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| http://dx.doi.org/10.3389/fneng.2014.00042 | DOI Listing |
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Enhancing the performance of SSVEP-based BCIs by combining task-related component analysis and deep neural network.
Sci Rep
January 2025
Department of Biomedical Engineering, School of Biomedical Engineering, Tsinghua University, Beijing, 100084, China.
Steady-State Visually Evoked Potential (SSVEP) signals can be decoded by either a traditional machine learning algorithm or a deep learning network. Combining the two methods is expected to enhance the performance of an SSVEP-based brain-computer interface (BCI) by exploiting their advantages. However, an efficient strategy for integrating the two methods has not yet been established.
View Article and Find Full Text PDFAn fMRI study on the generalization of motor learning after brain actuated supernumerary robot training.
NPJ Sci Learn
December 2024
Academy of Medical Engineering and Translational Medicine (AMT), Tianjin University, Tianjin, China.
Article Synopsis
- Generalization is key in motor learning, yet research on BCI-actuated supernumerary robotic fingers (BCI-SRF) and their long-term neuroplastic effects is limited.
- In a study with 20 right-handed participants, the BCI-SRF group showed a 350% improvement in finger opposition accuracy compared to the traditional finger group after 4 weeks of training.
- This improvement was linked to increased functional connectivity in brain regions involved in motor control, suggesting that BCI-SRF training may enhance motor learning by reorganizing the sensorimotor network.
A hybrid network using transformer with modified locally linear embedding and sliding window convolution for EEG decoding.
J Neural Eng
January 2025
West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, People's Republic of China.
. Brain-computer interface(BCI) is leveraged by artificial intelligence in EEG signal decoding, which makes it possible to become a new means of human-machine interaction. However, the performance of current EEG decoding methods is still insufficient for clinical applications because of inadequate EEG information extraction and limited computational resources in hospitals.
View Article and Find Full Text PDFToward brain-inspired foundation model for EEG signal processing: our opinion.
Front Neurosci
December 2024
School of Intelligence Science and Technology, University of Science and Technology Beijing, Beijing, China.
Delayed knowledge transfer: Cross-modal knowledge transfer from delayed stimulus to EEG for continuous attention detection based on spike-represented EEG signals.
Neural Netw
December 2024
WAVES Research Group, Department of Information Technology, Ghent University, Gent, Belgium. Electronic address:
Decoding visual and auditory stimuli from brain activities, such as electroencephalography (EEG), offers promising advancements for enhancing machine-to-human interaction. However, effectively representing EEG signals remains a significant challenge. In this paper, we introduce a novel Delayed Knowledge Transfer (DKT) framework that employs spiking neurons for attention detection, using our experimental EEG dataset.
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