Literature DB >> 10896187

Parallel man-machine training in development of EEG-based cursor control.

A Kostov1, M Polak.   

Abstract

A new parallel man-machine training approach to brain-computer interface (BCI) succeeded through a unique application of machine learning methods. The BCI system could train users to control an animated cursor on the computer screen by voluntary electroencephalogram (EEG) modulation. Our BCI system requires only two to four electrodes, and has a relatively short training time for both the user and the machine. Moving the cursor in one dimension, our subjects were able to hit 100% of randomly selected targets, while in two dimensions, accuracies of approximately 63% and 76% was achieved with our two subjects.

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Year:  2000        PMID: 10896187     DOI: 10.1109/86.847816

Source DB:  PubMed          Journal:  IEEE Trans Rehabil Eng        ISSN: 1063-6528


  14 in total

1.  Robust extraction of P300 using constrained ICA for BCI applications.

Authors:  Ozair Idris Khan; Faisal Farooq; Faraz Akram; Mun-Taek Choi; Seung Moo Han; Tae-Seong Kim
Journal:  Med Biol Eng Comput       Date:  2012-01-17       Impact factor: 2.602

2.  Microscale recording from human motor cortex: implications for minimally invasive electrocorticographic brain-computer interfaces.

Authors:  Eric C Leuthardt; Zac Freudenberg; David Bundy; Jarod Roland
Journal:  Neurosurg Focus       Date:  2009-07       Impact factor: 4.047

3.  Trained modulation of sensorimotor rhythms can affect reaction time.

Authors:  C B Boulay; W A Sarnacki; J R Wolpaw; D J McFarland
Journal:  Clin Neurophysiol       Date:  2011-03-15       Impact factor: 3.708

4.  Clinical Applications of Brain-Computer Interfaces: Current State and Future Prospects.

Authors:  Joseph N Mak; Jonathan R Wolpaw
Journal:  IEEE Rev Biomed Eng       Date:  2009

5.  Prediction of subjective ratings of emotional pictures by EEG features.

Authors:  Dennis J McFarland; Muhammad A Parvaz; William A Sarnacki; Rita Z Goldstein; Jonathan R Wolpaw
Journal:  J Neural Eng       Date:  2016-12-09       Impact factor: 5.379

6.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans.

Authors:  Jonathan R Wolpaw; Dennis J McFarland
Journal:  Proc Natl Acad Sci U S A       Date:  2004-12-07       Impact factor: 11.205

7.  Learning algorithms for human-machine interfaces.

Authors:  Zachary Danziger; Alon Fishbach; Ferdinando A Mussa-Ivaldi
Journal:  IEEE Trans Biomed Eng       Date:  2009-02-06       Impact factor: 4.538

8.  Effects of training pre-movement sensorimotor rhythms on behavioral performance.

Authors:  Dennis J McFarland; William A Sarnacki; Jonathan R Wolpaw
Journal:  J Neural Eng       Date:  2015-11-03       Impact factor: 5.379

9.  Feature Selection Applying Statistical and Neurofuzzy Methods to EEG-Based BCI.

Authors:  Juan-Antonio Martinez-Leon; Jose-Manuel Cano-Izquierdo; Julio Ibarrola
Journal:  Comput Intell Neurosci       Date:  2015-04-21

10.  A Biologically Inspired Approach to Frequency Domain Feature Extraction for EEG Classification.

Authors:  Nurhan Gursel Ozmen; Levent Gumusel; Yuan Yang
Journal:  Comput Math Methods Med       Date:  2018-01-23       Impact factor: 2.238

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