A mental state aware brain computer interface for adaptive control of electric powered wheelchair
Abstract Brain-computer interfaces (BCI) provide a mobility solution for patients with various disabilities. However, BCI systems require further research to enhance their performance while incorporating the physical and behavioral states of patients into the system. As the principal users of a BCI...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-024-82252-7 |
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| Summary: | Abstract Brain-computer interfaces (BCI) provide a mobility solution for patients with various disabilities. However, BCI systems require further research to enhance their performance while incorporating the physical and behavioral states of patients into the system. As the principal users of a BCI system, patients with disabilities are emotionally sensitive, so a BCI device that adaptively adjusts to the psychological effects of the patient could provide the foundation for refining BCI applications. This paper focuses on the collection and realization of human electroencephalogram (EEG) signals data, obtained as a response to different psychological effects of sound stimuli. Filtration and pre-processing of the data set are achieved using the frequency-based distribution of EEG signals. Different machine learning tools and techniques are evaluated and applied to abstracted powerbands of psychological signals. The experimental results show that the proposed system predicts mental states with an average accuracy of 74.26%. In addition, an automated BCI system is developed to control an electric wheelchair (EPW) while responding to the mental state of the user with a contingency mechanism. The results show that such a system could be designed to make BCI systems more reliable, safe, adaptable, and responsive to emotions for sensitive paralytic patients. The system also shows a satisfactory True Positive Rate (TPR) and False Positive Rate (FPR) with an average time of 8.4 s to generate the interpretable brain signal from the user. |
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| ISSN: | 2045-2322 |