brain-computer interface (BCI)

Deep Neural Network-based Empirical Mode Decomposition for Motor Imagery EEG Classification
Deep Neural Network-based Empirical Mode Decomposition for Motor Imagery EEG Classification 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Motor imagery refers to the brain’s response during the mental simulation of physical activities, which can be detected through electroencephalogram (EEG) signals. However, EEG signals exhibit a low signal-to-noise ratio… read more

Learning Invariant Patterns Based on a Convolutional Neural Network and Big Electroencephalography Data for Subject-Independent P300 Brain-Computer Interfaces
Author(s)3: Wei Gao, Tianyou Yu, Jin-Gang Yu, Zhenghui Gu, Kendi Li, Yong Huang, Yuanqing Li, Zhu Liang Yu
Learning Invariant Patterns Based on a Convolutional Neural Network and Big Electroencephalography Data for Subject-Independent P300 Brain-Computer Interfaces 540 430 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

A brain-computer interface (BCI) measures and analyzes brain activity and converts this activity into computer commands to control external devices. In contrast to traditional BCIs that require a subject-specific calibration…

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Performance Improvement of Near-Infrared Spectroscopy-based Brain-Computer Interfaces Using Transcranial Near-Infrared Photobiomodulation with the Same Device
Author(s)3: Jinuk Kwon, Chang-Hwan Im
Performance Improvement of Near-Infrared Spectroscopy-based Brain-Computer Interfaces Using Transcranial Near-Infrared Photobiomodulation with the Same Device 548 128 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Transcranial near-infrared photobiomodulation (tNIR-PBM) can modulate physiological characteristics of the human brain, such as the cerebral blood flow and oxidative metabolism. Here, we investigated whether the performance of near-infrared spectroscopy (NIRS)-based brain-computer interfaces (BCIs) can be improved by tNIR-PBM applied to the prefrontal cortex with the same NIRS device. read more
A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces

A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces
Author(s)3: Yijun Wang, Xiaogang Chen, Xiaorong Gao, Shangkai Gao
A Benchmark Dataset for SSVEP-Based Brain-Computer Interfaces 691 389 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

    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…

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Open Access Dataset for EEG+NIRS Single-Trial Classification

Open Access Dataset for EEG+NIRS Single-Trial Classification
Author(s)3: Jaeyoung Shin, Alexander von Luhmann, Benjamin Blankertz, Do-Won Kim, Jichai Jeong, Han-Jeong Hwang, Klaus-Robert Müller
Open Access Dataset for EEG+NIRS Single-Trial Classification 780 476 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

       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…

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Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials

Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials
Author(s)3: Chuang Lin, Bing-Hui Wang, Ning Jiang, Ren Xu, Natalie Mrachacz-Kersting, Dario Farina
Discriminative Manifold Learning Based Detection of Movement-Related Cortical Potentials 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

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,…

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Movement Anticipation and EEG: Implications for BCI-Contingent Robot Therapy

Movement Anticipation and EEG: Implications for BCI-Contingent Robot Therapy
Author(s)3: Sumner L. Norman, Mark Dennison, Eric Wolbrecht, Steven C. Cramer, Ramesh Srinivasan, David J. Reinkensmeyer
Movement Anticipation and EEG: Implications for BCI-Contingent Robot Therapy 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

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…

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Evaluate the feasibility of using frontal SSVEP to implement an SSVEP – based BCI in Young, Elderly and ALS groups
Author(s)3: Hao-Teng Hsu, I-Hui Lee, Han-Ting Tsai, Chun-Yen Chang, Hsiang-Chih Chang, Chuan-Chih Hsu, Kuo-Kai Shyu, Hsiao-Huang Chang, Ting-Kuang Yeh, Po-Lei Lee
Evaluate the feasibility of using frontal SSVEP to implement an SSVEP – based BCI in Young, Elderly and ALS groups 780 310 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

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…

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Using Actual and Imagined Walking Related Desynchronization Features in a BCI
Author(s)3: Marianne Severens, Monica Perusquia-Hernandez, Bart Nienhuis, Jason Farquhar, Jacques Duysens
Using Actual and Imagined Walking Related Desynchronization Features in a BCI 780 275 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

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…

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FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing
Author(s)3: Ian Daly, Reinhold Scherer, Martin Billinger, Gernot Muller-Putz
FORCe: Fully Online and Automated Artifact Removal for Brain-Computer Interfacing 780 858 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

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…

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