首页|Study Results from Inje University Update Understanding of Machine Learning (Bio -inspired EEG signal computing using machine learning and fuzzy theory for decis ion making in future-oriented brain-controlled vehicles)
Study Results from Inje University Update Understanding of Machine Learning (Bio -inspired EEG signal computing using machine learning and fuzzy theory for decis ion making in future-oriented brain-controlled vehicles)
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in artificial intelli gence. According to news originating from Gimhae, South Korea, by NewsRx corresp ondents, research stated, "One kind of autonomous vehicle that can take instruct ions 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." Our news reporters obtained a quote from the research from Inje University: "At present, there are limitations on the accuracy of BCI recognition, the number of distinguishable command categories, and the execution duration of command recog nition. Consequently, vehicles that are exclusively controlled by EEG signals de monstrate suboptimal control performance. To address the difficulty of improving the control capabilities of brain-controlled cars while maintaining BCI perform ance, a fuzzy logic-based technique called as Fuzzy Brain-Control Fusion Control is introduced. This approach uses Fuzzy Discrete Event System (FDES) supervisor y theory to verify the accuracy of the driver's brain-controlled directives. Con currently, a fuzzy logic-based automatic controller is developed to generate dec isions automatically in accordance with the present state of the vehicle via fuz zy reasoning. The final decision is then reached through the application of seco ndary fuzzy reasoning to the accuracy of the driver's instructions and the autom ated decisions to make adjustments that are more consistent with human intent."
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