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ChineseEEG is a high-density EEG and simultaneous eye-tracking dataset collected from 10 participants silently reading two Chinese novels over approximately 11 hours. The dataset includes raw and multiple stages of pre-processed EEG data, along with BERT-base-chinese text embeddings of the reading materials, designed to support research on semantic alignment between NLP model representations and neural activity. It provides a resource for studying naturalistic language processing and neural decoding using Chinese linguistic stimuli.

Participants

10

Channels

128 (egi-geodesic)

Citations

24

Size

697 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises electroencephalography (EEG) recordings investigating the neural correlates of syntactic structure and lexical properties using frequency tagging methodology. Participants were presented with linguistic stimuli while EEG activity was recorded to identify frequency-specific neural responses associated with different linguistic features. The dataset provides raw neurophysiological data suitable for studying the temporal dynamics of language processing at the neural level.

Participants

34

Citations

1

Size

195 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises raw 64-channel EEG, ECG, PPG, and behavioral data collected from 30 healthy young adults during resting-state and verbal working memory tasks. Participants completed a digit-span serial recall task under four stimulus presentation modes (simultaneous, fast, fast+delay, and slow sequential), enabling investigation of neural and physiological correlates of encoding, maintenance, and retrieval. The dataset supports research on working memory load classification and the relationship between oscillatory brain activity, autonomic signals, and behavioral performance.

Participants

30

Channels

63 (10-10)

Citations

5

Size

41.4 GB

Version

v1.0.0

Updated

Aug 19, 2026

Imported from OpenNeuro ds005262

Participants

12

Size

689 MB

Version

v1.0.0

Updated

Jul 10, 2026

A P300-based brain-computer interface dataset comprising EEG recordings from 13 healthy participants performing a visual speller task using covert spatial attention and feature attention mechanisms. The dataset includes data from three speller variants (Hex-o-Spell, Cake Speller, and Center Speller) with 63-channel recordings at 250 Hz sampling rate. Preprocessed data includes downsampling, filtering, and baseline correction. The original study reported 97% accuracy on the Center Speller variant.

Participants

13

Channels

63 (10-05)

Citations

81

HED

v8.4.0

Size

9.27 GB

Version

v1.0.2

Updated

Aug 18, 2026

This dataset comprises preprocessed EEG recordings from 8 healthy subjects performing imagined speech tasks involving three vowel phonemes (a, i, u). Participants received auditory and visual cues to imagine speaking each vowel, with 64-channel EEG data acquired at 256 Hz. The dataset includes 2,400 trials generating 7,200 overlapping 2-second epochs (8 subjects × 300 trials × 3 overlapping epochs per trial) analyzed using Riemannian manifold-based feature extraction and relevance vector machine classification for brain-computer interface applications, achieving approximately 49% mean accuracy across a 10-fold cross-validation scheme.

Participants

8

Channels

60 (10-10)

Citations

96

HED

v8.4.0

Size

2.58 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset comprises EEG recordings from a modified Sternberg working memory task designed to dissociate neural mechanisms of verbal information retention from those supporting active manipulation. Participants performed two task types—simple retention and alphabetization—across three memory loads (5, 6, or 7 letters), enabling investigation of temporally distinct oscillatory signatures underlying different working memory processes. Please cite: Pavlov, Y. G., & Kotchoubey, B. (2021). Temporally distinct oscillatory codes of retention and manipulation of verbal working memory. Scientific Reports, 11, 11230. https://doi.org/10.1038/s41598-020-72940-5

Participants

156

Channels

19 (10-20)

Citations

24

Size

20.3 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises 128-channel EEG recordings collected during a speech production paradigm designed to investigate neural correlates of overt, minimally overt, and covert speech. Participants produced one of five color words under each speech condition, with additional peripheral recordings including EOG, EMG, microphone, and display markers to support speech decoding analyses. The dataset includes both calibration sessions for decoder training and online sessions for real-time decoding evaluation.

Participants

3

Channels

128 (other)

Citations

1

Size

1.62 GB

Version

v1.0.0

Updated

Aug 18, 2026

A P300-based brain-computer interface dataset for home appliance control, comprising EEG recordings from 14 healthy subjects performing a visual oddball task with 31-channel recordings at 500 Hz sampling rate. The dataset includes 50 training and 30 testing blocks per subject, with target and non-target stimulus classifications presented via LCD display in an online BCI paradigm.

Participants

14

Channels

31 (10-10)

Citations

9

HED

v8.4.0

Size

644 MB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset comprises EEG recordings paired with vocal audio data collected during an overt speech production task, in which participants vocalized visually presented text. Sessions are organized by recording date, with multiple runs per day, supporting research on brain-to-speech decoding and neural correlates of speech production. The dataset is intended to facilitate development and evaluation of EEG-based speech brain-computer interface models.

Participants

3

Channels

128 (other)

Citations

2

Size

49.6 GB

Version

v1.0.0

Updated

Aug 18, 2026