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A comprehensive EEG dataset comprising 54 healthy subjects performing three major brain-computer interface (BCI) paradigms—motor imagery, event-related potentials, and steady-state visually evoked potentials—across two sessions. The dataset investigates BCI illiteracy rates and performance variations, revealing that motor imagery exhibits the highest illiteracy rate (53.7%) compared to other paradigms, while all participants demonstrated proficiency with at least one BCI system.

Citations

98

Size

60.8 GB

Updated

Jun 3, 2026

Citations

10

Size

10.2 GB

Updated

May 18, 2026

…watched a video. - “Memory” question type is a memory testing question that…

Size

5.23 GB

Updated

Jun 18, 2026

…multi-source interference task (MSIT) and Sternberg’s memory task * Blood tests…

Citations

29

Size

240 GB

Updated

Jun 25, 2026

This dataset comprises simultaneous EEG and fNIRS recordings from 12 participants performing semantic imagery tasks involving silent naming and sensory-based imagination of animals and tools. Participants engaged in visual, auditory, and tactile perception tasks while neural activity was captured using a 64-channel BioSemi EEG system and a NIRx fNIRS imaging system with integrated optodes. The multimodal neuroimaging data supports research in semantic decoding and brain-computer interface applications.

Size

24.1 GB

Updated

Jun 24, 2026

Citations

80

Size

3.08 GB

Updated

Jun 2, 2026

This dataset comprises EEG recordings from healthy adults listening to naturalistic continuous speech from an audiobook. Brain activity was recorded using a 128-channel ActiveTwo EEG system while subjects listened to segments from "The Old Man and the Sea," with concurrent linguistic annotations including word and phoneme timing. The dataset supports investigation of cortical responses to naturalistic speech and phoneme-level processing mechanisms.

Size

18.7 GB

Updated

Jun 24, 2026

…https://github.com/gifale95/eeg_encoding OSF: https://osf.io/3jk45/

Size

159 GB

Updated

May 16, 2026

Beetl2021-A is a preprocessed motor imagery EEG dataset from the BEETL Competition Task 2 (NeurIPS 2021), comprising data from 3 healthy subjects collected during an online racing game (Cybathlon2020IC). The dataset contains 63-channel EEG recordings at 500 Hz with four-class motor imagery tasks (rest, left hand, right hand, feet) and serves as a benchmark for evaluating transfer learning and domain adaptation methods across heterogeneous EEG datasets and subjects. This dataset is part of a larger competition focused on advancing transfer learning for subject independence and cross-dataset generalization in brain-computer interfacing.

Size

216 MB

Updated

Jun 23, 2026

…watched a video. - “Memory” question type is a memory testing question that…

Citations

5

Size

5.12 GB

Updated

Jun 17, 2026