IEEE Transactions on Biomedical Engineering

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Improving Performance of Devanagari Script Input-Based P300 Speller Using Deep Learning
  In P300 based BCI systems, eliciting ERP using the oddball stimulation will conceal the original P300 components in EEG signal. Therefore, it requires accurate detection of P300 components to precisely recognize the characters. For that purpose, conventional machine learning and... Read more
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A Brain–Computer Interface Based on Miniature-Event-Related Potentials Induced by Very Small Lateral Visual Stimuli
Traditional visual brain-computer interfaces (BCIs) was faced with the dilemma of whether or not to use large-size visual stimuli to code instructions. On the one hand, to overcome the noisy electroencephalography (EEG) environment, scientists preferred to use strong visual stimuli... Read more
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A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces
    This paper presents a benchmark steady-state visual evoked potential (SSVEP) dataset acquired with a 40-target brain-computer interface (BCI) speller. The dataset consists of 64-channel Electroencephalogram (EEG) data from 35 healthy subjects (8 experienced and 27 naïve) while they performed a... Read more
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Open Access Dataset for EEG+NIRS Single-Trial Classification
       We provide an open access dataset for hybrid brain-computer interfaces (BCIs) using electroencephalography (EEG) and near-infrared spectroscopy (NIRS). For this, we conducted two BCI experiments (left vs. right hand motor imagery; mental arithmetic vs. resting state). The dataset was validated... Read more
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A BCI-based Environmental Control System for Patients with Severe Spinal Cord Injuries
An important application of EEG-based brain-computer interface (BCI) is environmental control, which can improve the quality of life for paralyzed patients. However, most of the existing BCI-based environmental control systems were designed for the control of a single device without... Read more
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Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials
The detection of voluntary motor intention from EEG has been applied to closed-loop brain–computer interfacing (BCI). The movement-related cortical potential (MRCP) is a low frequency component of the EEG signal, which represents movement intention, preparation, and execution. In this study,... Read more
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Movement Anticipation and EEG: Implications for BCI-Contingent Robot Therapy
Brain-computer interfacing is a technology that has the potential to improve patient engagement in robot-assisted rehabilitation therapy. For example, movement intention reduces mu (8-13 Hz) oscillation amplitude over the sensorimotor cortex, a phenomenon referred to as event-related desynchronization (ERD). In... Read more
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Evaluate the feasibility of using frontal SSVEP to implement an SSVEP – based BCI in Young, Elderly and ALS groups
This paper studied the amplitude-frequency characteristic of frontal steady-state visual evoked potential (SSVEP) and its feasibility as a control signal for brain computer interface (BCI). SSVEPs induced by different stimulation frequencies, from 13 ~ 31 Hz in 2 Hz steps,... Read more
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Using Actual and Imagined Walking Related Desynchronization Features in a BCI
Recently, brain–computer interface (BCI) research has extended to investigate its possible use in motor rehabilitation. Most of these investigations have focused on the upper body. Only few studies consider gait because of the difficulty of recording EEG during gross movements.... Read more
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FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing
A fully automated and online artifact removal method for the electroencephalogram (EEG) is developed for use in braincomputer interfacing (BCI). The method (FORCe) is based upon a novel combination of wavelet decomposition, independent component analysis, and thresholding. FORCe is able... Read more