Joint spatial and temporal features extraction for multi-classification of motor imagery EEG

Jia, Xueyu; Song, Yujiang; Yang, Lie; Xie, Longhan

doi:10.1016/j.bspc.2021.103247

Cited by 18 publications

(9 citation statements)

References 28 publications

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“…Subsequently, we compared our method with several state-of-the-art methods for MI four-class classification. As shown in the TABLE Ⅲ, the accuracies reveal that our method outperformed the previous studies by the FBCSP [12], SVM+FBCSP [23], ShallowNet [45], EEGNet [24], TSFCNN [50] and SPCNN [51]. The experiments show that the performance of MBSTCNN-ECA-LightGBM is improved by 9% (average accuracy) compared with the In general, the non-stationary and individual variability of EEG signals makes the model difficult to decode the crosssession MI tasks [10,52].…”

Section: Overall Classification Results and Comparisonmentioning

confidence: 82%

A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM for MI-BCI Classification

Jia

Yin

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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Accurately decoding motor imagery (MI) braincomputer interface (BCI) tasks has remained a challenge for both neuroscience research and clinical diagnosis. Unfortunately, less subject information and low signal-to-noise ratio of MI electroencephalography (EEG) signals make it difficult to decode the movement intentions of users. In this study, we proposed an end-to-end deep learning model, a multi-branch spectral-temporal convolutional neural network with channel attention and LightGBM model (MBSTCNN-ECA-LightGBM), to decode MI-EEG tasks. We first constructed a multi branch CNN module to learn spectral-temporal domain features. Subsequently, we added an efficient channel attention mechanism module to obtain more discriminative features. Finally, LightGBM was applied to decode the MI multi-classification tasks. The within-subject cross-session training strategy was used to validate classification results. The experimental results showed that the model achieved an average accuracy of 86% on the two-class MI-BCI data and an average accuracy of 74% on the four-class MI-BCI data, which outperformed current state-of-the-art methods. The proposed MBSTCNN-ECA-LightGBM can efficiently decode the spectral and temporal domain information of EEG, improving the performance of MI-based BCIs.

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Section: Overall Classification Results and Comparisonmentioning

confidence: 82%

A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM for MI-BCI Classification

Jia

Yin

et al. 2023

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…The time-frequency common spatial pattern method was used to solve the problem of poor classification and robustness in MI-BCI systems (Mishuhina and Jiang, 2021 ). Jia et al published one of the most recent studies based on the spatial EEG decoding method, and they employed time-contained spatial filtering to extract spatial and temporal information for EEG multi-classification tasks (Jia et al, 2021 ).…”

Section: Introductionmentioning

confidence: 99%

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

Liu

et al. 2022

Front. Neurosci.

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Multiple types of brain-control systems have been applied in the field of rehabilitation. As an alternative scheme for balancing user fatigue and the classification accuracy of brain–computer interface (BCI) systems, facial-expression-based brain control technologies have been proposed in the form of novel BCI systems. Unfortunately, existing machine learning algorithms fail to identify the most relevant features of electroencephalogram signals, which further limits the performance of the classifiers. To address this problem, an improved classification method is proposed for facial-expression-based BCI (FE-BCI) systems, using a convolutional neural network (CNN) combined with a genetic algorithm (GA). The CNN was applied to extract features and classify them. The GA was used for hyperparameter selection to extract the most relevant parameters for classification. To validate the superiority of the proposed algorithm used in this study, various experimental performance results were systematically evaluated, and a trained CNN-GA model was constructed to control an intelligent car in real time. The average accuracy across all subjects was 89.21 ± 3.79%, and the highest accuracy was 97.71 ± 2.07%. The superior performance of the proposed algorithm was demonstrated through offline and online experiments. The experimental results demonstrate that our improved FE-BCI system outperforms the traditional methods.

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“…As a standard paradigm in brain-computer interfaces [7], MI has rapidly developed in recent years. Underlying this rapid development is the ability of MI to trigger contralateral explicit event-related desynchronization (ERD) and, in some cases, simultaneous ipsilateral event-related synchronization (ERS) by unilateral imaging movements [8] For instance, when picturing unilateral hand movements, the energy of mu rhythms (8)(9)(10)(11)(12) and beta rhythms (14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) in the contralateral brain region is decreased (ERD), whereas the energy of mu rhythms and beta rhythms in the ipsilateral motor-sensory areas is increased (ERS) [9]. The spontaneity and classifiability of MI make it a critical factor in ensuring the availability and smoothness (the efficiency of information transmission) of the machine subsystem in BCI systems.…”

Section: Introductionmentioning

confidence: 99%

“…The spontaneity and classifiability of MI make it a critical factor in ensuring the availability and smoothness (the efficiency of information transmission) of the machine subsystem in BCI systems. Much of the current research on motion imagery has focused on the separability of MI, enhancing accuracy by examining feature extraction [10], channel selection [11], and classification methods [12].…”

Section: Introductionmentioning

confidence: 99%

Exploring the Visual Guidance of Motor Imagery in Sustainable Brain–Computer Interfaces

et al. 2022

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Motor imagery brain–computer interface (MI-BCI) systems hold the possibility of restoring motor function and also offer the possibility of sustainable autonomous living for individuals with various motor and sensory impairments. When utilizing the MI-BCI, the user’s performance impacts the system’s overall accuracy, and concentrating on the user’s mental load enables a better evaluation of the system’s overall performance. The impacts of various levels of abstraction on visual guidance of mental training in motor imagery (MI) may be comprehended. We proposed hypotheses about the effects of visually guided abstraction on brain activity, mental load, and MI-BCI performance, then used the event-related desynchronization (ERD) value to measure the user’s brain activity, extracted the brain power spectral density (PSD) to measure the brain load, and finally classified the left- and right-handed MI through a support vector machine (SVM) classifier. The results showed that visual guidance with a low level of abstraction could help users to achieve the highest brain activity and the lowest mental load, and the highest accuracy rate of MI classification was 97.14%. The findings imply that to improve brain–computer interaction and enable those less capable to regain their mobility, visual guidance with a low level of abstraction should be employed when training brain–computer interface users. We anticipate that the results of this study will have considerable implications for human-computer interaction research in BCI.

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Joint spatial and temporal features extraction for multi-classification of motor imagery EEG

Cited by 18 publications

References 28 publications

A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM for MI-BCI Classification

A Model Combining Multi Branch Spectral-Temporal CNN, Efficient Channel Attention, and LightGBM for MI-BCI Classification

A novel EEG decoding method for a facial-expression-based BCI system using the combined convolutional neural network and genetic algorithm

Exploring the Visual Guidance of Motor Imagery in Sustainable Brain–Computer Interfaces

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