Computational Intelligence and Neuroscience

Ergonomic Issues in Brain-Computer Interface Technologies: Current Status, Challenges, and Future Direction


Publishing date
01 Nov 2019
Status
Published
Submission deadline
21 Jun 2019

Lead Editor

1Soonchunhyang University, Asan, Republic of Korea

2Korea University, Seoul, Republic of Korea

3Handong Global University, Pohang, Republic of Korea

4Wadsworth Center, Albany, USA

5University of North Carolina at Chapel Hill, Chapel Hill, USA


Ergonomic Issues in Brain-Computer Interface Technologies: Current Status, Challenges, and Future Direction

Description

The brain-computer interface is a fast-growing technology that involves the brain to sense, analyze, and translate signals into commands for the purpose of providing a human computer interaction that will enable communication with computers and control of external devices such as robotic agents. It emerged a few decades ago, and BCI research has mainly focused on signal processing methods to increase its accuracy for its applications such as virtual keyboards, wheelchair control, or rehabilitation training. BCI is now maturing towards being more realistic and practically plausible; therefore it is time to take ergonomics (or human factors) into account as part of the BCI design process. In addition to improving BCI system performance such as accuracy, brain signal measurements should be more convenient and easier, and the BCI paradigm including audio or visual stimulation should not make the user tired.

The goal of this special issue is to bring researchers and practitioners from academia and industry together to present their vision for research and development of future user-friendly BCI technologies. We invite original research papers as well as review articles that describe current and expected challenges along with potential solutions for brain-computer interface technology.

Potential topics include but are not limited to the following:

  • New innovative BCI devices for improvement of user experience
  • Artificial intelligence and signal processing methods to improve the reliability and performance of BCI systems
  • Evaluating the ergonomics of BCI devices, paradigms, and systems
  • New BCI paradigm and stimulus methods for reducing user fatigue
  • Effective integration of BCI systems with other control functions such as muscle, eye, tongue, or speech behavior
  • Clinical and nonclinical application of BCI technologies such as rehabilitation, assistive robotics, and entertainments
  • Literature and systematic reviews on the field

Articles

  • Special Issue
  • - Volume 2020
  • - Article ID 4876397
  • - Editorial

Ergonomic Issues in Brain-Computer Interface Technologies: Current Status, Challenges, and Future Direction

Hyun Jae Baek | Hyun Seok Kim | ... | Sangtae Ahn
  • Special Issue
  • - Volume 2019
  • - Article ID 9374802
  • - Research Article

Evaluating a Semiautonomous Brain-Computer Interface Based on Conformal Geometric Algebra and Artificial Vision

Mauricio Adolfo Ramírez-Moreno | David Gutiérrez
  • Special Issue
  • - Volume 2019
  • - Article ID 3807670
  • - Review Article

Advances in Hybrid Brain-Computer Interfaces: Principles, Design, and Applications

Zina Li | Shuqing Zhang | Jiahui Pan
  • Special Issue
  • - Volume 2019
  • - Article ID 4721863
  • - Research Article

Driving Fatigue Detection from EEG Using a Modified PCANet Method

Yuliang Ma | Bin Chen | ... | Yingchun Zhang
  • Special Issue
  • - Volume 2019
  • - Article ID 4259369
  • - Research Article

Covert Intention to Answer “Yes” or “No” Can Be Decoded from Single-Trial Electroencephalograms (EEGs)

Jeong Woo Choi | Kyung Hwan Kim
  • Special Issue
  • - Volume 2019
  • - Article ID 9680697
  • - Research Article

Comparison of Visual Stimuli for Steady-State Visual Evoked Potential-Based Brain-Computer Interfaces in Virtual Reality Environment in terms of Classification Accuracy and Visual Comfort

Kang-min Choi | Seonghun Park | Chang-Hwan Im
  • Special Issue
  • - Volume 2019
  • - Article ID 7876248
  • - Research Article

Impact of Speller Size on a Visual P300 Brain-Computer Interface (BCI) System under Two Conditions of Constraint for Eye Movement

R. Ron-Angevin | L. Garcia | ... | V. Lespinet-Najib
  • Special Issue
  • - Volume 2019
  • - Article ID 5427154
  • - Review Article

Enhancing the Usability of Brain-Computer Interface Systems

Hyun Jae Baek | Min Hye Chang | ... | Kwang Suk Park
  • Special Issue
  • - Volume 2019
  • - Article ID 4629859
  • - Research Article

Application of Deep Learning in Neuroradiology: Brain Haemorrhage Classification Using Transfer Learning

Awwal Muhammad Dawud | Kamil Yurtkan | Huseyin Oztoprak
  • Special Issue
  • - Volume 2019
  • - Article ID 8068357
  • - Research Article

An Optimized Channel Selection Method Based on Multifrequency CSP-Rank for Motor Imagery-Based BCI System

Jian Kui Feng | Jing Jin | ... | Andrzej Cichocki
Computational Intelligence and Neuroscience
 Journal metrics
Acceptance rate27%
Submission to final decision84 days
Acceptance to publication42 days
CiteScore2.270
Impact Factor2.154
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