brain-computer interface

Enhancing ERD Activation and Functional Connectivity via the Sixth-Finger Motor Imagery in Stroke Patients
Enhancing ERD Activation and Functional Connectivity via the Sixth-Finger Motor Imagery in Stroke Patients 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Motor imagery (MI) is widely employed in stroke rehabilitation due to the event-related desynchronization (ERD) phenomenon in sensorimotor cortex induced by MI is similar to actual movement. However, the traditional… read more

Motor-Related EEG Analysis Using a Pole Tracking Approach
Motor-Related EEG Analysis Using a Pole Tracking Approach 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
This study introduces an alternative approach to electroencephalography (EEG) time-frequency analysis based on time-varying autoregressive (TV-AR) models in a cascade configuration to independently monitor key EEG spectral components. The method… read more

MASER: Enhancing EEG Spatial Resolution With State Space Modeling
MASER: Enhancing EEG Spatial Resolution With State Space Modeling 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Consumer-grade Electroencephalography (EEG) devices equipped with few electrodes often suffer from low spatial resolution, hindering the accurate capture of intricate brain activity patterns. To address this issue, we propose MASER,… read more

Effective Phoneme Decoding With Hyperbolic Neural Networks for High-Performance Speech BCIs
Effective Phoneme Decoding With Hyperbolic Neural Networks for High-Performance Speech BCIs 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

Objective: Speech brain-computer interfaces (speech BCIs), which convert brain signals into spoken words or sentences, have demonstrated great potential for high-performance BCI communication. Phonemes are the basic pronunciation units. For…

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Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network
Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
Decoding motor imagery (MI) using deep learning in cortical level has potential in brain computer interface based intelligent rehabilitation. However, a mass of dipoles is inconvenient to extract the personalized… read more

Brain-Computer-Spinal Interface Restores Upper Limb Function after Spinal Cord Injury
Author(s)3: Soshi Samejima, Abed Khorasani, Vaishnavi Ranganathan, Jared Nakahara, Nick M. Tolley, Adrien Boissenin, Vahid Shalchyan, Mohammad Reza Daliri, Joshua R. Smith, Chet T. Moritz
Brain-Computer-Spinal Interface Restores Upper Limb Function after Spinal Cord Injury 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

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…

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Decoding Finger Tapping with the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution
Author(s)3: Minji Lee, Ji-Hoon Jeong, Yun-Hee Kim, Seong-Whan Lee
Decoding Finger Tapping with the Affected Hand in Chronic Stroke Patients During Motor Imagery and Execution 150 150 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

In stroke rehabilitation, motor imagery based on a brain–computer interface is an extremely useful method to control an external device and utilize neurofeedback. Many studies have reported on the classification…

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Inter-and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-based BCIs
Author(s)3: Chi Man Wong, Ze Wang, Boyu Wang, Ka Fai Lao, Agostinho Rosa, Peng Xu, Tzyy-Ping Jung, C. L. Philip Chen, Feng Wan
Inter-and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-based BCIs 1000 920 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)
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

Android Feedback-based Training modulates Sensorimotor Rhythms during Motor Imagery
Author(s)3: Christian I. Penaloza, Maryam Alimardani, Shuichi Nishio
Android Feedback-based Training modulates Sensorimotor Rhythms during Motor Imagery 780 411 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

   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…

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BCI Use and Its Relation to Adaptation in Cortical Networks

BCI Use and Its Relation to Adaptation in Cortical Networks
Author(s)3: Kaitlyn Casimo, Kurt E. Weaver, Jeremiah Wander, Jeffrey G. Ojemann
BCI Use and Its Relation to Adaptation in Cortical Networks 780 520 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

    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…

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