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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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Background: In the realm of brain-computer interfaces (BCI), identifying emotions from electroencephalogram (EEG) data is a difficult endeavor because of the volume of data, the intricacy of the signals, and the several channels that make up the signals., New Methods: Using dual-stream structure scaling and multiple attention mechanisms (LDMGEEG), a lightweight network is provided to maximize the accuracy and performance of EEG-based emotion identification. Reducing the number of computational parameters while maintaining the current level of classification accuracy is the aim. This network employs a symmetric dual-stream architecture to assess separately time-domain and frequency-domain spatio-temporal maps constructed using differential entropy features of EEG signals as inputs., Result: The experimental results show that after significantly lowering the number of parameters, the model achieved the best possible performance in the field, with a 95.18 % accuracy on the SEED dataset., Comparison With Existing Methods: Moreover, it reduced the number of parameters by 98 % when compared to existing models., Conclusion: The proposed method distinct channel-time/frequency-space multiple attention and post-attention methods enhance the model's ability to aggregate features and result in lightweight performance., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier B.V. All rights reserved.)

Although the state of hyperhomocysteinemia (HHcy) appears to be associated with higher risks of coronary, cerebral and peripheral vascular disease as well as with a number of other clinical conditions, the underlying molecular mechanisms are not fully elucidated. There is strong evidence, however, that HHcy could induce a pathogenic state of oxidative stress. The interest in modulating the elevated levels of total homocysteine in HHcy and/or their negative impacts through preventive strategies, particularly through the supplementation with vitamins that may be linked to the homeostasis of homocysteine (folate, vitamin B(12), and vitamin B(6)), has increased in recent years. Here we show that active antioxidant components of the traditionally used black seeds of Nigella sativa plant protect against the development of methionine-induced HHcy and its associated state of oxidative stress. Pretreatment of rats with an oral dose of 100 mg/kg of thymoquinone, the main active constituent of the black seed, for 30 min and for 1 week almost completely protected against induced HHcy measured 5 h after methionine load (100 mg/kg). Under similar conditions pretreatment with commercial black seed oil (100 microl/kg) for 30 min and for 1 week produced significant and strong protection levels of 74.2 and 94.5%, respectively. Under the state of induced HHcy there were significant increases in the plasma levels of triglycerides, lipid peroxidation, cholesterol and in the activities of glutathione peroxidase and superoxide dismutase. Catalase activity was not affected. The total antioxidant status, however, was significantly depressed. All of these effects were almost totally blocked by prior treatment with thymoquinone or black seed oil. These findings may contribute towards a protective measure utilizing the black seed against the negative impacts of HHcy.