Determination of trajectories using non-invasive bci techniques in 3d environments

Teodoro García-Egea, Carlos Alberto Díaz-Hernández, Juan López-Coronado, Jose L. Contreras Vidal

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The feasibility of continuous decoding of self-initiated, self-selected hand movements to three-dimensional (3D) spatial targets from scalp electroencephalography (EEG) using linear decoders has been recently demonstrated. In this paper, we show that it is possible to train linear classifiers to decode hand movement direction to eight 3D spatial targets, in both planning and movement windows, using only the fluctuations in the amplitude of smoothed low-frequency signals from high-density scalp EEG. Taken together these results support the design of brain-computer interfaces (BCI) based on non-invasive scalp EEG signals and suggest that the current perception of the limits of EEG as a source signal for BCI applications merits further examination.

Original languageEnglish (US)
Title of host publicationNature-Inspired Mobile Robotics
Subtitle of host publicationProceedings of the 16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2013
Pages291-300
Number of pages10
StatePublished - Dec 1 2013
Event16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2013 - Sydney, NSW, Australia
Duration: Jul 14 2013Jul 17 2013

Other

Other16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile Machines, CLAWAR 2013
CountryAustralia
CitySydney, NSW
Period7/14/137/17/13

ASJC Scopus subject areas

  • Artificial Intelligence
  • Human-Computer Interaction

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