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      Toward Brain-Actuated Humanoid Robots: Asynchronous Direct Control Using an EEG-Based BCI

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

          F Lotte, M Congedo, A Lécuyer … (2007)
          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.
          Article 0 comments Cited 436 times – based on 0 reviews     Review now
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            Direct cortical control of 3D neuroprosthetic devices.

            Dawn Taylor, Stephen I Helms Tillery, Andrew B. Schwartz (2002)
            Three-dimensional (3D) movement of neuroprosthetic devices can be controlled by the activity of cortical neurons when appropriate algorithms are used to decode intended movement in real time. Previous studies assumed that neurons maintain fixed tuning properties, and the studies used subjects who were unaware of the movements predicted by their recorded units. In this study, subjects had real-time visual feedback of their brain-controlled trajectories. Cell tuning properties changed when used for brain-controlled movements. By using control algorithms that track these changes, subjects made long sequences of 3D movements using far fewer cortical units than expected. Daily practice improved movement accuracy and the directional tuning of these units.
            Article 0 comments Cited 324 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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                Author and article information

                Journal
                Title: IEEE Transactions on Robotics
                Abbreviated Title: IEEE Trans. Robot.
                Publisher: Institute of Electrical and Electronics Engineers (IEEE)
                ISSN (Print): 1552-3098
                ISSN (Electronic): 1941-0468
                Publication date Created: October 2012
                Publication date (Print): October 2012
                Volume: 28
                Issue: 5
                Pages: 1131-1144
                Article
                DOI: 10.1109/TRO.2012.2201310
                SO-VID: de38a28a-8bee-4b12-868f-2a2ff1904265
                Copyright © © 2012
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