Electroencephalogram (EEG)

EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset
EEG-Based Brain Functional Network Analysis for Differential Identification of Dementia-Related Disorders and Their Onset 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Diagnosing and treating dementia, including mild cognitive impairment (MCI), is challenging due to diverse disease types and overlapping symptoms. Early MCI detection is vital as it can precede dementia, yet… read more
Comparison of average ERD/ERS of younger and older participants in the alpha-beta frequency band (8–26 Hz).

Age-Related Changes in Vibro-Tactile EEG Response and Its Implications in BCI Applications: A Comparison Between Older and Younger Populations
Author(s)3: Mei Lin Chen, Dannie Fu, Jennifer Boger, Ning Jiang
Age-Related Changes in Vibro-Tactile EEG Response and Its Implications in BCI Applications: A Comparison Between Older and Younger Populations 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

    The rapid increase in the number of older adults around the world is accelerating research in applications to support age-related conditions, such as brain–computer interface (BCI) applications for post-stroke…

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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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Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG

Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG
Author(s)3: Yunfa Fu, Xin Xiong, Changhao Jiang, Baolei Xu, Yongcheng Li, Hongyi Li
Imagined Hand Clenching Force and Speed Modulate Brain Activity and Are Classified by NIRS Combined With EEG 780 303 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

      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…

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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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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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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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Sliding HDCA: Single-Trial EEG Classification to Overcome and Quantify Temporal Variability

Sliding HDCA: Single-Trial EEG Classification to Overcome and Quantify Temporal Variability
Sliding HDCA: Single-Trial EEG Classification to Overcome and Quantify Temporal Variability 556 235 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

A. Marathe, A. Ries, and K. McDowell ACCESS PAPER DATA READ FULL ARTICLE ON IEEE XPLORE Abstract Patterns of neural data obtained from electroencephalography (EEG) can be classified by machine learning techniques…

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L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI
L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI 556 235 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Y. Zhang, G. Zhou, J. Jin, M. Wang, X. Wang, and A. Cichocki   ACCESS PAPER DATA READ FULL ARTICLE ON IEEE XPLORE Abstract Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG)…

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TNSRE serves the community of biomedical engineers and clinical researchers who work at the intersection of neuroscience and physical medicine. We publish novel approaches and technologies for better understanding neural systems, human movement, and the relationships between them, with a focus on assistive devices that improve life for patients, for practicing clinicians, and for everyday use.

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