Literature DB >> 15687805

Changing the P300 brain computer interface.

Jessica D Bayliss1, Samuel A Inverso, Aleksey Tentler.   

Abstract

Brain-computer interfaces (BCIs) are now feasible for use as an alternative control option for those with severe motor impairments. The P300 component of the evoked potential has proven useful as a control signal. Individuals do not need to be trained to produce the signal, and it is fairly stable and has a large evoked potential. Even with recent signal classification advances, on-line experiments with P300-based BCIs remain far from perfect. We present two potential methods for improving control accuracy. Experimental results in an evoked potential BCI, used to control items in a virtual apartment, show a reduced response exists when items are accidentally controlled. The presence of a P300-like signal in response to goal items means that it can be used for automatic error correction. Preliminary results from an interface experiment using three different button configurations for a yes/no BCI task show that the configuration of buttons may affect on-line signal classification. These results will be discussed in light of the special considerations needed when working with an amyotrophic lateral sclerosis (ALS) patient.

Entities:  

Mesh:

Year:  2004        PMID: 15687805     DOI: 10.1089/cpb.2004.7.694

Source DB:  PubMed          Journal:  Cyberpsychol Behav        ISSN: 1094-9313


  5 in total

1.  A new auditory multi-class brain-computer interface paradigm: spatial hearing as an informative cue.

Authors:  Martijn Schreuder; Benjamin Blankertz; Michael Tangermann
Journal:  PLoS One       Date:  2010-04-01       Impact factor: 3.240

2.  Online detection of P300 and error potentials in a BCI speller.

Authors:  Bernardo Dal Seno; Matteo Matteucci; Luca Mainardi
Journal:  Comput Intell Neurosci       Date:  2010-02-11

Review 3.  Hybrid Brain-Computer Interface Techniques for Improved Classification Accuracy and Increased Number of Commands: A Review.

Authors:  Keum-Shik Hong; Muhammad Jawad Khan
Journal:  Front Neurorobot       Date:  2017-07-24       Impact factor: 2.650

4.  The Brainarium: An Interactive Immersive Tool for Brain Education, Art, and Neurotherapy.

Authors:  Romain Grandchamp; Arnaud Delorme
Journal:  Comput Intell Neurosci       Date:  2016-09-06

5.  A conceptual space for EEG-based brain-computer interfaces.

Authors:  Nataliya Kosmyna; Anatole Lécuyer
Journal:  PLoS One       Date:  2019-01-03       Impact factor: 3.240

  5 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.