Computational Intelligence and Neuroscience

Brain-Computer Interface Applications for Improving the Quality of Elderly Living


Publishing date
01 Jan 2021
Status
Closed
Submission deadline
04 Sep 2020

1United Arab Emirates University, Abu Dhabi, UAE

2Tokyo Institute of Technology, Yokohama, Japan

3Institut national de la recherche scientifique, Quebec, Canada

4Osaka University, Osaka, Japan

This issue is now closed for submissions.
More articles will be published in the near future.

Brain-Computer Interface Applications for Improving the Quality of Elderly Living

This issue is now closed for submissions.
More articles will be published in the near future.

Description

Ageing becomes debilitating and dependence increases as time goes by. Many researchers have suggested transdisciplinary approaches to address the problem of ageing and its effects on daily living. Brain-computer interface (BCI) technology is now being incorporated into the treatment of many elderly patients suffering from physical impairments. This technology offers the promise of greatly enhancing these patients’ quality of life by improving their autonomy and mobility. BCI can be used as an assistive technology to monitor brain activity and translate specific signal features that reflect the elderly’s intent into commands that can operate any nearby device. BCI systems could be useful for elderly people in many ways, such as enabling them to improve their motor and cognitive abilities, direct home appliances through brain activity alone and even control an exoskeleton to enhance joint strength.

This Special Issue therefore aims to gather original research and review articles that cover the challenges and applications of BCIs for health and wellness in general, with particular emphasis on clinical and non-clinical applications for the elderly.

Potential topics include but are not limited to the following:

  • Assistive, adaptive, and rehabilitative applications of electroencephalogram (EEG)-based BCI
  • Brain activations related to healthy elderly
  • Cognitive neuroengineering for health and wellness
  • Brain signal processing techniques and machine learning algorithms for assisted living
  • BCI control in a realistic smart home environment for assisted living
  • EEG-based brain-computer systems for automating home appliances to assist the elderly
  • Medical and non-medical BCI-based gaming applications for assisted living
  • EEG and electromyography (EMG)-based exoskeleton control for the elderly
  • Neuroengineering for swarm intelligence to assist the elderly
  • Cybersecurity for BCIs which assist the elderly
  • Neurofeedback for assisted living
  • Hybrid, mobile and wireless BCIs for assisted living
Computational Intelligence and Neuroscience
 Journal metrics
Acceptance rate28%
Submission to final decision79 days
Acceptance to publication37 days
CiteScore4.700
Impact Factor2.284
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