Motor-Imagery EEG-Based BCIs in Wheelchair Movement and Control: A Systematic Literature Review.
Arrigo PalumboVera GramignaBarbara CalabreseNicola IelpoPublished in: Sensors (Basel, Switzerland) (2021)
The pandemic emergency of the coronavirus disease 2019 (COVID-19) shed light on the need for innovative aids, devices, and assistive technologies to enable people with severe disabilities to live their daily lives. EEG-based Brain-Computer Interfaces (BCIs) can lead individuals with significant health challenges to improve their independence, facilitate participation in activities, thus enhancing overall well-being and preventing impairments. This systematic review provides state-of-the-art applications of EEG-based BCIs, particularly those using motor-imagery (MI) data, to wheelchair control and movement. It presents a thorough examination of the different studies conducted since 2010, focusing on the algorithm analysis, features extraction, features selection, and classification techniques used as well as on wheelchair components and performance evaluation. The results provided in this paper could highlight the limitations of current biomedical instrumentations applied to people with severe disabilities and bring focus to innovative research topics.
Keyphrases
- coronavirus disease
- resting state
- functional connectivity
- systematic review
- deep learning
- working memory
- public health
- sars cov
- machine learning
- healthcare
- respiratory syndrome coronavirus
- early onset
- physical activity
- emergency department
- meta analyses
- mental health
- big data
- white matter
- drug induced
- antiretroviral therapy
- health information
- health promotion
- risk assessment
- randomized controlled trial
- artificial intelligence
- multiple sclerosis
- blood brain barrier