100 results for "neural sequenceness" · page 4 of 10 · ranked by relevance
…Smit, S. & Varlet, M. (2025). Neural decoding of competitive decision-making in…
This dataset comprises event-related potential (ERP) recordings from 13 healthy subjects performing a visual matrix speller task using a calibrationless brain-computer interface approach. The study introduces learning from label proportions (LLP), an unsupervised classification method that exploits known target/non-target stimulus ratios to enable online BCI operation without prior calibration. Subjects performed copy-spelling tasks using a 6×7 character grid across three sessions, achieving 84.5% character accuracy without labeled training data.
…Three behavioural tasks and neural measurements (EEG) using these stimuli. - Spontaneous dissimilarity…
This dataset contains raw EEG recordings from 15 participants who each completed three auditory oddball paradigms within a single experimental session: the Optimum-1 sequence for mismatch negativity (MMN), the learning-oddball sequence for P3b, and the local-global paradigm for assessing local and global auditory novelty effects. The dataset was collected to compare within-individual sensitivity of MMN and P3b responses across these different oddball paradigms. It includes original stimulation triggers, corrected event files, and source stimulation sequence definitions used in the analysis.
…Identification of perceived sentences using deep neural networks in EEG. Journal of…
…separate out predictive neural activity from sensory evoked neural activity. The study…
A synchronized multimodal neuroimaging dataset containing concurrent fMRI and MEG recordings from 12 Mandarin Chinese speakers during naturalistic story listening, supplemented with high-resolution structural imaging, diffusion MRI, and resting-state fMRI. The dataset includes rich linguistic annotations of stimuli encompassing word frequencies, syntactic structures, temporal alignments, and embeddings from multiple pre-trained language models, enabling comprehensive investigation of neural mechanisms underlying language processing.
…Naming is Shaped by Early Facilitative and Late Compensatory Neural Interactions: An…
NeuroMorph is a high-temporal resolution MEG dataset designed for morpheme-based linguistic analysis, comprising recordings from 24 subjects totaling over 17 hours of data. Data were collected using a KIT/Yokogawa MEG system to investigate visual language and cognitive processing, with a focus on morphological structure in written language comprehension.
[![DOI](https://img.shields.io/badge/DOI-10.82901%2Fnemar.on005059-blue…