brain-computer interface

Brain-Computer-Spinal Interface Restores Upper Limb Function after Spinal Cord Injury

Author(s): 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): 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): 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): 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): 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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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): 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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A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation

A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation

Author(s): Lin Yao, Xinjun Sheng, Dingguo Zhang, Ning Jiang, Natalie Mrachacz-Kersting, Xiangyang Zhu, Dario Farina
A Stimulus-Independent Hybrid BCI Based on Motor Imagery and Somatosensory Attentional Orientation 780 435 Transactions on Neural Systems and Rehabilitation Engineering (TNSRE)

       Distinctive EEG signals from the motor and somatosensory cortex are generated during mental tasks of motor imagery (MI) and somatosensory attentional orientation (SAO). In this study, we hypothesize that…

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