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Brain-Computer Interface for Control of Wheelchair Using Fuzzy Neural Networks
Rahib H Abiyev1, Nurullah Akkaya1, Ersin Aytac2
1Department of Computer Engineering, Applied Artificial Intelligence Research Centre, Near East University, Lefkosa, Northern Cyprus, Mersin 10, Turkey.
Biomed Research International
|October 26, 2016
Summary
This study presents a brain-computer interface for wheelchairs, using electroencephalographic (EEG) signals and fuzzy neural networks (FNN) for precise control. The system enhances wheelchair navigation for physically disabled individuals by improving accuracy.
Area of Science:
- Biomedical Engineering
- Neuroscience
- Rehabilitation Technology
Background:
- Physically disabled individuals often face mobility challenges.
- Existing assistive technologies may have limitations in intuitive control.
- Brain-computer interfaces (BCIs) offer a potential solution for restoring mobility.
Purpose of the Study:
- To design and evaluate a novel brain-computer interface (BCI) for wheelchair control.
- To enable intuitive and accurate wheelchair navigation for physically disabled users.
- To leverage electroencephalographic (EEG) signals for direct control commands.
Main Methods:
- Receiving, processing, and classifying electroencephalographic (EEG) signals.
- Developing a classification system based on fuzzy neural networks (FNN).
- Training and testing the FNN algorithm for brain-actuated wheelchair control.
Main Results:
- The FNN-based algorithm demonstrated effective classification of user's mental activities.
- Wheelchair control was successfully implemented under real-world conditions.
- The system achieved improved control accuracy and reduced misclassification probability.
Conclusions:
- The proposed BCI system provides a viable method for controlling wheelchairs using EEG signals.
- Fuzzy neural networks enhance the precision and reliability of brain-actuated control.
- This technology holds significant potential for improving the independence of physically disabled individuals.
