41 datasets found

The dataset used for training and testing the deep learning model for sEMG signal classification.

The EEG database consists of SCP training of EEG recordings. The feature extraction step was performed using time-frequency-amplitude analysis by Hilbert-Huang Transform.

The dataset used in this paper for sustained attention level evaluation using EEG.

The dataset used for testing the MinD-Vis model, consisting of fMRI data and corresponding images.

The dataset used for testing the MinD-Vis model, consisting of fMRI data and corresponding images.

The dataset used for training and testing the MinD-Vis model, consisting of fMRI data and corresponding images.

The dataset used in the paper is EEG data from epileptic patients, which is used to detect early warning indicators for abrupt dynamical transitions in complex systems or...

The dataset consists of Before Calculation Signals (BCS) and During Calculation Signals (DCS). The dataset consisted of 36 participants.

A massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence.

The Natural Scenes Dataset (NSD) is a large-scale fMRI dataset used for visual decoding. It features in-depth recordings of brain activities from 8 participants who passively...

Brain Diffusion for Visual Exploration: Cortical Discovery using Large Scale Generative Models

This dataset is used for EEG-based motor imagery classification.

The OpenBMI dataset is a large benchmark containing 2 sessions of 2-class MI-EEG data (left hand, right hand) from 54 right-handed healthy subjects.

This paper presents DADLNet, a dynamic domain adaptation based deep learning network framework for decoding motor imagery tasks.

The HYGRiP dataset contains fNIRS signals recorded during a unimanual hand grip task.

UKB dataset, a massive 7T fMRI dataset to bridge cognitive neuroscience and artificial intelligence.

fMRI-Shape dataset, a pioneering dataset specifically designed for Recon3DMind, which pairs fMRI data with 3D visuals.

The dataset used in this paper is a collection of EEG signals recorded from 6 healthy males using dry electrodes. The dataset is used for training and testing a convolutional...

Modeling the behavior of coupled networks is challenging due to their intricate dynamics. For example in neuroscience, it is of critical importance to understand the...

DynDepNet: Learning Time-Varying Dependency Structures from fMRI Data via Dynamic Graph Structure Learning