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We developed a unique and valuable dataset specifically for advancing Brain-Computer Interface (BCI) systems by recording brain activity from a dedicated volunteer. The participant was asked to pronounce 100 carefully selected Malayalam words, along with their English translations, which were chosen for their relevance to astronauts during human space missions. The volunteer pronounced these words both vocally and subvocally, each word being repeated 50 times. Non-invasive Electroencephalography (EEG) sensors were employed to capture the brain activity associated with these tasks.

The Deepfake face detection task involves a facial image of unknown authenticity for testing. While most deepfake detection methods take only the image as input, our literature demonstrates that conditioning the deepfake detector on identity—i.e., knowing whose deepfake face the picture might be—can enhance detection performance. Existing deepfake detection datasets, such as FaceForensics++ and DFDC, do not include identity information for authentic and deepfake faces.

This data provides an overview of the simulation of Hybrid Electric Vehicles (HEVs) using MATLAB. HEVs have emerged as a promising solution for reducing emissions and improving fuel efficiency. MATLAB offers a flexible platform to accurately model and analyze the complex dynamics of hybrid powertrains, including various components and control strategies. The simulation capabilities enable the evaluation of HEV performance under different conditions, optimization of designs, and exploration of factors impacting energy consumption.

Although surface coating is commonly utilized in many industries for improving the aesthetics and functionality of the end product, our tactile perception of coated surfaces has not been investigated in depth yet. In fact, there are only a few studies investigating the effect of coating material on our tactile perception of extremely smooth surfaces having roughness amplitudes in the order of a few nanometers.