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Assessment and Communication for People with Disorders of Consciousness
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EEG-Based Brain-Computer Interfaces.

Yijun Wang1, Masaki Nakanishi2, Dan Zhang3

  • 1Institute of Semiconductors, Chinese Academy of Sciences, Beijing, China. wangyj@semi.ac.cn.

Advances in Experimental Medicine and Biology
|November 16, 2019
PubMed
Summary
This summary is machine-generated.

Electroencephalography (EEG)-based brain-computer interfaces (BCIs) offer a direct brain-device link. This overview categorizes EEG-BCIs, discusses challenges for practical use, and reviews applications, highlighting future potential.

Keywords:
Brain-computer interfacesEEG-based BCIERPSSVEPhybrid BCI

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Brain-computer interfaces (BCIs) enable direct communication between the brain and external devices.
  • Electroencephalography (EEG) is a prevalent BCI method due to its non-invasiveness, usability, and affordability.

Purpose of the Study:

  • To provide a comprehensive overview of current EEG-based BCIs for communication and control.
  • To categorize existing EEG-BCI systems and discuss their challenges and applications.

Main Methods:

  • Classification of EEG-BCI systems into three categories: event-related potentials (ERPs), sensorimotor rhythms, and hybrid BCIs.
  • Review of challenges in developing practical BCIs, focusing on communication speed, usability, and user variability.
  • Exploration of current medical and non-medical applications of EEG-BCIs.

Main Results:

  • EEG-BCIs are categorized into ERP-based, sensorimotor rhythm-based, and hybrid systems.
  • Key challenges include enhancing communication rates, improving system convenience, and reducing user-specific variations.
  • BCIs have diverse applications in both medical and non-medical fields.

Conclusions:

  • EEG-based BCIs are a rapidly advancing technology with significant potential for communication and control.
  • Future development will focus on overcoming current limitations to enable more widespread and effective BCI implementation.
  • The field shows promising future perspectives for both clinical and everyday use.