…CSP, deep_learning - **Frequency bands**: bandpass=[5.0, 30.0] Hz - **Spatial…
…Commande robuste d'un effecteur par une interface cerveau machine EEG asynchrone…
A 60-channel EEG dataset from 10 healthy, BCI-naive participants performing simple limb motor imagery (left hand, right hand, feet) and compound limb motor imagery (both hands, left hand with right foot, right hand with left foot), plus a rest condition. The recordings were used to examine EEG oscillatory patterns (ERD/ERS), cognitive processes, and effective connectivity during motor imagery. This is a preprocessed (bandpass filtered, downsampled to 200 Hz) BIDS version curated through MOABB.
…EEG, reach and grasp, motor execution, brain-machine interface, freewill References ---------- Thapa…
Chisco-2.0 is an EEG dataset of imagined speech recorded from two participants (sub-01 and sub-02). The raw recordings are provided in EDF format and organized according to the BIDS standard. The dataset is intended to support research on decoding imagined speech from brain activity.
…brain-computer interfaces, machine learning, random forests, regularized linear discriminant analysis, sensorimotor…
The FRESH Motor Dataset is a functional near-infrared spectroscopy (fNIRS) dataset organized in BIDS format. It is associated with a motor task paradigm. The README gives little detail, and the record is a NEMAR-hosted version of the OpenNeuro deposit ds005963.
EEG recordings from 20 healthy participants who wrote ten letters (a, d, e, f, j, n, o, s, t, v) with the right index finger after a visual cue. The dataset supports classification of handwritten characters, either directly from low-frequency and broadband EEG or with a two-step approach that first decodes continuous hand kinematics and then classifies the letter. It is intended for research on non-invasive brain-computer interfaces for communication.
…e.g., an electroencephalogram, using machine learning is key to brain-computer…
This dataset contains multimodal recordings from an n-back cognitive workload task with four difficulty levels (1-back to 4-back). It includes 19-channel mobile EEG, ECG, and Pupil Labs eye-tracking and pupillometry data, synchronized with Lab Streaming Layer. Twenty participants were recorded and 18 are released, with two excluded for data quality failures. The data were converted from XDF to BIDS.