Cargando…

User’s Self-Prediction of Performance in Motor Imagery Brain–Computer Interface

Performance variation is a critical issue in motor imagery brain–computer interface (MI-BCI), and various neurophysiological, psychological, and anatomical correlates have been reported in the literature. Although the main aim of such studies is to predict MI-BCI performance for the prescreening of...

Descripción completa

Detalles Bibliográficos
Autores principales:	Ahn, Minkyu, Cho, Hohyun, Ahn, Sangtae, Jun, Sung C.
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	Frontiers Media S.A. 2018
Materias:	Neuroscience
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5818431/ https://www.ncbi.nlm.nih.gov/pubmed/29497370 http://dx.doi.org/10.3389/fnhum.2018.00059

Ejemplares similares

EEG datasets for motor imagery brain–computer interface
por: Cho, Hohyun, et al.
Publicado: (2017)

High Theta and Low Alpha Powers May Be Indicative of BCI-Illiteracy in Motor Imagery
por: Ahn, Minkyu, et al.
Publicado: (2013)

Review of public motor imagery and execution datasets in brain-computer interfaces
por: Gwon, Daeun, et al.
Publicado: (2023)

Editorial: Deep Learning in Brain-Computer Interface
por: Ahn, Minkyu, et al.
Publicado: (2022)

Corrigendum: Review of public motor imagery and execution datasets in brain-computer interfaces
por: Gwon, Daeun, et al.
Publicado: (2023)

Ergonomic Issues in Brain-Computer Interface Technologies: Current Status, Challenges, and Future Direction
por: Baek, Hyun Jae, et al.
Publicado: (2020)

Gamma band activity associated with BCI performance: simultaneous MEG/EEG study
por: Ahn, Minkyu, et al.
Publicado: (2013)

Vividness of Visual Imagery and Personality Impact Motor-Imagery Brain Computer Interfaces
por: Leeuwis, Nikki, et al.
Publicado: (2021)

Steady-State Somatosensory Evoked Potential for Brain-Computer Interface—Present and Future
por: Ahn, Sangtae, et al.
Publicado: (2016)

Multi-Modal Integration of EEG-fNIRS for Brain-Computer Interfaces – Current Limitations and Future Directions
por: Ahn, Sangtae, et al.
Publicado: (2017)

Improving Motor Imagery-Based Brain-Computer Interface Performance Based on Sensory Stimulation Training: An Approach Focused on Poorly Performing Users
por: Park, Sangin, et al.
Publicado: (2021)

Effects of Gaze Fixation on the Performance of a Motor Imagery-Based Brain-Computer Interface
por: Meng, Jianjun, et al.
Publicado: (2022)

Multi-Class Motor Imagery EEG Decoding for Brain-Computer Interfaces
por: Wang, Deng, et al.
Publicado: (2012)

An Impending Paradigm Shift in Motor Imagery Based Brain-Computer Interfaces
por: Papadopoulos, Sotirios, et al.
Publicado: (2022)

Neurofeedback Training of Alpha Relative Power Improves the Performance of Motor Imagery Brain-Computer Interface
por: Zhou, Qing, et al.
Publicado: (2022)

Corrigendum: Multi-Modal Integration of EEG-fNIRS for Brain-Computer Interfaces – Current Limitations and Future Directions
por: Ahn, Sangtae, et al.
Publicado: (2021)

Functional Connectivity Analysis in Motor-Imagery Brain Computer Interfaces
por: Leeuwis, Nikki, et al.
Publicado: (2021)

Effects of Frontal Theta Rhythms in a Prior Resting State on the Subsequent Motor Imagery Brain-Computer Interface Performance
por: Kang, Jae-Hwan, et al.
Publicado: (2021)

Optimization of Task Allocation for Collaborative Brain–Computer Interface Based on Motor Imagery
por: Gu, Bin, et al.
Publicado: (2021)

Transformed common spatial pattern for motor imagery-based brain-computer interfaces
por: Ma, Zhen, et al.
Publicado: (2023)

Increased motor cortex excitability during motor imagery in brain-computer interface trained subjects
por: Mokienko, Olesya A., et al.
Publicado: (2013)

Closed-loop motor imagery EEG simulation for brain-computer interfaces
por: Shin, Hyonyoung, et al.
Publicado: (2022)

Closed-Loop Phase-Dependent Vibration Stimulation Improves Motor Imagery-Based Brain-Computer Interface Performance
por: Zhang, Wenbin, et al.
Publicado: (2021)

Non-motor tasks improve adaptive brain-computer interface performance in users with severe motor impairment
por: Faller, Josef, et al.
Publicado: (2014)

Corrigendum: Neurofeedback training of alpha relative power improves the performance of motor imagery brain-computer interface
por: Zhou, Qing, et al.
Publicado: (2022)

A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users
por: Ahn, Minkyu, et al.
Publicado: (2014)

A Synchronous Motor Imagery Based Neural Physiological Paradigm for Brain Computer Interface Speller
por: Cao, Lei, et al.
Publicado: (2017)

A Novel Transfer Support Matrix Machine for Motor Imagery-Based Brain Computer Interface
por: Chen, Yan, et al.
Publicado: (2020)

The Differences Between Motor Attempt and Motor Imagery in Brain-Computer Interface Accuracy and Event-Related Desynchronization of Patients With Hemiplegia
por: Chen, Shugeng, et al.
Publicado: (2021)

Deep Learning Based Inter-subject Continuous Decoding of Motor Imagery for Practical Brain-Computer Interfaces
por: Roy, Sujit, et al.
Publicado: (2020)

Progressive Training for Motor Imagery Brain-Computer Interfaces Using Gamification and Virtual Reality Embodiment
por: Škola, Filip, et al.
Publicado: (2019)

Improved Brain–Computer Interface Signal Recognition Algorithm Based on Few-Channel Motor Imagery
por: Wang, Fan, et al.
Publicado: (2022)

Motor imagery brain–computer interface rehabilitation system enhances upper limb performance and improves brain activity in stroke patients: A clinical study
por: Liao, Wenzhe, et al.
Publicado: (2023)

On the Better Performance of Pianists with Motor Imagery-Based Brain-Computer Interface Systems
por: Riquelme-Ros, José-Vicente, et al.
Publicado: (2020)

Selective Cross-Subject Transfer Learning Based on Riemannian Tangent Space for Motor Imagery Brain-Computer Interface
por: Xu, Yilu, et al.
Publicado: (2021)

Individually Adapted Imagery Improves Brain-Computer Interface Performance in End-Users with Disability
por: Scherer, Reinhold, et al.
Publicado: (2015)

A large EEG database with users’ profile information for motor imagery brain-computer interface research
por: Dreyer, Pauline, et al.
Publicado: (2023)

Single Trial Predictors for Gating Motor-Imagery Brain-Computer Interfaces Based on Sensorimotor Rhythm and Visual Evoked Potentials
por: Geronimo, Andrew, et al.
Publicado: (2016)

Decoding EEG rhythms offline and online during motor imagery for standing and sitting based on a brain-computer interface
por: Triana-Guzman, Nayid, et al.
Publicado: (2022)

Electroencephalography-Based Brain–Computer Interface Motor Imagery Classification
por: Mohammadi, Ehsan, et al.
Publicado: (2021)

Cannot write session to /tmp/vufind_sessions/sess_nfeuisbucm0p2cstki8ekh9qbv