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      Brain-Computer Interface-Based Humanoid Control: A Review

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          A Brain-Computer Interface (BCI) acts as a communication mechanism using brain signals to control external devices. The generation of such signals is sometimes independent of the nervous system, such as in Passive BCI. This is majorly beneficial for those who have severe motor disabilities. Traditional BCI systems have been dependent only on brain signals recorded using Electroencephalography (EEG) and have used a rule-based translation algorithm to generate control commands. However, the recent use of multi-sensor data fusion and machine learning-based translation algorithms has improved the accuracy of such systems. This paper discusses various BCI applications such as tele-presence, grasping of objects, navigation, etc. that use multi-sensor fusion and machine learning to control a humanoid robot to perform a desired task. The paper also includes a review of the methods and system design used in the discussed applications.

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          BCI2000: a general-purpose brain-computer interface (BCI) system.

          Gerwin Schalk, Dennis J McFarland, Thilo Hinterberger … (2004)
          Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BC12000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.
          Article 0 comments Cited 539 times – based on 0 reviews     Review now
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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.
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              Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials

              L.A. Farwell, E Donchin (1988)
              This paper describes the development and testing of a system whereby one can communicate through a computer by using the P300 component of the event-related brain potential (ERP). Such a system may be used as a communication aid by individuals who cannot use any motor system for communication (e.g., 'locked-in' patients). The 26 letters of the alphabet, together with several other symbols and commands, are displayed on a computer screen which serves as the keyboard or prosthetic device. The subject focuses attention successively on the characters he wishes to communicate. The computer detects the chosen character on-line and in real time. This detection is achieved by repeatedly flashing rows and columns of the matrix. When the elements containing the chosen character are flashed, a P300 is elicited, and it is this P300 that is detected by the computer. We report an analysis of the operating characteristics of the system when used with normal volunteers, who took part in 2 experimental sessions. In the first session (the pilot study/training session) subjects attempted to spell a word and convey it to a voice synthesizer for production. In the second session (the analysis of the operating characteristics of the system) subjects were required simply to attend to individual letters of a word for a specific number of trials while data were recorded for off-line analysis. The analyses suggest that this communication channel can be operated accurately at the rate of 0.20 bits/sec. In other words, under the conditions we used, subjects can communicate 12.0 bits, or 2.3 characters, per min.
              Article 0 comments Cited 384 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Journal ID (publisher-id): sensors
                Title: Sensors (Basel, Switzerland)
                Publisher: MDPI
                ISSN (Electronic): 1424-8220
                Publication date (Electronic): 27 June 2020
                Publication date Collection: July 2020
                Volume: 20
                Issue: 13
                Electronic Location Identifier: 3620
                Affiliations
                [1 ]Department of Electrical and Electronics, Birla Institute of Technology & Science, Pilani 333031, India; vinay.chamola@ 123456pilani.bits-pilani.ac.in (V.C.); h20180144@ 123456pilani.bits-pilani.ac.in (A.V.)
                [2 ]Graduate School, Duy Tan University, Da Nang 550000, Vietnam; anandnayyar@ 123456duytan.edu.vn
                [3 ]Faculty of Information Technology, Duy Tan University, Da Nang 550000, Vietnam
                [4 ]Department of Electrical Engineering and Renewable energy, Oregon Institute of Technology, Klamath Falls, OR 97601, USA
                Author notes
                [* ]Correspondence: eklas.hossain@ 123456oit.edu ; Tel.: +1-541-885-1516
                Author information
                https://orcid.org/0000-0002-6730-3060
                https://orcid.org/0000-0001-7180-8574
                https://orcid.org/0000-0003-2332-8095
                Article
                Publisher ID: sensors-20-03620
                DOI: 10.3390/s20133620
                PMC ID: 7374399
                PubMed ID: 32605077
                SO-VID: d5fada86-0afa-4371-aae4-19afb5a1cb54
                Copyright © © 2020 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 25 April 2020
                Date accepted : 17 June 2020
                Categories
                Subject: Review

                ScienceOpen disciplines: Biomedical engineering
                Keywords: brain-computer interface (bci),data fusion,nao humanoid,electroencephalography (eeg),p300,biological feedback
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: brain-computer interface (bci), data fusion, nao humanoid, electroencephalography (eeg), p300, biological feedback

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