognition for multiclass upper limb motor imagery. In TDLNet, the Transfer Data Module (TDM) is used to process cross-subject electroencephalogram (EEG) signals in groups and then fuse cross-subject channel features through two one-dimensional convolutions. The Residual Attention Mechanism Module (RAMM) assigns weights to each EEG signal channel and dynamically focuses on the EEG signal channels most relevant to a specific task. Additionally, a feature visualization algorithm based on occlusion signal frequency is proposed to qualitatively analyze the proposed TDLNet. The experimental results show that TDLNet achieves the best classification results on two datasets compared to CNN-based reference methods and transfer learning method. In the 6-class scenario, TDLNet obtained an accuracy of 65%±0.05 on the UML6 dataset and 63%±0.06 on the GRAZ dataset. The visualization results demonstrate that the proposed framework can produce distinct classifier patterns for multiple categories of upper limb motor imagery through signals of different frequencies. The ULM6 dataset is available at

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confidence: 99%

TDLNet: Transfer Data Learning Network for Cross-Subject Classification Based on Multiclass Upper Limb Motor Imagery EEG

Bi,

Chu

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN

Ma,

Yang,

Rong

et al. 2024

Cogn Neurodyn

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A survey on encoding and decoding technology of non-invasive brain-computer interface

Shuang,

Banghua,

Xiaogang

et al. 2023

Journal of Image and Graphics

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The unilateral upper limb classification from fMRI-weighted EEG signals using convolutional neural network

Cited by 3 publications

References 38 publications

TDLNet: Transfer Data Learning Network for Cross-Subject Classification Based on Multiclass Upper Limb Motor Imagery EEG

TDLNet: Transfer Data Learning Network for Cross-Subject Classification Based on Multiclass Upper Limb Motor Imagery EEG

Motor imagery decoding using source optimized transfer learning based on multi-loss fusion CNN

A survey on encoding and decoding technology of non-invasive brain-computer interface

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