A novel hybrid CNN-Transformer model for EEG Motor Imagery classification

Ma, Yi; Song, Yujiang; Gao, Fei

doi:10.1109/ijcnn55064.2022.9892821

Cited by 21 publications

(18 citation statements)

References 21 publications

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“…Later, a study by [38] demonstrated the effectiveness of CSP in classifying multiple classes of motor imagery tasks. They used one-versus-the-rest (OVR) scheme to handle multi-class classification problems, which has since been widely adopted in the field [39,40]. In this scheme, a binary CSP filter is calculated for each class against all the remaining classes.…”

Section: Related Workmentioning

confidence: 99%

Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework

Ozelbas,

Tülay,

Ozekes

2024

Mach. Learn.: Sci. Technol.

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Motor Imagery Brain-Computer Interfaces (MI-BCIs) have gained a lot of attention in recent years thanks to their potential to enhance rehabilitation and control of prosthetic devices for individuals with motor disabilities. However, accurate classification of motor imagery signals remains a challenging task due to the high inter-subject variability and non-stationarity in the electroencephalogram (EEG) data. In the context of MI-BCIs, with limited data availability, the acquisition of EEG data can be difficult. In this study, several data augmentation techniques have been compared with the proposed data augmentation technique Adaptive Cross-Subject Segment Replacement (ACSSR). This technique, in conjunction with the proposed deep learning framework, allows for a combination of similar subject pairs to take advantage of one another and boost the classification performance of MI-BCIs. The proposed framework features a multi-domain feature extractor based on Common Spatial Patterns (CSP) with a sliding window and a parallel two-branch Convolutional Neural Network (CNN). The performance of the proposed methodology has been evaluated on the multi-class BCI Competition IV Dataset 2a through repeated 10- fold cross-validation. Experimental results indicated that the implementation of the ACSSR method (80.46%) in the proposed framework has led to a considerable improvement in the classification performance compared to the classification without data augmentation (77.63%), and other fundamental data augmentation techniques used in the literature. The study contributes to the advancements for the development of effective MI-BCIs by showcasing the ability of the ACSSR method to address the challenges in motor imagery signal classification tasks.

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Section: Related Workmentioning

confidence: 99%

Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework

Ozelbas,

Tülay,

Ozekes

2024

Mach. Learn.: Sci. Technol.

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“…[198] Spatially, EEG processing is transferred to vision Transformers to guide the compression and evaluation of feature maps to establish dependencies. [196,205] With the discovery of more intrinsically relevant features, it is hoped that Transformers reveal more EEG features and become the TA B L E 6 Details and quantitative assessments of Transformers in EEG processing.…”

Section: Eeg Processingmentioning

confidence: 99%

“…BENDR developed brain‐map modeling using self‐supervised learning with a self‐supervised loss‐based cosine similarity and mean squared activation, [211] and was then fine‐tuned to accommodate different downstream tasks. Similar to Ahn et al., [204] Ma et al [205] . established a Transformer to focus on the global dependence of EEG in three domains.…”

Section: Transformers In Brain Sciencesmentioning

confidence: 99%

“…In time modeling, Transformers can be directly used with EEG data, thereby driving improvements to the classic Transformer theory [198] . Spatially, EEG processing is transferred to vision Transformers to guide the compression and evaluation of feature maps to establish dependencies [196,205] . With the discovery of more intrinsically relevant features, it is hoped that Transformers reveal more EEG features and become the most significant paradigm for man‐machine interaction and brain‐computer interfaces.…”

Section: Transformers In Brain Sciencesmentioning

confidence: 99%

“…BENDR developed brain-map modeling using selfsupervised learning with a self-supervised loss-based cosine similarity and mean squared activation, [211] and was then fine-tuned to accommodate different downstream tasks. Similar to Ahn et al, [204] Ma et al [205] established a Transformer to focus on the global dependence of EEG in three domains. They placed a CNN in the post-processing of a spectral attention map to capture regional features and then implemented temporal attention enhancement to calculate global dependencies.…”

Section: Eeg Processingmentioning

confidence: 99%

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A novel hybrid CNN-Transformer model for EEG Motor Imagery classification

Cited by 21 publications

References 21 publications

Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework

Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework

Understanding the brain with attention: A survey of transformers in brain sciences

Comprehensive review of Transformer‐based models in neuroscience, neurology, and psychiatry

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