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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| Vydáno v: | SLAS technology Ročník 29; číslo 5; s. 100187 |
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| Hlavní autoři: | , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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United States
Elsevier Inc
01.10.2024
Elsevier |
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| ISSN: | 2472-6303, 2472-6311, 2472-6311 |
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| Abstract | 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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| AbstractList | 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. 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.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. |
| ArticleNumber | 100187 |
| Author | MP, Sunil Reegu, Faheem Ahmad Dutta, Ashit Kumar Dasari, Rakeshnag Yellu, Ramswaroop Reddy Quraishi, Aadam Khan, Ihtiram Raza Byeon, Haewon Khalaf, Mohammed I. Bhatt, Mohammed Wasim |
| Author_xml | – sequence: 1 givenname: Haewon orcidid: 0000-0002-3363-390X surname: Byeon fullname: Byeon, Haewon organization: Department of AI and Software, Inje University, Gimhae 50834, Republic of Korea – sequence: 2 givenname: Aadam surname: Quraishi fullname: Quraishi, Aadam organization: M.D Research, Intervention Treatment Institute, Houston, TX, USA – sequence: 3 givenname: Mohammed I. surname: Khalaf fullname: Khalaf, Mohammed I. organization: Department of Computer Science, Al Maarif University College, Al Anbar, 31001, Iraq – sequence: 4 givenname: Sunil surname: MP fullname: MP, Sunil organization: Department of Electronics and Communication Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India – sequence: 5 givenname: Ihtiram Raza surname: Khan fullname: Khan, Ihtiram Raza organization: Computer Science Department, Jamia Hamdard Delhi, New Delhi, India – sequence: 6 givenname: Ashit Kumar surname: Dutta fullname: Dutta, Ashit Kumar organization: Department of Computer Science and Information Systems, College of Applied Sciences, AlMaarefa University, Ad Diriyah, Riyadh 13713, Kingdom of Saudi Arabia – sequence: 7 givenname: Rakeshnag surname: Dasari fullname: Dasari, Rakeshnag organization: Department of CSE, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, 522510, India – sequence: 8 givenname: Ramswaroop Reddy surname: Yellu fullname: Yellu, Ramswaroop Reddy organization: Independent Research, University of Texas, Austin, USA – sequence: 9 givenname: Faheem Ahmad surname: Reegu fullname: Reegu, Faheem Ahmad organization: Department of Electrical and Electronics Engineering, College of Engineering and Computer Science, Jazan University, Jazan, Kingdom of Saudi Arabia – sequence: 10 givenname: Mohammed Wasim surname: Bhatt fullname: Bhatt, Mohammed Wasim email: wasimmohammad71@gmail.com organization: Model Institute of Engineering and Technology, Jammu, J&K, India |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/39209118$$D View this record in MEDLINE/PubMed |
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| Keywords | Neuron-electronics BCI EEG signals Fuzzy theory Brain control vehicles Neuron-Electronics EEG Signals Brain Control Vehicles, Fuzzy theory |
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