Towards Practical BCI-Driven Wheelchairs: A Systematic Review Study

Area of Science:

• Biomedical Engineering
• Neuroscience
• Rehabilitation Technology

Background:

• Electroencephalography (EEG)-controlled wheelchairs offer a potential mobility solution for individuals with severe motor impairments, such as those with motor neuron disease.
• Despite significant research over two decades, the practical application of EEG-driven wheelchairs remains largely confined to laboratory settings.
• Existing technologies face limitations in reliability, usability, and seamless integration into daily life.

Purpose of the Study:

• To systematically review the current state-of-the-art in EEG-controlled wheelchair technology.
• To identify and analyze various models and approaches employed in the literature for EEG-based wheelchair control.
• To highlight the primary challenges hindering the widespread adoption of this technology and explore emerging research trends.

Main Methods:

• A systematic literature review was conducted to gather and analyze relevant studies on EEG-controlled wheelchairs.
• The review focused on identifying different control strategies, system architectures, and performance metrics reported in existing research.
• Key challenges and future research directions were extracted and synthesized from the reviewed literature.

Main Results:

• The review identified diverse EEG signal processing techniques and machine learning algorithms used for wheelchair control.
• Key challenges include signal noise, user training requirements, limited control accuracy, and the need for robust real-time performance.
• Emerging trends focus on improving signal acquisition, developing more intuitive control paradigms, and enhancing system robustness for practical environments.

Conclusions:

• EEG-controlled wheelchairs show significant potential for restoring mobility and independence to individuals with motor disabilities.
• Overcoming current technical and practical challenges is crucial for transitioning this technology from laboratory research to widespread clinical and personal use.
• Continued research into advanced signal processing, adaptive algorithms, and user-centered design is essential for the future success of EEG-driven mobility solutions.