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This dataset comprises EEG recordings from 6 healthy participants performing imagined speech tasks involving two conditions: cooperate and independent word imagery. The study employed a motor imagery paradigm with auditory and visual cues, yielding 3,600 preprocessed trials (1,800 per class) recorded at 256 Hz from 64 channels. Data were analyzed using Riemannian manifold methods and relevance vector machines for brain-computer interface applications, achieving mean classification accuracy of 66.2±4.8%.

Participants

6

Channels

60 (10-10)

Citations

97

HED

v8.4.0

Size

1.27 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset comprises simultaneous 7T fMRI and EEG recordings from 25 native Mandarin Chinese speakers engaged in a naturalistic listening task featuring the Chinese version of Le Petit Prince under varying acoustic conditions (single-talker and multi-talker scenarios). The study investigates neural mechanisms of selective attention and speech comprehension in complex auditory environments, with participants attending to different talkers while maintaining fixation during fMRI acquisition. Preprocessed anatomical and functional MRI data, along with high-density EEG recordings, are provided in BIDS format alongside behavioral comprehension assessments. This dataset is derived from OpenNeuro ds005345 (v1.0.1) and has been curated for the NEMAR repository. Note: The README references French cohort considerations; however, the primary dataset composition consists of the 25 native Mandarin Chinese speakers described above.

Participants

26

Channels

64

Citations

3

Size

405 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset contains 64-channel EEG recordings and behavioural ratings from 4 participants who read and evaluated English-language poetic and non-poetic texts (Haiku, Senryu, and Control). It is a supplementary companion to Poetry Assessment EEG Dataset 1, including participants whose EEG data were acquired in segmented sessions and later concatenated, excluded from primary PSD analyses but retained for completeness. The study investigates neural and psychological correlates of aesthetic appreciation, emotional response, and creativity judgments in response to poetic language.

Participants

4

Channels

64 (10-10)

Size

4.29 GB

Version

v1.0.0

Updated

Aug 19, 2026

Participants

2

Size

1.87 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises neuroimaging data from a logical reasoning study conducted by the Cognitive and Computational Neuroscience Laboratory. The study investigates neural correlates of logical reasoning processes through multimodal brain imaging. Data were collected and organized according to BIDS standards for reproducibility and accessibility. This dataset is a NEMAR mirror of the OpenNeuro dataset ds003483.

Participants

21

Size

68.3 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises magnetoencephalography (MEG) recordings from 50 healthy French-speaking adults reading the French text of *Le Petit Prince* presented via rapid serial visual presentation (RSVP). Each participant completed 9 experimental runs while MEG, electrooculogram, and electrocardiogram signals were continuously recorded at 1000 Hz. High-resolution anatomical MRI scans were acquired for each participant to enable source localization and cortical surface reconstruction.

Participants

50

Citations

29

Size

299 GB

Version

v1.0.0

Updated

Jul 10, 2026

This dataset comprises preprocessed EEG recordings from 6 healthy participants performing imagined speech tasks, specifically discriminating between short and long words ('cooperate' vs 'in'). Data were acquired at 256 Hz using 64 EEG channels and processed with bandpass filtering (8-70 Hz), notch filtering (60 Hz), and artifact removal. The dataset contains 1,200 trials. Classification results (mean accuracy 73.3±8.9%) reported in the original Nguyen et al. 2017 study are provided for reference.

Participants

6

Channels

60 (10-10)

Citations

98

HED

v8.4.0

Size

1.15 GB

Version

v1.0.3

Updated

Aug 18, 2026

This dataset presents a multimodal collection of naturalistic fMRI, structural MRI, and EEG data from 52 healthy older Cantonese-speaking adults (over 65 years) listening to excerpts from 'The Little Prince' in Cantonese. It aims to address the underrepresentation of non-Indo-European languages and older populations in language neurobiology research, providing insights into brain-behavior relationships and healthy aging. The dataset includes rich behavioral, linguistic, and cognitive measures alongside detailed audio and text annotations.

Participants

52

Channels

64

Citations

3

Size

109 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises simultaneous EEG and fMRI recordings from 3 participants performing an inner speech task across 2 sessions each. Participants covertly produced 8 words presented in randomized order, each repeated 40 times, while EEG (64-channel, 5000 Hz) and fMRI (TR=2s) data were acquired concurrently. The dataset is intended to support research into the neural correlates of inner speech using multimodal neuroimaging.

Participants

3

Channels

64

Citations

1

Size

15.3 GB

Version

v1.0.0

Updated

Aug 19, 2026

This dataset comprises EEG recordings from 16 healthy participants performing motion visually evoked potential (mVEP) speller tasks using three different paradigm variants: overt gaze-directed, covert attention-based, and foveal motion center stimulation. The study investigates gaze-independent brain-computer interface communication through motion VEP-based P300 visual speller applications with two-level hierarchical selection from 30 symbols (6 symbols per level), employing 63-channel EEG recordings at 100 Hz sampling rate with preprocessed data including artifact rejection and baseline correction.

Participants

16

Channels

63 (10-10)

Citations

44

HED

v8.4.0

Size

3.79 GB

Version

v1.0.2

Updated

Aug 18, 2026