EEG and EMG dataset for analyzing movement-related cortical potentials in hand gesture tasks

This dataset contains electroencephalography (EEG) and electromyography (EMG) recordings acquired during the execution of specific motor tasks aimed at eliciting movement-related cortical potentials (MRCP). The goal is to provide an accessible resource for research in brain-computer interfaces (BCI), neurorehabilitation, and EEG-based prosthetic control. Data were collected from 40 healthy participants aged 18-30 years across five sessions, each comprising ten right-hand fist closure movements, guided by a custom Python-based visual interface. EEG signals were recorded using a 32-channel EMOTIV Flex 2 wireless system following the international 10-10 system, with a sampling rate of 128 Hz and electrode placement focused on the central cortical areas. All recordings, including raw EEG, raw EMG, and event triggers synchronized with the visual interface, were stored in .CSV format. To demonstrate that the EEG recordings in the dataset contain sufficient low-frequency information for MRCP analysis, we applied a standard preprocessing pipeline consisting of a common average reference (CAR), a Anti-Laplacian spatial filter, and a 0.1-1 Hz Butterworth band-pass filter. This procedure was used only for internal validation, allowing us to visualize the expected MRCP components from the nine motor-related electrodes. It is important to emphasize that these processed signals are not included in the database. The public dataset contains only the raw EEG and EMG recordings, so that users may apply their preferred preprocessing and analysis methods. The dataset was collected under controlled laboratory conditions at the Medical Devices Laboratory, Universidad Autónoma de Guadalajara, and represents a valuable contribution to the understanding and application of MRCP in BCI research.