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      EEG-Based Brain-Computer Interfaces Using Motor-Imagery: Techniques and Challenges

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          Abstract

          Electroencephalography (EEG)-based brain-computer interfaces (BCIs), particularly those using motor-imagery (MI) data, have the potential to become groundbreaking technologies in both clinical and entertainment settings. MI data is generated when a subject imagines the movement of a limb. This paper reviews state-of-the-art signal processing techniques for MI EEG-based BCIs, with a particular focus on the feature extraction, feature selection and classification techniques used. It also summarizes the main applications of EEG-based BCIs, particularly those based on MI data, and finally presents a detailed discussion of the most prevalent challenges impeding the development and commercialization of EEG-based BCIs.

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          Differential Evolution Algorithm With Strategy Adaptation for Global Numerical Optimization

          A.K. Qin, V.L. Huang, P.N. Suganthan (2009)
          Article 0 comments Cited 428 times – based on 0 reviews     Review now
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            Deep learning with convolutional neural networks for EEG decoding and visualization

            Robin Schirrmeister, Jost Tobias Springenberg, Lukas Fiederer … (2017)
            Abstract Deep learning with convolutional neural networks (deep ConvNets) has revolutionized computer vision through end‐to‐end learning, that is, learning from the raw data. There is increasing interest in using deep ConvNets for end‐to‐end EEG analysis, but a better understanding of how to design and train ConvNets for end‐to‐end EEG decoding and how to visualize the informative EEG features the ConvNets learn is still needed. Here, we studied deep ConvNets with a range of different architectures, designed for decoding imagined or executed tasks from raw EEG. Our results show that recent advances from the machine learning field, including batch normalization and exponential linear units, together with a cropped training strategy, boosted the deep ConvNets decoding performance, reaching at least as good performance as the widely used filter bank common spatial patterns (FBCSP) algorithm (mean decoding accuracies 82.1% FBCSP, 84.0% deep ConvNets). While FBCSP is designed to use spectral power modulations, the features used by ConvNets are not fixed a priori. Our novel methods for visualizing the learned features demonstrated that ConvNets indeed learned to use spectral power modulations in the alpha, beta, and high gamma frequencies, and proved useful for spatially mapping the learned features by revealing the topography of the causal contributions of features in different frequency bands to the decoding decision. Our study thus shows how to design and train ConvNets to decode task‐related information from the raw EEG without handcrafted features and highlights the potential of deep ConvNets combined with advanced visualization techniques for EEG‐based brain mapping. Hum Brain Mapp 38:5391–5420, 2017. © 2017 Wiley Periodicals, Inc.
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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 382 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): 22 March 2019
                Publication date Collection: March 2019
                Volume: 19
                Issue: 6
                Electronic Location Identifier: 1423
                Affiliations
                [1 ]Centre for Signal and Image Processing, University of Strathclyde, Glasgow G1 1XW, UK; natasha.padfield@ 123456strath.ac.uk (N.P.); j.zabalza@ 123456strath.ac.uk (J.Z.)
                [2 ]School of Computer Sciences, Guangdong Polytechnic Normal University, Guangzhou 510665, China
                [3 ]The Guangzhou Key Laboratory of Digital Content Processing and Security Technologies, Guangzhou 510665, China
                [4 ]Department of Computer Systems and Telematics Engineering, Universidad de Extremadura, 06007 Badajoz, Spain; vmasero@ 123456unex.es
                [5 ]School of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan 030024, China
                Author notes
                Author information
                https://orcid.org/0000-0001-6116-3194
                Article
                Publisher ID: sensors-19-01423
                DOI: 10.3390/s19061423
                PMC ID: 6471241
                PubMed ID: 30909489
                SO-VID: be5b7078-dc0e-448f-ae36-f12da9a35c05
                Copyright © © 2019 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 : 30 January 2019
                Date accepted : 19 March 2019
                Categories
                Subject: Review

                ScienceOpen disciplines: Biomedical engineering
                Keywords: brain-computer interface (bci),electroencephalography (eeg),motor-imagery (mi)
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: brain-computer interface (bci), electroencephalography (eeg), motor-imagery (mi)

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