Adaptive EEG thought pattern classifier for advanced wheelchair control

Area of Science:

• Neuroscience and Biomedical Engineering
• Assistive Technology Research
• Rehabilitation Engineering

Background:

• Spinal Cord Injury (SCI) often leads to severe motor impairments, necessitating advanced mobility solutions.
• Existing head-movement controlled power wheelchairs have limitations in command repertoire.
• Electroencephalogram (EEG) based control offers a potential avenue for augmenting wheelchair functionality.

Purpose of the Study:

• To develop and evaluate a real-time EEG classification system for enhancing power wheelchair control in SCI patients.
• To integrate mental command recognition with existing head-movement control for a richer user interface.
• To assess the efficacy of adaptive training in improving EEG-based command classification.

Main Methods:

• Collected 32-channel EEG data from 10 participants performing mental tasks.
• Developed a real-time classification system to identify three distinct mental commands.
• Utilized a subset of 4 EEG channels for classification.
• Implemented and evaluated an adaptive training protocol for the classifier.

Main Results:

• Achieved an average classification rate of 82% for mental commands using 4 EEG channels.
• Demonstrated significant improvement in recognition rates through adaptive training.
• Adaptive training increased average recognition rates from 52.5% to 77.5% for unseen individuals.

Conclusions:

• Real-time EEG classification is a viable method for enhancing power wheelchair control for SCI patients.
• Adaptive training is crucial for optimizing EEG-based control system performance in new users.
• The developed system offers a promising approach to expand the control capabilities of assistive devices.