Literature DB >> 22713543

High γ-power predicts performance in sensorimotor-rhythm brain-computer interfaces.

Moritz Grosse-Wentrup1, Bernhard Schölkopf.   

Abstract

Subjects operating a brain-computer interface (BCI) based on sensorimotor rhythms exhibit large variations in performance over the course of an experimental session. Here, we show that high-frequency γ-oscillations, originating in fronto-parietal networks, predict such variations on a trial-to-trial basis. We interpret this finding as empirical support for an influence of attentional networks on BCI performance via modulation of the sensorimotor rhythm.

Mesh:

Year:  2012        PMID: 22713543     DOI: 10.1088/1741-2560/9/4/046001

Source DB:  PubMed          Journal:  J Neural Eng        ISSN: 1741-2552            Impact factor:   5.379


  23 in total

1.  Learning in brain-computer interface control evidenced by joint decomposition of brain and behavior.

Authors:  Jennifer Stiso; Marie-Constance Corsi; Jean M Vettel; Javier Garcia; Fabio Pasqualetti; Fabrizio De Vico Fallani; Timothy H Lucas; Danielle S Bassett
Journal:  J Neural Eng       Date:  2020-07-24       Impact factor: 5.379

2.  Predicting Mental Imagery-Based BCI Performance from Personality, Cognitive Profile and Neurophysiological Patterns.

Authors:  Camille Jeunet; Bernard N'Kaoua; Sriram Subramanian; Martin Hachet; Fabien Lotte
Journal:  PLoS One       Date:  2015-12-01       Impact factor: 3.240

3.  Workshops of the Fifth International Brain-Computer Interface Meeting: Defining the Future.

Authors:  Jane E Huggins; Christoph Guger; Brendan Allison; Charles W Anderson; Aaron Batista; Anne-Marie A-M Brouwer; Clemens Brunner; Ricardo Chavarriaga; Melanie Fried-Oken; Aysegul Gunduz; Disha Gupta; Andrea Kübler; Robert Leeb; Fabien Lotte; Lee E Miller; Gernot Müller-Putz; Tomasz Rutkowski; Michael Tangermann; David Edward Thompson
Journal:  Brain Comput Interfaces (Abingdon)       Date:  2014-01

4.  Benefits of deep learning classification of continuous noninvasive brain-computer interface control.

Authors:  James R Stieger; Stephen A Engel; Daniel Suma; Bin He
Journal:  J Neural Eng       Date:  2021-06-09       Impact factor: 5.043

5.  Reinforcement learning for adaptive threshold control of restorative brain-computer interfaces: a Bayesian simulation.

Authors:  Robert Bauer; Alireza Gharabaghi
Journal:  Front Neurosci       Date:  2015-02-12       Impact factor: 4.677

6.  Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training.

Authors:  Matthias Witte; Silvia Erika Kober; Manuel Ninaus; Christa Neuper; Guilherme Wood
Journal:  Front Hum Neurosci       Date:  2013-08-15       Impact factor: 3.169

7.  Gamma band activity associated with BCI performance: simultaneous MEG/EEG study.

Authors:  Minkyu Ahn; Sangtae Ahn; Jun H Hong; Hohyun Cho; Kiwoong Kim; Bong S Kim; Jin W Chang; Sung C Jun
Journal:  Front Hum Neurosci       Date:  2013-12-06       Impact factor: 3.169

8.  A brain-computer-interface for the detection and modulation of gamma band activity.

Authors:  Neda Salari; Michael Rose
Journal:  Brain Sci       Date:  2013-11-18

9.  A fully integrated wireless system for intracranial direct cortical stimulation, real-time electrocorticography data transmission, and smart cage for wireless battery recharge.

Authors:  Marco Piangerelli; Marco Ciavarro; Antonino Paris; Stefano Marchetti; Paolo Cristiani; Cosimo Puttilli; Napoleon Torres; Alim Louis Benabid; Pantaleo Romanelli
Journal:  Front Neurol       Date:  2014-08-25       Impact factor: 4.003

10.  Python Executable Script for Estimating Two Effective Parameters to Individualize Brain-Computer Interfaces: Individual Alpha Frequency and Neurophysiological Predictor.

Authors:  Luz María Alonso-Valerdi
Journal:  Front Neuroinform       Date:  2016-06-22       Impact factor: 4.081

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