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      Decoding of four movement directions using hybrid NIRS-EEG brain-computer interface

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          Abstract

          The hybrid brain-computer interface (BCI)'s multimodal technology enables precision brain-signal classification that can be used in the formulation of control commands. In the present study, an experimental hybrid near-infrared spectroscopy-electroencephalography (NIRS-EEG) technique was used to extract and decode four different types of brain signals. The NIRS setup was positioned over the prefrontal brain region, and the EEG over the left and right motor cortex regions. Twelve subjects participating in the experiment were shown four direction symbols, namely, “forward,” “backward,” “left,” and “right.” The control commands for forward and backward movement were estimated by performing arithmetic mental tasks related to oxy-hemoglobin (HbO) changes. The left and right directions commands were associated with right and left hand tapping, respectively. The high classification accuracies achieved showed that the four different control signals can be accurately estimated using the hybrid NIRS-EEG technology.

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          Most cited references51

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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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            10/20, 10/10, and 10/5 systems revisited: their validity as relative head-surface-based positioning systems.

            With the advent of multi-channel EEG hardware systems and the concurrent development of topographic and tomographic signal source localization methods, the international 10/20 system, a standard system for electrode positioning with 21 electrodes, was extended to higher density electrode settings such as 10/10 and 10/5 systems, allowing more than 300 electrode positions. However, their effectiveness as relative head-surface-based positioning systems has not been examined. We previously developed a virtual 10/20 measurement algorithm that can analyze any structural MR head and brain image. Extending this method to the virtual 10/10 and 10/5 measurement algorithms, we analyzed the MR images of 17 healthy subjects. The acquired scalp positions of the 10/10 and 10/5 systems were normalized to the Montreal Neurological Institute (MNI) stereotactic coordinates and their spatial variability was assessed. We described and examined the effects of spatial variability due to the selection of positioning systems and landmark placement strategies. As long as a detailed rule for a particular system was provided, it yielded precise landmark positions on the scalp. Moreover, we evaluated the effective spatial resolution of 329 scalp landmark positions of the 10/5 system for multi-subject studies. As long as a detailed rule for landmark setting was provided, 241 scalp positions could be set effectively when there was no overlapping of two neighboring positions. Importantly, 10/10 positions could be well separated on a scalp without overlapping. This study presents a referential framework for establishing the effective spatial resolutions of 10/20, 10/10, and 10/5 systems as relative head-surface-based positioning systems.
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              Brain Computer Interfaces, a Review

              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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                Author and article information

                Contributors
                Journal
                Front Hum Neurosci
                Front Hum Neurosci
                Front. Hum. Neurosci.
                Frontiers in Human Neuroscience
                Frontiers Media S.A.
                1662-5161
                28 April 2014
                2014
                : 8
                : 244
                Affiliations
                [1] 1Department of Cogno-Mechatronics Engineering, Pusan National University Busan, Republic of Korea
                [2] 2Department of Education Policy and Social Analysis, Columbia University New York, NY, USA
                [3] 3School of Mechanical Engineering, Pusan National University Busan, Republic of Korea
                Author notes

                Edited by: Francesco Di Russo, University of Rome “Foro Italico,” Italy

                Reviewed by: Mederic Descoins, NYU Langone Medical Center, USA; Rodolphe J. Gentili, University of Maryland, USA

                *Correspondence: Keum-Shik Hong, Department of Cogno-Mechatronics Engineering, Pusan National University, 2 Busandaehak-ro, Guemjeong-gu, Busan 609-735, Republic of Korea e-mail: kshong@ 123456pusan.ac.kr

                This article was submitted to the journal Frontiers in Human Neuroscience.

                Article
                10.3389/fnhum.2014.00244
                4009438
                24808844
                248fb470-ab7a-41f4-9fe3-4fb949a8316a
                Copyright © 2014 Khan, Hong and Hong.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 13 November 2013
                : 03 April 2014
                Page count
                Figures: 7, Tables: 2, Equations: 3, References: 59, Pages: 10, Words: 5675
                Categories
                Neuroscience
                Methods Article

                Neurosciences
                electroencephaelography,near-infrared spectroscopy,hybrid brain-computer interface,motor execution,arithmetic mental task,linear discriminant analysis

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