EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study

Dong, Runlin; Zhang, Xiaodong; Li, Hanzhe; Masengo, Gilbert; Zhu, Aibin; Shi, Xiaojun; He, Chen

doi:10.3389/fnins.2023.1305850

Front. Neurosci.

2024

DOI: 10.3389/fnins.2023.1305850

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EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study

Runlin Dong,

Xiaodong Zhang,

Hanzhe Li

et al.

Abstract: IntroductionActive rehabilitation requires active neurological participation when users use rehabilitation equipment. A brain-computer interface (BCI) is a direct communication channel for detecting changes in the nervous system. Individuals with dyskinesia have unclear intentions to initiate movement due to physical or psychological factors, which is not conducive to detection. Virtual reality (VR) technology can be a potential tool to enhance the movement intention from pre-movement neural signals in clinica… Show more

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Cited by 3 publications

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Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals

Dong,

Zhang,

et al. 2024

Front. Bioeng. Biotechnol.

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Background and ObjectiveExoskeleton robot control should ideally be based on human voluntary movement intention. The readiness potential (RP) component of the motion-related cortical potential is observed before movement in the electroencephalogram and can be used for intention prediction. However, its single-trial features are weak and highly variable, and existing methods cannot achieve high cross-temporal and cross-subject accuracies in practical online applications. Therefore, this work aimed to combine a deep convolutional neural network (CNN) framework with a transfer learning (TL) strategy to predict the lower limb voluntary movement intention, thereby improving the accuracy while enhancing the model generalization capability; this would also provide sufficient processing time for the response of the exoskeleton robotic system and help realize robot control based on the intention of the human body.MethodsThe signal characteristics of the RP for lower limb movement were analyzed, and a parameter TL strategy based on CNN was proposed to predict the intention of voluntary lower limb movements. We recruited 10 subjects for offline and online experiments. Multivariate empirical-mode decomposition was used to remove the artifacts, and the moment of onset of voluntary movement was labeled using lower limb electromyography signals during network training.ResultsThe RP features can be observed from multiple data overlays before the onset of voluntary lower limb movements, and these features have long latency periods. The offline experimental results showed that the average movement intention prediction accuracy was 95.23% ± 1.25% for the right leg and 91.21% ± 1.48% for the left leg, which showed good cross-temporal and cross-subject generalization while greatly reducing the training time. Online movement intention prediction can predict results about 483.9 ± 11.9 ms before movement onset with an average accuracy of 82.75%.ConclusionThe proposed method has a higher prediction accuracy with a lower training time, has good generalization performance for cross-temporal and cross-subject aspects, and is well-prioritized in terms of the temporal responses; these features are expected to lay the foundation for further investigations on exoskeleton robot control.

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Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals

Dong,

Zhang,

et al. 2024

Front. Bioeng. Biotechnol.

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Swimtrans Net: a multimodal robotic system for swimming action recognition driven via Swin-Transformer

Chen,

Yue

2024

Front. Neurorobot.

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IntroductionCurrently, using machine learning methods for precise analysis and improvement of swimming techniques holds significant research value and application prospects. The existing machine learning methods have improved the accuracy of action recognition to some extent. However, they still face several challenges such as insufficient data feature extraction, limited model generalization ability, and poor real-time performance.MethodsTo address these issues, this paper proposes an innovative approach called Swimtrans Net: A multimodal robotic system for swimming action recognition driven via Swin-Transformer. By leveraging the powerful visual data feature extraction capabilities of Swin-Transformer, Swimtrans Net effectively extracts swimming image information. Additionally, to meet the requirements of multimodal tasks, we integrate the CLIP model into the system. Swin-Transformer serves as the image encoder for CLIP, and through fine-tuning the CLIP model, it becomes capable of understanding and interpreting swimming action data, learning relevant features and patterns associated with swimming. Finally, we introduce transfer learning for pre-training to reduce training time and lower computational resources, thereby providing real-time feedback to swimmers.Results and discussionExperimental results show that Swimtrans Net has achieved a 2.94% improvement over the current state-of-the-art methods in swimming motion analysis and prediction, making significant progress. This study introduces an innovative machine learning method that can help coaches and swimmers better understand and improve swimming techniques, ultimately improving swimming performance.

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CAM-Vtrans: real-time sports training utilizing multi-modal robot data

LinLin,

Sangheang,

GuanTing

2024

Front. Neurorobot.

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IntroductionAssistive robots and human-robot interaction have become integral parts of sports training. However, existing methods often fail to provide real-time and accurate feedback, and they often lack integration of comprehensive multi-modal data.MethodsTo address these issues, we propose a groundbreaking and innovative approach: CAM-Vtrans—Cross-Attention Multi-modal Visual Transformer. By leveraging the strengths of state-of-the-art techniques such as Visual Transformers (ViT) and models like CLIP, along with cross-attention mechanisms, CAM-Vtrans harnesses the power of visual and textual information to provide athletes with highly accurate and timely feedback. Through the utilization of multi-modal robot data, CAM-Vtrans offers valuable assistance, enabling athletes to optimize their performance while minimizing potential injury risks. This novel approach represents a significant advancement in the field, offering an innovative solution to overcome the limitations of existing methods and enhance the precision and efficiency of sports training programs.

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EEG generation mechanism of lower limb active movement intention and its virtual reality induction enhancement: a preliminary study

Cited by 3 publications

References 49 publications

Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals

Cross-domain prediction approach of human lower limb voluntary movement intention for exoskeleton robot based on EEG signals

Swimtrans Net: a multimodal robotic system for swimming action recognition driven via Swin-Transformer

CAM-Vtrans: real-time sports training utilizing multi-modal robot data

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