Normalized deep learning algorithms based information aggregation functions to classify motor imagery EEG signal

Al-Hamadani, Ammar A.; Mohammed, Mamoun Jassim; Tariq, Suphian Mohammed

doi:10.1007/s00521-023-08944-9

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…However, these methods use fully supervised learning. Furthermore, deep learning-based MI-EEG recognition studies usually also use fully supervised learning methods such as CNN, RNN, and hybrid neural networks [11][12][13]. Miao et al [14] used a lightweight multidimensional attention network called LMDA-Net to improve the feature extraction and classification performance by introducing a channel attention module and a deep attention module.…”

Section: Introductionmentioning

confidence: 99%

Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network

Cunlin,

Ye,

Nenggang

2024

J. Neural Eng.

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Objective. Aiming for the research on the brain–computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samples. Approach. In this paper, we propose a self-supervised MI-EEG recognition method based on self-supervised learning with one-dimensional multi-task convolutional neural networks and long short-term memory (1-D MTCNN-LSTM). The model is divided into two stages: signal transform identification stage and pattern recognition stage. In the signal transform recognition phase, the signal transform dataset is recognized by the upstream 1-D MTCNN-LSTM network model. Subsequently, the backbone network from the signal transform identification phase is transferred to the pattern recognition phase. Then, it is fine-tuned using a trace amount of labeled data to finally obtain the motion recognition model. Main results. The upstream stage of this study achieves more than 95% recognition accuracy for EEG signal transforms, up to 100%. For MI-EEG pattern recognition, the model obtained recognition accuracies of 82.04% and 87.14% with F1 scores of 0.7856 and 0.839 on the datasets of BCIC-IV-2b and BCIC-IV-2a. Significance. The improved accuracy proves the superiority of the proposed method. It is prospected to be a method for accurate classification of MI-EEG in the BCI system.

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