…48 affective images paired with semantically matching sounds. Heart rate deceleration was…
Chisco-2.0 is an EEG dataset of imagined speech recorded from two participants (sub-01 and sub-02). The raw recordings are provided in EDF format and organized according to the BIDS standard. The dataset is intended to support research on decoding imagined speech from brain activity.
…computer interface (BCI) research and semantic decoding methods can be effectively conducted…
…relatively little on lexical or semantic knowledge in untrained listeners. Dataset Content…
MEG dataset from a logical reasoning study collected by the Cognitive and Computational Neuroscience Laboratory (UPM - UCM). The dataset is organized in BIDS format and mirrored on NEMAR from the OpenNeuro deposit ds003483.
…Participants responded whether the target word was semantically related or unrelated to…
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.
EEG recordings from twenty-nine participants who listened to song melodies and responded as soon as a song felt familiar. Participants then tried to identify the song (title, artist, or lyrics) and chose the title from a four-option multiple choice display, receiving visual feedback on correctness. The dataset includes synchronized audio reconstructions of what each participant heard, supporting research on music familiarity and recognition.
This dataset contains independent 3-second single-channel intracranial EEG (stereo-EEG) clips from St. Anne's University Hospital (FNUSA), annotated for classification of physiological and pathological graphoelements and artifactual signals. It is a BIDS conversion of the segmented .mat archives released with Nejedly et al. 2020. Clips from one subject and one SEEG contact are concatenated per run at 5000 Hz, with events preserving labels, anatomy, reviewer, seizure onset zone flag, and electrode type.
EEG recordings from six healthy participants performing imagined speech of long words, comprising two word classes (cooperate and independent), with 64 channels recorded at 256 Hz. Trials were cued by auditory beeps and visual cues, and the data were analyzed with Riemannian manifold covariance features and relevance vector machines for brain-computer interface classification. The dataset is re-hosted in BIDS format from the MOABB benchmark collection.