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      • Record: found
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      Classification of Four-Class Motor Imagery Employing Single-Channel Electroencephalography

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      Author(s): 1 , * , 2 , 1
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      Publication date (Electronic):
      Journal: PLoS ONE
      Publisher: Public Library of Science

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          Abstract

          With advances in brain-computer interface (BCI) research, a portable few- or single-channel BCI system has become necessary. Most recent BCI studies have demonstrated that the common spatial pattern (CSP) algorithm is a powerful tool in extracting features for multiple-class motor imagery. However, since the CSP algorithm requires multi-channel information, it is not suitable for a few- or single-channel system. In this study, we applied a short-time Fourier transform to decompose a single-channel electroencephalography signal into the time-frequency domain and construct multi-channel information. Using the reconstructed data, the CSP was combined with a support vector machine to obtain high classification accuracies from channels of both the sensorimotor and forehead areas. These results suggest that motor imagery can be detected with a single channel not only from the traditional sensorimotor area but also from the forehead area.

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          Brain Computer Interfaces, a Review

          Luis Nicolas-Alonso, Jaime Gomez-Gil (2012)
          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.
          Article 0 comments Cited 393 times – based on 0 reviews     Review now
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            Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks.

            G. Pfurtscheller, C. Brunner, A Schlögl … (2006)
            We studied the reactivity of EEG rhythms (mu rhythms) in association with the imagination of right hand, left hand, foot, and tongue movement with 60 EEG electrodes in nine able-bodied subjects. During hand motor imagery, the hand mu rhythm blocked or desynchronized in all subjects, whereas an enhancement of the hand area mu rhythm was observed during foot or tongue motor imagery in the majority of the subjects. The frequency of the most reactive components was 11.7 Hz +/- 0.4 (mean +/- SD). While the desynchronized components were broad banded and centered at 10.9 Hz +/- 0.9, the synchronized components were narrow banded and displayed higher frequencies at 12.0 Hz +/- 1.0. The discrimination between the four motor imagery tasks based on classification of single EEG trials improved when, in addition to event-related desynchronization (ERD), event-related synchronization (ERS) patterns were induced in at least one or two tasks. This implies that such EEG phenomena may be utilized in a multi-class brain-computer interface (BCI) operated simply by motor imagery.
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              Stimulus specificity of phase-locked and non-phase-locked 40 Hz visual responses in human.

              Catherine Tallon-Baudry, Olivier Bertrand, Claude Delpuech … (1996)
              Considerable interest has been raised by non-phase-locked episodes of synchronization in the gamma-band (30-60 Hz). One of their putative roles in the visual modality is feature-binding. We tested the stimulus specificity of high-frequency oscillations in humans using three types of visual stimuli: two coherent stimuli (a Kanizsa and a real triangle) and a noncoherent stimulus ("no-triangle stimulus"). The task of the subject was to count the occurrences of a curved illusory triangle. A time-frequency analysis of single-trial EEG data recorded from eight human subjects was performed to characterize phase-locked as well as non-phase-locked high-frequency activities. We found in early phase-locked 40 Hz component, maximal at electrodes Cz-C4, which does not vary with stimulation type. We describe a second 40 Hz component, appearing around 280 msec, that is not phase-locked to stimulus onset. This component is stronger in response to a coherent triangle, whether real or illusory: it could reflect, therefore, a mechanism of feature binding based on high-frequency synchronization. Because both the illusory and the real triangle are more target-like, it could also correspond to an oscillatory mechanism for testing the match between stimulus and target. At the same latencies, the low-frequency evoked response components phase-locked to stimulus onset behave differently, suggesting that low- and high-frequency activities have different functional roles.
              Article 0 comments Cited 290 times – based on 0 reviews     Review now
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                Author and article information

                Contributors
                Luigi Bianchi: Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2014
                Publication date (Electronic): 20 June 2014
                Volume: 9
                Issue: 6
                Electronic Location Identifier: e98019
                Affiliations
                [1 ]Key Laboratory of Child Development and Learning Science of Ministry of Education, Research Center for Learning Science, Southeast University, Nanjing, Jiangsu, China
                [2 ]School of Electronic Engineering and Optoelectronic Technology, Nanjing University of Science and Technology, Nanjing, Jiangsu, China
                University of Rome Tor Vergata, Italy
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SG DCY. Performed the experiments: SG. Analyzed the data: SG RMW. Contributed reagents/materials/analysis tools: SG RMW. Wrote the paper: SG.

                Article
                Publisher ID: PONE-D-13-49200
                DOI: 10.1371/journal.pone.0098019
                PMC ID: 4064966
                PubMed ID: 24950192
                SO-VID: 7cc75de3-96b9-48ab-bb77-c1f63272cee2
                Copyright statement: Copyright @ 2014
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 22 January 2014
                Date accepted : 23 April 2014
                Page count
                Pages: 7
                Funding
                This work was supported by the National Natural Science Foundation of China (No. 51007040, 61273224), the Program for New Century Excellent Talents in University of Ministry of Education, China (No. 61074126), and the Fundamental Research Funds for the Central Universities, China (No. 3250183202, 10602026). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Biology and Life Sciences
                Subject: Anatomy
                Subject: Nervous System
                Subject: Motor System
                Subject: Biotechnology
                Subject: Bioengineering
                Subject: Biological Systems Engineering
                Subject: Biomedical Engineering
                Subject: Medical Devices and Equipment
                Subject: Computational Biology
                Subject: Computational Neuroscience
                Subject: Neuroscience
                Subject: Engineering and Technology

                ScienceOpen disciplines: Uncategorized
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                ScienceOpen disciplines: Uncategorized

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