IEEE Transactions on Biomedical Engineering

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Brain-Computer Interface-based Soft Robotic Glove Rehabilitation for Stroke
This paper presents the results of a study involving the use of a Brain-Computer Interface-based Soft Robotic Glove as a novel strategy in stroke rehabilitation. The technology uses the electroencephalogram signals from stroke patients to drive the assistive actions of the soft robotic glove to assist them in physically carrying out activities of daily living. The two-arm study showed prolonged improvements in FMA and ARAT scores although no significant intergroup differences were observed during the study. In addition, all of the patients in the BCI-SRG group also experienced a vivid kinesthetic illusion lasting beyond the active intervention period... Read more
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Inter-and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-based BCIs
Steady-state visual evoked potential (SSVEP)-based brain-computer interfaces (BCIs) that can deliver high information transfer rate (ITR) usually require subject’s calibration data to learn the class-and subject-specific model parameters (e.g. the spatial filters and SSVEP templates). Normally, the amount of the calibration data for learning is proportional to the number of classes (or visual stimuli), which could be huge and consequently lead to a time-consuming calibration... Read more
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On Error-Related Potentials During Sensorimotor-Based Brain-Computer Interface: Explorations With a Pseudo-Online Brain-Controlled Speller
Brain-computer interface (BCI) spelling is a promising communication solution for people in paralysis. Currently, BCIs suffer from imperfect decoding accuracy which calls for methods to handle spelling mistakes. Detecting error-related potentials (ErrPs) has been early identified as a potential remedy.... Read more
Articles, Published Articles
Reaction Time Predicts Brain-Computer Interface Aptitude
     There is evidence that 15–30% of the general population cannot effectively operate brain–computer interfaces (BCIs). Thus the BCI performance predictors are critically required to pre-screen participants. Current neurophysiological and psychological tests either require complicated equipment or suffer from subjectivity. Thus,... Read more
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Three-Dimensional Brain-Computer Interface Control through Simultaneous Overt Spatial Attentional and Motor Imagery Tasks
Brain-computer interfacing (BCI) is a promising method for providing alternative connections between the brain and the outside world in concert with natural connections or re-establishing natural limb movement in cases where these have been potentially disrupted by disease or injury.... Read more
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Decoding Covert Somatosensory Attention by a BCI System Calibrated With Tactile Sensation
Objective: We propose a novel calibration strategy to facilitate the decoding of covert somatosensory attention by exploring the oscillatory dynamics induced by tactile sensation. Methods: It was hypothesized that the similarity of the oscillatory pattern between stimulation sensation (SS, real... Read more
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Feasibility of Automatic Error Detect-and-Undo System in Human Intracortical Brain-Computer Interfaces
Brain-computer interfaces (BCIs) aim to help people with paralysis to improve their communication and independence. Intracortical BCIs (iBCIs) have shown promising results in pilot clinical trials. Despite the performance improvements over the last decades, BCI systems still make errors that... Read more
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Android Feedback-based Training modulates Sensorimotor Rhythms during Motor Imagery
   EEG-based brain computer interface (BCI) systems have demonstrated potential to assist patients with devastating motor paralysis conditions. However, there is great interest in shifting the BCI trend toward applications aimed at healthy users. Although BCI operation depends on technological factors... Read more
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BCI Use and Its Relation to Adaptation in Cortical Networks
    Brain-computer interfaces (BCIs) carry great potential in the treatment of motor impairments. As a new motor output, BCIs interface with the native motor system, but acquisition of BCI proficiency requires a degree of learning to integrate this new function. In... Read more
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Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG
      Simultaneous acquisition of brain activity signals from the sensorimotor area using NIRS combined with EEG, imagined hand clenching force and speed modulation of brain activity, as well as 6-class classification of these imagined motor parameters by NIRS-EEG were explored. Near... Read more