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Brain-computer interface (BCI) has high application value in the field of healthcare. However, in practical clinical applications, convenience and system performance should be considered in the use of BCI. Wearable BCIs are generally with high convenience, but their performance in real-life scenario needs to be evaluated. This study proposed a wearable steady-state visual evoked potential (SSVEP)-based BCI system equipped with a small-sized electroencephalogram (EEG) collector and a high-performance training-free decoding algorithm. Ten healthy subjects participated in the test of BCI system under simplified experimental preparation. The results showed that the average classification accuracy of this BCI was 94.10% for 40 targets, and there was no significant difference compared to the dataset collected under the laboratory condition. The system achieved a maximum information transfer rate (ITR) of 115.25 bit/min with 8-channel signal and 98.49 bit/min with 4-channel signal, indicating that the 4-channel solution can be used as an option for the few-channel BCI. Overall, this wearable SSVEP-BCI can achieve good performance in real-life scenario, which helps to promote BCI technology in clinical practice.
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http://dx.doi.org/10.7507/1001-5515.202310069 | DOI Listing |
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi
August 2025
School of Mechanical Engineering, North University of China, Taiyuan 030051, P. R. China.
In order to characterize the characteristics of pathological tremor of human upper limb, a simulation system of pathological tremor of human arm was provided and its dynamic response was analyzed. Firstly, in this study, a two-degree-of-freedom human arm dynamic model was established and linearized according to the arbitrary initial angle of joints. After solving the analytical solutions of steady-state responses of the joints, the numerical solution was used to verify it.
View Article and Find Full Text PDFSci Data
July 2025
Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, 300072, China.
Steady-state visually evoked potential (SSVEP)-based brain-computer interfaces (BCIs) have shown significant promise for practical applications. The integration of SSVEP-BCIs with head-mounted augmented-reality (AR) displays is expected to foster wearable, portable systems; nevertheless, empirical resources for such configurations are scarce, especially for paradigms employing innovative stimulation paradigms. Here we present a curated SSVEP dataset recorded with a binocular AR headset that independently modulates the visual input to each eye and a lightweight electroencephalography recorder.
View Article and Find Full Text PDFJ Neural Eng
August 2025
School of Electronic and Electrical Engineering, Institute of Robotics, Autonomous Systems and Sensing, University of Leeds, LS2 9JT Leeds, United Kingdom.
Few-shot steady-state visual evoked potential (SSVEP) detection remains a major challenge in brain-computer interface (BCI) systems, as limited calibration data often leads to degraded performance. This study aims to enhance few-shot SSVEP detection through an effective data augmentation (DA) strategy.We propose a mixup-based DA method that generates synthetic trials by linearly interpolating between real SSVEP signals extracted using a sliding window strategy.
View Article and Find Full Text PDFProc Natl Acad Sci U S A
July 2025
Elmore Family School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47906.
Since the 1960s, enzymatic sensors have been vital in healthcare and environmental monitoring due to their high selectivity. Traditionally, their performance is interpreted using the Michaelis-Menten (MM) equation, which assumes idealized, homogeneous, well-mixed laboratory conditions. However, integrating these sensors with microneedle (MN) patches for wearable applications introduces challenges such as spatial and temporal variations and limited reactant availability.
View Article and Find Full Text PDFISA Trans
July 2025
College of Engineering and Technology, Jilin Agricultural University, Changchun130118, China. Electronic address:
This study presents an Adaptive Sliding Mode Controller enhanced by an Improved Sparrow Search Algorithm (ISSA-SMC) for accurate motion tracking of lower-limb assistive exoskeletons. By incorporating human joint torque inputs into the exoskeleton's dynamic model, ISSA-SMC achieves real-time adaptation to user variability, external disturbances, and modeling uncertainties. A softmax strategy combined with the branch and bound method efficiently optimizes controller parameters, enhancing tracking accuracy and robustness with low computational cost.
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