NeuroMorph: A High-Temporal Resolution MEG Dataset for Morpheme-Based Linguistic Analysis
[ recordings from participants listening to hierarchically structured binary sound sequences of varying complexity. The study tests the language of thought hypothesis by examining how the human brain compresses regular sequences in working memory using recursive structures. Brain activity was recorded while participants processed sequences requiring different levels of complexity (transition probabilities, chunking, or nested structures) and responded to occasional deviant sounds probing sequence knowledge. Note: This dataset contains MEG data only; fMRI data from the same study are archived separately.
…a sequenceness analysis step was omitted from the published 2020 Nature Neuroscience…
…Temporal dynamics of short-term neural adaptation in human visual cortex. https…
This dataset comprises behavioral events and intracranial electrophysiological recordings from a categorized free recall task conducted across multiple clinical sites. Participants studied semantically organized word lists (12 items from 3 categories with paired exemplars), performed a distractor task, and freely recalled the words. The dataset includes monopolar and bipolar iEEG recordings with electrode localization information, supporting investigations of memory encoding and retrieval processes.
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on006107-blue…
…Large-scale intracranial recordings from naturalistic language > stimuli.* Advances in Neural Information…
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on004995-blue…
…This dataset aims to foster studies on neural decoding, perception, and cognitive…