A magnetoencephalography (MEG) dataset from a frequency-tagging study of syntactic structure and lexical properties in language processing. The recordings are organized in BIDS format and are intended for investigating neural tracking of linguistic structure.
A multimodal naturalistic dataset from 52 healthy older Cantonese speakers (over 65 years) who listened to excerpts of The Little Prince in Cantonese during fMRI and EEG sessions. It includes task-based and resting-state fMRI, structural MRI, EEG, language history and lifetime experience questionnaires, cognitive task scores, and audio/text annotations with NLP-derived word-level predictors. The resource supports research on language comprehension and healthy aging in a non-Indo-European language.
High-density (256-channel) electrocorticography recorded at UCSF from patients undergoing clinical treatment for epilepsy while they read aloud consonant-vowel syllables from a list. The dataset comprises 31 recording blocks across four subject codes, with hand-marked annotations of syllable identity, onset, consonant-vowel transition and offset, plus rest baseline periods and anatomical electrode labels. It supports study of the functional organization of human ventral sensorimotor cortex during speech articulation and is organized as iEEG-BIDS with source NWB files retained unchanged.
MorseEEG-ATP is a multitask EEG dataset for studying auditory temporal processing and temporal pattern discrimination using Morse code stimuli. It contains synchronized 62-channel EEG and behavioral data from 32 healthy adults performing dot-count and first-last match discrimination tasks at three presentation rates (15, 30, and 40 characters per minute). The release includes raw and minimally preprocessed EEG, event annotations, behavioral measures, and analysis scripts in an EEG-BIDS structure.
EEG recordings from 27 normal-hearing adults listening to original monophonic music and 'chimeric' music, in which the pitch contours and note onset times of two melodies are exchanged. This distorts musical pattern while preserving the marginal pitch and timing statistics. The dataset supports investigation of pitch-time interactions in the electrophysiological encoding of music. It includes a click-train phase and a music phase with 33 trials per stimulus type.
A calibrated task subset of Kai J. Miller's library of human electrocorticographic (ECoG) data, comprising 111 recordings from 24 epilepsy patients with subdural electrode grids. Tasks include finger flexion, hand gestures, motor movement and imagery, and verb generation and noun reading. Data were recorded at 1 kHz and converted to BIDS in BrainVision format with source-documented amplitude calibration.
This dataset contains EEG recordings from deaf readers who read sentences containing semantic and syntactic violations, used to assess sensitivity to semantic and syntactic information during reading. Event-related potentials were recorded while participants viewed 180 sentences presented word by word, including subject-verb agreement, semantic, double, and word-order violations alongside correct control sentences.
NeuroMorph is a magnetoencephalography (MEG) dataset with high temporal resolution, designed for morpheme-based linguistic analysis. It contains recordings from 24 participants, totaling over 17 hours of data, acquired with a 208-channel KIT/Yokogawa magnetometer system. The dataset supports research on the neural processing of morphology during language comprehension.
This dataset contains behavioral events and intracranial EEG recordings from a delayed free recall task in which participants studied visually presented word lists, solved arithmetic distractor problems, and then freely recalled the words. Data were collected at clinical sites in collaboration with the Computational Memory Lab at the University of Pennsylvania. It is a preliminary cognitive electrophysiology study that precedes the FR1 and CatFR1 datasets.
Magnetoencephalography (MEG) recordings from participants listening to 16-item binary sound sequences of varying complexity, used to test a language-of-thought account of compression in human working memory. Sequences were defined by minimal description length (transition probabilities, chunking, or nested structures), and occasional deviant sounds probed participants' knowledge of each sequence. The data accompany a study reporting that MEG and fMRI responses scale with sequence complexity.