Determination of trajectories using non-invasive BCI techniques in 3D environments

Teodoro García-Egea; Carlos Alberto Díaz-Hernández; Juan López-Coronado; José L. Contreras-Vidal

doi:10.1142/9789814525534_0038

Determination of trajectories using non-invasive BCI techniques in 3D environments

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

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English (US)
Title of host publication	Nature-Inspired Mobile Robotics
Publisher	World Scientific Publishing Co.
Pages	291-300
Number of pages	10
ISBN (Electronic)	9789814525534
ISBN (Print)	9789814525527
DOIs	https://doi.org/10.1142/9789814525534_0038
State	Published - Jan 1 2013

ASJC Scopus subject areas

Computer Science(all)
Engineering(all)

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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.",

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AU - López-Coronado, Juan

AU - Contreras-Vidal, José L.

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AB - 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.

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Determination of trajectories using non-invasive BCI techniques in 3D environments

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