Improving motor imagery BCI with user response to feedback-Reference-Cited by-同舟云学术

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Improving motor imagery BCI with user response to feedback

Published:2017-04-03 Issue:1-2 Volume:4 Page:74-86
ISSN:2326-263X
Container-title:Brain-Computer Interfaces
language:en
Short-container-title:Brain-Computer Interfaces

Author:

Mousavi Mahta¹,Koerner Adam S.²^ORCID,Zhang Qiong³,Noh Eunho¹,de Sa Virginia R.²⁴

Affiliation:

1. Department of Electrical and Computer Engineering, University of California, San Diego, La Jolla, CA, USA

2. Neurosciences Graduate Program, University of California, San Diego, La Jolla, CA, USA

3. Carnegie Mellon University, Pittsburgh, PA, USA

4. Department of Cognitive Science, University of California, San Diego, La Jolla, CA, USA

Funder

National Institutes of Health

National Science Foundation

UCSD FISP

Publisher

Informa UK Limited

Subject

Electrical and Electronic Engineering,Behavioral Neuroscience,Human-Computer Interaction,Biomedical Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/2326263X.2017.1303253

Reference30 articles.

1. A Comprehensive Survey of Brain Interface Technology Designs

2. Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks

3. The effects of self-movement, observation, and imagination on μ rhythms and readiness potentials (RP's): toward a brain-computer interface (BCI)

4. Motor imagery and direct brain-computer communication

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1. Bridging Minds and Machines: The Recent Advances of Brain-Computer Interfaces in Neurological and Neurosurgical Applications;World Neurosurgery;2024-09

2. Learning spatial-spectral-temporal EEG representations with dual-stream neural networks for motor imagery;Biomedical Signal Processing and Control;2024-06

3. Window-Adjusted Common Spatial Pattern for Detecting Error-Related Potentials in P300 BCI;Neural Processing Letters;2023-08-08

4. Review of public motor imagery and execution datasets in brain-computer interfaces;Frontiers in Human Neuroscience;2023-03-30

5. Enhancement of motor imagery training efficiency by an online adaptive training paradigm integrated with error related potential;Journal of Neural Engineering;2023-01-27

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