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      Brain Computer Interfaces, a Review

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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.

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          Most cited references 350

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          A review of classification algorithms for EEG-based brain–computer interfaces

          In this paper we review classification algorithms used to design brain-computer interface (BCI) systems based on electroencephalography (EEG). We briefly present the commonly employed algorithms and describe their critical properties. Based on the literature, we compare them in terms of performance and provide guidelines to choose the suitable classification algorithm(s) for a specific BCI.
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            Response of brain tissue to chronically implanted neural electrodes.

            Chronically implanted recording electrode arrays linked to prosthetics have the potential to make positive impacts on patients suffering from full or partial paralysis. Such arrays are implanted into the patient's cortical tissue and record extracellular potentials from nearby neurons, allowing the information encoded by the neuronal discharges to control external devices. While such systems perform well during acute recordings, they often fail to function reliably in clinically relevant chronic settings. Available evidence suggests that a major failure mode of electrode arrays is the brain tissue reaction against these implants, making the biocompatibility of implanted electrodes a primary concern in device design. This review presents the biological components and time course of the acute and chronic tissue reaction in brain tissue, analyses the brain tissue response of current electrode systems, and comments on the various material science and bioactive strategies undertaken by electrode designers to enhance electrode performance.
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              Brain-machine interfaces: past, present and future.

              Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm.
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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel, Switzerland)
                Molecular Diversity Preservation International (MDPI)
                1424-8220
                2012
                31 January 2012
                : 12
                : 2
                : 1211-1279
                Affiliations
                Department of Signal Theory, Communications and Telematics Engineering, University of Valladolid, Valladolid 47011, Spain; E-Mail: jgomgil@ 123456tel.uva.es
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: lnicalo@ 123456ribera.tel.uva.es ; Tel.: +34-690-357-486; Fax: +34-983-423-667.
                Article
                sensors-12-01211
                10.3390/s120201211
                3304110
                22438708
                © 2012 by the authors; licensee MDPI, Basel, Switzerland

                This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

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