Analysis and Classification for Single-Trial EEG Induced by Sequential Finger Movements

Zhang, Shanshan; Wang, Kun; Xu, Minpeng; Wang, Zhongpeng; Chen, Long; Wang, Faqi; Zhang, Lixin; Ming, Dong

doi:10.1109/embc.2019.8857117

Cited by 2 publications

(4 citation statements)

References 15 publications

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“…Hence, we used DCPM and TRCA to extract the MRCP features and used FBCSP to extract the ERD features. Then, we selected the features based on mutual information ( Zhang S. et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

“…It has been found that the effect of learning movement sequences by imagining movements is similar to that of performing the same movement sequences, and the changes in brain activity between the two are consistent ( Zhang et al, 2011 ; Hardwick et al, 2018 ; Wang et al, 2019 ; Zhang Q. et al, 2019 ). Recently, we investigated how data length affected the classification of repeated keystroke tasks with the index finger and found that single-trial EEG induced by the repeated finger movements had good separability ( Zhang S, et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces

Liu,

You,

Wang

et al. 2023

Front. Neurosci.

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ObjectiveIn recent years, motor imagery-based brain–computer interfaces (MI-BCIs) have developed rapidly due to their great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement-intention encoding paradigm based on sequential finger movement.ApproachTen subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an auditory prompt of 1 Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm.Main resultsAs a result, both the MRCP and ERD features showed the specific temporal–spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71% for LL-versus-RR and LR-versus-RL, respectively.SignificanceThis paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.

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“…Hence, we used DCPM and TRCA to extract the MRCP features and used FBCSP to extract the ERD features. Then, we selected the features based on mutual information ( Zhang S. et al, 2019 ).…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces

Liu,

You,

Wang

et al. 2023

Front. Neurosci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…How to achieve effective feature extraction and classification of motor imagery EEG signals is the research direction of EEGbased MI-BCI systems. In the early days, researchers focused attention of BCI research on the alpha or beta frequency bands, which are the driving signals that generate Event-Related Desynchronization (ERD) and Synchronization phenomena (ERS), and thus carried out research on motor-imagery EEG [11,12] . Based on the ERD/ERS phenomenon, researchers have proposed a series of processing algorithms for motor imagery EEG signals, such as Common Spatial Pattern (CSP) [13] , Autoregressive model (AR) [14] , Filter Bank Common Spatial Pattern (FBCSP) [15] and so on.…”

Section: Introductionmentioning

confidence: 99%

“…However, most of the current MI-EEG-based BCI research has focused on signal detection and classification of movements of the left and right hand, foot and tongue, while few studies have been conducted on the motor imagery EEG signals of specific local areas in a small range of limbs, such as five classes of fingers or more [18,19,20] . The nonlinearity and weakness [21] of the finger signals themselves and the flexion correlation [22,23,24] between fingers increase the difficulty of classifying individual finger signals.…”

Section: Introductionmentioning

confidence: 99%

Motor imagery classification of finger movements based on position subtask network learning

Luo¹,

Ye²,

Lu³

et al. 2023

International Conference on Signal Processing and Communication Technology (SPCT 2022)

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The multi-classification of finger movements is a difficult challenge at present. In this paper, a multi-task Siamese network is proposed. Based on the basic Siamese network architecture, this study designs the position relationships between sample pairs and constructs single-sample subtasks for the left and right input samples. The position relationships between samples not only additionally supplement the spatial factors between electroencephalogram (EEG) signals that facilitate the generalization performance of position network learning, but also turn the EEG signals set structured so that the multi-task Siamese network is used to effectively learn multiple relationships between samples. During the model testing phase, a balanced matching algorithm of single test sample VS multi-training sample is used to improve the robustness of the multi-task Siamese network model. The experimental results on the 5F motor imagery data set from The Largest SCP of Motor-Imagery, an EEG data signal repository, and data set4 of the 4th brain-computer interface (BCI) competition show that compared with the single-subtask Siamese network, EEGNet, ResNet-50 and the public experimental results of the original dataset, the multi-task Siamese network in this paper can obtain better multiclass motor imagery (MI) classification accuracy and can better realize the multi-classification of MI-EEG signals. This study could be helpful to promote the development of finger-based MI-BCI system.

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Analysis and Classification for Single-Trial EEG Induced by Sequential Finger Movements

Cited by 2 publications

References 15 publications

Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces

Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces

Motor imagery classification of finger movements based on position subtask network learning

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