Article

Steady-State VEP-Based Brain-Computer Interface Control in an Immersive 3D Gaming Environment.

EURASIP J. Adv. Sig. Proc 01/2005; 2005:3156-3164.
Source: DBLP
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    Article: Brain computer interfaces, a review.
    Luis Fernando Nicolas-Alonso, Jaime Gomez-Gil
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    ABSTRACT: A brain-computer interface (BCI) is a hardware and software communications system that permits cerebral activity alone to control computers or external devices. The immediate goal of BCI research is to provide communications capabilities to severely disabled people who are totally paralyzed or 'locked in' by neurological neuromuscular disorders, such as amyotrophic lateral sclerosis, brain stem stroke, or spinal cord injury. Here, we review the state-of-the-art of BCIs, looking at the different steps that form a standard BCI: signal acquisition, preprocessing or signal enhancement, feature extraction, classification and the control interface. We discuss their advantages, drawbacks, and latest advances, and we survey the numerous technologies reported in the scientific literature to design each step of a BCI. First, the review examines the neuroimaging modalities used in the signal acquisition step, each of which monitors a different functional brain activity such as electrical, magnetic or metabolic activity. Second, the review discusses different electrophysiological control signals that determine user intentions, which can be detected in brain activity. Third, the review includes some techniques used in the signal enhancement step to deal with the artifacts in the control signals and improve the performance. Fourth, the review studies some mathematic algorithms used in the feature extraction and classification steps which translate the information in the control signals into commands that operate a computer or other device. Finally, the review provides an overview of various BCI applications that control a range of devices.
    Sensors 01/2012; 12(2):1211-79. · 1.74 Impact Factor
  • Article: Complex-valued spatial filters for SSVEP-based BCIs with phase coding.
    Owen Falzon, Kenneth Camilleri, Joseph Muscat
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    ABSTRACT: Brain-computer interface (BCI) systems based on steady-state visual evoked potentials (SSVEPs) have gained considerable popularity because of the robustness and high information transfer rate these can provide. Typical SSVEP setups make use of visual targets flashing at different frequencies, where a user's choice is determined from the SSVEPs elicited by the user gazing at a specific target. The range of stimulus frequencies available for such setups is limited by a variety of factors, including the strength of the evoked potentials as well as user comfort and safety with light stimuli flashing at those frequencies. One way to tackle this limitation is by introducing targets flickering at the same frequency but with different phases. In this paper, we propose the use of the analytic common spatial patterns (ACSPs) method to discriminate between phase coded SSVEP targets, and we demonstrate that the complex-valued spatial filters used for discrimination can exceed the performance of existing techniques. Furthermore, the ACSP method also yields a set of spatial patterns, separable into amplitude and phase components, that provide insight into the underlying brain activity.
    IEEE transactions on bio-medical engineering 06/2012; 59(9):2486-95. · 2.15 Impact Factor
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    Chapter: Online BCI Implementation of High-Frequency Phase Modulated Visual Stimuli
    Danhua Zhu, Gary Garcia-Molina, Vojkan Mihajlović, Ronald M. Aarts
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    ABSTRACT: Brain computer interfaces (BCI) that use the steady-state-visual-evoked-potential (SSVEP) as neural source, offer two main advantages over other types of BCIs: shorter calibration times and higher information transfer rates. SSVEPs elicited by high frequency (larger than 30 Hz) repetitive visual stimulation are less prone to cause visual fatigue, safer, and more comfortable for the user. However in the high frequency range there is a practical limitation because only few frequencies can elicit sufficiently strong SSVEPs for BCI purposes. We bypass this limitation by using only one stimulation frequency and different phases. To detect the phase from the recorded SSVEP, we use spatial filtering combined to phase synchrony analysis. We developed an online BCI implementation which was tested on six subjects and resulted on an average accuracy of 95.5% and an average bit rate of 34 bits-per-minute. Our approach has the advantage of entailing only minimal visual annoyance for the user.
    06/2011: pages 645-654;

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