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MindBigData 2022 is a large-scale EEG signal dataset comprising three primary datasets and their sub-datasets. The data were collected using various EEG devices such as MindWave, EPOC1, Muse1, Insight1, etc., with detailed sampling rates and channel information. The dataset is split into 80% training and 20% testing, containing both labels and EEG recordings. Each sub-dataset has specific device and sampling‑rate configurations. Specifically: 1. MindBigData MNIST of Brain Digits – four sub-datasets based on MindWave, EPOC1, Muse1, and Insight1; 2. MindBigData Imagenet of the Brain – two sub-datasets based on Insight1 EEG signals and spectrograms; 3. MindBigData Visual MNIST of Brain Digits – three sub-datasets based on Muse2, Cap64, and Cap64 Morlet devices.

This dataset contains synchronized two‑photon calcium‑imaging movies and loose‑patch cell‑attachment recordings from layer‑2 pyramidal cells and interneurons in the primary visual cortex of wild‑type mice. Recordings were performed in vivo while presenting moving grating stimuli to the contralateral eye. Indicators (jGCaMP8f/m/s, jGCaMP7f, XCaMPgf) were expressed via AAV injection under the synapsin‑1 promoter. Multiple neurons were recorded per mouse, and each cell was recorded multiple times in ~3‑minute sessions (movie & scan). A single .nwb file aggregates all data for a given cell. The dataset includes motion‑corrected two‑photon movies, raw loose‑patch electrophysiology traces for individual neurons, cell bodies and neuropil ROIs segmented by Suite2p, fluorescence traces for all ROIs, and detailed information about the moving grating stimulus presented to the contralateral eye.