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An empirical survey of electroencephalography-based brain-computer interfaces

  • Megha M. Wankhade EMAIL logo und Suvarna S. Chorage
Veröffentlicht/Copyright: 3. November 2020
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Bio-Algorithms and Med-Systems
Aus der Zeitschrift Bio-Algorithms and Med-Systems Band 16 Heft 4

Abstract

Objectives

The Electroencephalogram (EEG) signal is modified using the Motor Imagery (MI) and it is utilized for patients with high motor impairments. Hence, the direct relationship between the computer and brain is termed as an EEG-based brain-computer interface (BCI). The objective of this survey is to presents an analysis of the existing distinct BCIs based on EEG.

Methods

This survey provides a detailed review of more than 60 research papers presenting the BCI-based EEG, like motor imagery-based techniques, spatial filtering-based techniques, Steady-State Visual Evoked Potential (SSVEP)-based techniques, machine learning-based techniques, Event-Related Potential (ERP)-based techniques, and online EEG-based techniques. Subsequently, the research gaps and issues of several EEG-based BCI systems are adopted to help the researchers for better future scope.

Results

An elaborative analyses as well as discussion have been provided by concerning the parameters, like evaluation metrics, year of publication, accuracy, implementation tool, and utilized datasets obtained by various techniques.

Conclusions

This survey paper exposes research topics on BCI-based EEG, which helps the researchers and scholars, who are interested in this domain.

Keywords: brain-computer interface; electroencephalography; event-related potential; machine learning; motor-imagery classification
Corresponding author: Megha M. Wankhade, Research Scholar, Dept. of Electronics &Telecommunication Engineering, AISSMS Institute of Information Technology, Pune 411001, Indiaand Assistant Professor, Dept. of Electronics & Telecommunication Engineering, Sinhgad College of Engineering, Affiliated to S.P. Pune Universit, Pune 411001, India, E-mail:

  1. Research funding: None.

  2. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  3. Competing interests: The funding organization(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report or in the decision to submit the report for publication. The authors declare that they have no conflict of interest.

  4. Informed consent: Informed consent was obtained from all individuals included in this study.

  5. Ethical Approval: The conducted research is not related to either human or animal use.

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Received: 2019-10-14
Accepted: 2020-09-30
Published Online: 2020-11-03

© 2020 Walter de Gruyter GmbH, Berlin/Boston

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https://doi.org/10.1515/bams-2019-0053
Schlagwörter für diesen Artikel
brain-computer interface; electroencephalography; event-related potential; machine learning; motor-imagery classification

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