Bio-inspired EEG signal computing using machine learning and fuzzy theory for decision making in future-oriented brain-controlled vehicles

One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatly affected by how well the BCI works. At present,...

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Bibliographic Details
Published in:SLAS technology Vol. 29; no. 5; p. 100187
Main Authors: Byeon, Haewon, Quraishi, Aadam, Khalaf, Mohammed I., MP, Sunil, Khan, Ihtiram Raza, Dutta, Ashit Kumar, Dasari, Rakeshnag, Yellu, Ramswaroop Reddy, Reegu, Faheem Ahmad, Bhatt, Mohammed Wasim
Format: Journal Article
Language:English
Published: United States Elsevier Inc 01.10.2024
Elsevier
Subjects:
BCI
Brain control vehicles
EEG signals
Fuzzy theory
Neuron-electronics
Neuron-electronics
BCI
EEG signals
Fuzzy theory
Brain control vehicles
Neuron-Electronics
EEG Signals
Brain Control Vehicles, Fuzzy theory
ISSN:2472-6303, 2472-6311, 2472-6311
Online Access:Get full text
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Summary:One kind of autonomous vehicle that can take instructions from the driver by reading their electroencephalogram (EEG) signals using a Brain-Computer Interface (BCI) is called a Brain-Controlled Vehicle (BCV). The operation of such a vehicle is greatly affected by how well the BCI works. At present, there are limitations on the accuracy of BCI recognition, the number of distinguishable command categories, and the execution duration of command recognition. Consequently, vehicles that are exclusively controlled by EEG signals demonstrate suboptimal control performance. To address the difficulty of improving the control capabilities of brain-controlled cars while maintaining BCI performance, a fuzzy logic-based technique called as Fuzzy Brain-Control Fusion Control is introduced. This approach uses Fuzzy Discrete Event System (FDES) supervisory theory to verify the accuracy of the driver's brain-controlled directives. Concurrently, a fuzzy logic-based automatic controller is developed to generate decisions automatically in accordance with the present state of the vehicle via fuzzy reasoning. The final decision is then reached through the application of secondary fuzzy reasoning to the accuracy of the driver's instructions and the automated decisions to make adjustments that are more consistent with human intent. A clever BCI gadget known as the Consistent State Visual Evoked Potential (SSVEP) is utilized to show the viability of the proposed technique. We recommend that additional research should be conducted at this time to confirm that our recommended system may further improve the control execution of BCI-fueled cars, regardless of whether BCIs have special limitations.
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ISSN:2472-6303
2472-6311
2472-6311
DOI:10.1016/j.slast.2024.100187