Electroencephalography-Based Brain-Machine Interfaces in Older Adults: A Literature Review

Area of Science:

• Gerontology
• Neuroscience
• Biomedical Engineering

Background:

• Aging impacts cognitive, affective, and physical functions, affecting environmental interactions.
• While subjective cognitive decline is normal, objective impairment occurs in neurocognitive disorders, especially dementia.
• Electroencephalography-based brain-machine interfaces (BMI) offer neuro-rehabilitative applications for older adults.

Purpose of the Study:

• To provide an overview of brain-machine interfaces (BMI) designed to assist older adults.
• To examine both technical challenges and user-centered applications of BMI in aging.

Main Methods:

• Literature review of electroencephalography-based brain-machine interfaces (BMI).
• Analysis of signal detection, feature extraction, and classification techniques.
• Consideration of user needs and application-specific aspects for older adults.

Main Results:

• BMI technologies are being developed to support daily activities and enhance quality of life for the elderly.
• Key technical challenges include signal detection, feature extraction, and classification accuracy.
• Application design must consider the specific needs and contexts of older users.

Conclusions:

• BMI holds significant potential for assisting older adults and improving their quality of life through neuro-rehabilitation.
• Addressing technical hurdles and user-centric design is crucial for successful BMI implementation in the aging population.