IEEE Transactions on Biomedical Engineering

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Align and Pool for EEG Headset Domain Adaptation (ALPHA) to Facilitate Dry Electrode Based SSVEP-BCI
This study leverages transfer learning to improve the performance for steady-state visual evoked potential based brain-computer interface (SSVEP-BCI) implemented by dry electrodes. We utilize auxiliary individual electroencephalogram (EEG) recorded from wet electrode for cross-device transfer learning via the proposed framework named ALign and Pool for EEG Headset domain Adaptation (ALPHA), which aligns the SSVEP features by domain adaptation. ALPHA significantly outperformed the competing methods in two transfer directions, and boosted the dry-electrode systems using wet-electrode EEG. The cross-device transfer learning by ALPHA could increase the utility and potentially promote the use of dry electrode based SSVEP-BCIs in practical applications... Read more
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Brain-Computer-Spinal Interface Restores Upper Limb Function after Spinal Cord Injury
Brain-computer interfaces (BCIs) are an emerging strategy for spinal cord injury (SCI) intervention that may be used to reanimate paralyzed limbs. This approach requires decoding movement intention from the brain to control movement-evoking stimulation. Common decoding methods use spike-sorting and... Read more
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Fast EEG-based decoding of the directional focus of auditory attention using common spatial patterns
Current hearing devices lack information about the sound source a user attends to when there are multiple speakers. Auditory attention decoding (AAD) algorithms, which decode the auditory attention from brain signals, solve this problem and inform the hearing device about the to-be-enhanced speaker. While current AAD algorithms typically require an EEG buffer of 10s, leading to long delays, we present a new fast and accurate AAD algorithm that decodes the spatial focus of auditory attention in 1s using common spatial pattern filtering... Read more
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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