TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI

Liu, Xiaolin; Shi, Rongye; Hui, Qianxin; Xu, Susu; Wang, Shuai; Na, Rui; Sun, Yonghe; Ding, Wenbo; Zheng, Dezhi; Chen, Xinlei

doi:10.1016/j.ipm.2022.103001

Cited by 31 publications

(11 citation statements)

References 37 publications

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“…EEGNet [17] exhibited robust performance compared to DeepConvNet and ShallowConvNet within 4-30 Hz, 4-50 Hz, and 4-120 Hz, while DeepConvNet [15] and ShallowConvNet showed better performance than EEGNet within 0.5-30 Hz, 0.5-60 Hz, and 0.5-120 Hz. TCACNet [39] showed similar performance as DeepConvNet. However, the channel attention module from the TCACNet showed less effective performance than our proposed models.…”

Section: A Decoding Performance Evaluationmentioning

confidence: 77%

“…Deep ConvNet [15] Shallow ConvNet [15] EEGNet [17] TSception [37] TCACNet [39] S3T [40] S3T [40] also showed decent performance as TSception, by utilizing CSP filters and multi-head attention.…”

Section: Datasetmentioning

confidence: 99%

“…# of trainable parameters DeepConvNet [15] 305,554 ShallowConvNet [15] 110,526 EEGNet [17] 2,812 TSception [37] 8,753 S3T [40] 22,266 TCACNet [39] 233,206 TSformer 32,958 MS-TSformer 31,702 TSformer-DS 66,366 MS-TSformer-DS 65,110…”

Section: Modelsmentioning

confidence: 99%

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Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities

Ahn

Lee

Jeong

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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A new kind of sequence-to-sequence model called a transformer has been applied to electroencephalogram (EEG) systems. However, the majority of EEG-based transformer models have applied attention mechanisms to the temporal domain, while the connectivity between brain regions and the relationship between different frequencies have been neglected. In addition, many related studies on imagery-based brain-computer interface (BCI) have been limited to classifying EEG signals within one type of imagery. Therefore, it is important to develop a general model to learn various types of neural representations. In this study, we designed an experimental paradigm based on motor imagery, visual imagery, and speech imagery tasks to interpret the neural representations during mental imagery in different modalities. We conducted EEG source localization to investigate the brain networks. In addition, we propose the multiscale convolutional transformer for decoding mental imagery, which applies multi-head attention over the spatial, spectral, and temporal domains. The proposed network shows promising performance with 0.62, 0.70, and 0.72 mental imagery accuracy with the private EEG dataset, BCI competition IV 2a dataset, and Arizona State University dataset, respectively, as compared to the conventional deep learning models. Hence, we believe that it will contribute significantly to overcoming the limited number of classes and low classification performances in the BCI system.

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Section: A Decoding Performance Evaluationmentioning

confidence: 77%

Section: Datasetmentioning

confidence: 99%

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Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities

Ahn

Lee

Jeong

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…Song et al [62] proposed EEG Conformer, a convolutional transformer for EEG decoding and visualization. Liu et al [63] proposed TCACNet, a temporal and channel attention convolutional network for MI-EEG classification. Altaheri et al [64] proposed an attention-based temporal convolutional network (ATCNet) for EEG-based MI classification.…”

Section: Overview Of Existing Mi-bci Approachesmentioning

confidence: 99%

Algorithm contest of motor imagery BCI in the World Robot Contest 2022: A survey

An,

Chen,

2023

Brain Science Advances

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From August 19 to 21, 2022, the BCI Controlled Robot Contest finals in the World Robot Contest 2022 were held in Beijing, China. Fifteen teams participated in the finals in the Algorithm Contest of Motor Imagery BCI. This paper introduces the algorithms in the motor imagery (MI) classification area, describes the competition content and set, and summarizes the algorithms and results of the top five teams in the finals. First, the MI paradigm and the overview of the existing motor imagery brain–computer interface classification algorithms are introduced, followed by the introduction of the algorithms of the top five teams in the final step by step, including electroencephalography channel selection, data length selection, data preprocessing, data augmentation, classification network, training, and testing settings. Finally, the highlights and results of each algorithm are discussed.

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“…Recently, human emotions have been identified and understood using computers in the fields of medicine, neuroscience, psychology, and affective computing. Particularly, in the fields of medicine and education [1][2][3], emotion recognition can help doctors to treat different diseases, such as autism, anxiety, and certain brain dysfunctions, and assist teachers in analyzing students' cognitive levels during the learning process, providing personalized recommendations for course and exercise content.…”

Section: Introductionmentioning

confidence: 99%

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification

Liang,

Zhang,

et al. 2024

J. Neural Eng.

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Objective Electroencephalogram (EEG) analysis has always been an important tool in neural engineering, and the recognition and classification of human emotions are one of the important tasks in neural engineering. EEG data, obtained from electrodes placed on the scalp, represent a valuable resource of information for brain activity analysis and emotion recognition. Feature extraction methods have shown promising results, but recent trends have shifted toward end-to-end methods based on deep learning. However, these approaches often overlook channel representations, and their complex structures pose certain challenges to model fitting. Approach To address these challenges, this paper proposes a hybrid approach named FetchEEG that combines feature extraction and temporal-channel joint attention. Leveraging the advantages of both traditional feature extraction and deep learning, the FetchEEG adopts a multi-head self-attention mechanism to extract representations between different time moments and channels simultaneously. The joint representations are then concatenated and classified using fully-connected layers for emotion recognition. The performance of the FetchEEG is verified by comparison experiments on a self-developed dataset and two public datasets. Results In both subject-dependent and subject-independent experiments, the FetchEEG demonstrates better performance and stronger generalization ability than the state-of-the-art methods on all datasets. Moreover, the performance of the FetchEEG is analyzed for different sliding window sizes and overlap rates in the feature extraction module. The sensitivity of emotion recognition is investigated for three- and five-frequency-band scenarios. Finally, we perform visualization of the channel-attention to project the model's reliance on different channels onto scalp topographic maps. Significance FetchEEG is a novel hybrid method based on EEG for emotion classification, which significantly improves the performance of recognition and is effective and feasible.

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TCACNet: Temporal and channel attention convolutional network for motor imagery classification of EEG-based BCI

Cited by 31 publications

References 37 publications

Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities

Multiscale Convolutional Transformer for EEG Classification of Mental Imagery in Different Modalities

Algorithm contest of motor imagery BCI in the World Robot Contest 2022: A survey

FetchEEG: a hybrid approach combining feature extraction and temporal-channel joint attention for EEG-based emotion classification

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