A Multi-Scale Fusion Convolutional Neural Network Based on Attention Mechanism for the Visualization Analysis of EEG Signals Decoding

Li, Donglin; Xu, Jiacan; Wang, Jianhui; Fang, Xiaoke; Ji, Ying

doi:10.1109/tnsre.2020.3037326

Cited by 80 publications

(37 citation statements)

References 43 publications

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“…Then, these 2 D images are applied to different 2D deep learning networks. Another future work is using novel DL techniques such as attention learning [119][120][121][122], transformers [123,124], and other advanced deep learning techniques [125][126][127][128][129][130][131][132][133][134] for epileptic seizure detection. Finally, adopting novel deep feature fusion techniques to epileptic seizures detection based on EEG signals can be noteworthy as one of the future works [135].…”

Section: Discussion Conclusion and Future Workmentioning

confidence: 99%

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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Epilepsy is a brain disorder disease that affects people’s quality of life. Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper provides a computer-aided diagnosis system (CADS) for the automatic diagnosis of epileptic seizures in EEG signals. The proposed method consists of three steps, including preprocessing, feature extraction, and classification. In order to perform the simulations, the Bonn and Freiburg datasets are used. Firstly, we used a band-pass filter with 0.5–40 Hz cut-off frequency for removal artifacts of the EEG datasets. Tunable-Q Wavelet Transform (TQWT) is used for EEG signal decomposition. In the second step, various linear and nonlinear features are extracted from TQWT sub-bands. In this step, various statistical, frequency, and nonlinear features are extracted from the sub-bands. The nonlinear features used are based on fractal dimensions (FDs) and entropy theories. In the classification step, different approaches based on conventional machine learning (ML) and deep learning (DL) are discussed. In this step, a CNN–RNN-based DL method with the number of layers proposed is applied. The extracted features have been fed to the input of the proposed CNN–RNN model, and satisfactory results have been reported. In the classification step, the K-fold cross-validation with k = 10 is employed to demonstrate the effectiveness of the proposed CNN–RNN classification procedure. The results revealed that the proposed CNN–RNN method for Bonn and Freiburg datasets achieved an accuracy of 99.71% and 99.13%, respectively.

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Section: Discussion Conclusion and Future Workmentioning

confidence: 99%

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Malekzadeh

Zare

Yaghoobi

et al. 2021

Sensors

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“…The branch module was used to save and process each band to obtain optimal classification results. Donglin et al [18] proposed a multiscale fusion convolution neural network based on attention mechanism (MS-AMF) to improve the ability to learn local details of the brain region. First, the network divided the original EEG data according to different brain regions, and then, it extracted the spatio- With the development of deep learning, the end-to-end deep learning method is used to process the EEG signals of motor imagery, and different network structures are designed to adapt to the classification of EEG signals of motor imagery, which meets the requirements of extracting effective features and has higher robustness.…”

Section: Introductionmentioning

confidence: 99%

Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network

Chang

Zhang

2022

Micromachines

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For the successful application of brain-computer interface (BCI) systems, accurate recognition of electroencephalography (EEG) signals is one of the core issues. To solve the differences in individual EEG signals and the problem of less EEG data in classification and recognition, an attention mechanism-based multi-scale convolution network was designed; the transfer learning data alignment algorithm was then introduced to explore the application of transfer learning for analyzing motor imagery EEG signals. The data set 2a of BCI Competition IV was used to verify the designed dual channel attention module migration alignment with convolution neural network (MS-AFM). Experimental results showed that the classification recognition rate improved with the addition of the alignment algorithm and adaptive adjustment in transfer learning; the average classification recognition rate of nine subjects was 86.03%.

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“…Still, the approaches for building class activation mapping-based (CAM) visualizations have increased interest in MI research, which performs a weighted sum of the feature maps of the last convolutional layer for each class using and a structural regularizer for preventing overfitting during training [32,33]. Specifically, the visualizations generated by gradient-based methods such as GradCam provide explanations with fine-grained details of the predicted class [34][35][36]. However, the CNN-learned features to be highlighted for interpretation purposes must be compatible with the neurophysiological principle of MI [37,38].…”

Section: Introductionmentioning

confidence: 99%

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

Collazos-Huertas

Álvarez-Meza

Castellanos-Domínguez

2022

Applied Sciences

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Brain activity stimulated by the motor imagery paradigm (MI) is measured by Electroencephalography (EEG), which has several advantages to be implemented with the widely used Brain–Computer Interfaces (BCIs) technology. However, the substantial inter/intra variability of recorded data significantly influences individual skills on the achieved performance. This study explores the ability to distinguish between MI tasks and the interpretability of the brain’s ability to produce elicited mental responses with improved accuracy. We develop a Deep and Wide Convolutional Neuronal Network fed by a set of topoplots extracted from the multichannel EEG data. Further, we perform a visualization technique based on gradient-based class activation maps (namely, GradCam++) at different intervals along the MI paradigm timeline to account for intra-subject variability in neural responses over time. We also cluster the dynamic spatial representation of the extracted maps across the subject set to come to a deeper understanding of MI-BCI coordination skills. According to the results obtained from the evaluated GigaScience Database of motor-evoked potentials, the developed approach enhances the physiological explanation of motor imagery in aspects such as neural synchronization between rhythms, brain lateralization, and the ability to predict the MI onset responses and their evolution during training sessions.

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A Multi-Scale Fusion Convolutional Neural Network Based on Attention Mechanism for the Visualization Analysis of EEG Signals Decoding

Cited by 80 publications

References 43 publications

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Epileptic Seizures Detection in EEG Signals Using Fusion Handcrafted and Deep Learning Features

Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network

Image-Based Learning Using Gradient Class Activation Maps for Enhanced Physiological Interpretability of Motor Imagery Skills

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