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**PARADIGM:** The study uses the method of guided imagery to induce resting, eyes-closed participants using voice-guided imagination to enter distinct 15 emotion states during acquisition of high-density EEG data. During the study, participants listen to 15 voice recordings that each suggest imagining a scenario in which they have experienced -- or would experience the named target emotion. Some target emotions have positive valence (e.g., joy, happiness), others negative valence (e.g., sadn

Participants

34

Size

36.0 GB

## Audio-Visual Attention Shift Experiment **Project name:** [Sensory processing in aging] **Years the project ran:** 2007-2008 **Brief overview of experiment task:** The purpose of this Auditory-Visual Attention Shift study was to explore the effects of aging on selective attending and responding to auditory and visual stimulus differences using an interleaved dual-oddball audio-visual task design. EEG and EOG channels were acquired. **Data collection.** Scalp EEG data were collected fr

Participants

49

Size

7.70 GB

This dataset is a part of the data used for the study: 'Kandemir, A.L., Litvak, V., Florin, E., 2020. The comparative performance of DBS artefact rejection methods for MEG recordings, NeuroImage, 2020, https://doi.org/10.1016/j.neuroimage.2020.117057.' Please use the latest version of the dataset. For detailed information about measurement protocol please refer to https://doi.org/10.1016/j.neuroimage.2020.117057. Additional information about CTF Phantom measurement is provided below.

Participants

2

Size

20.1 GB

Link to published paper for this data resource: https://rdcu.be/b57kz This collection contains data from 26 human patients who underwent electrical stimulation during functional magnetic resonance imaging (es-fMRI). The patients had medically refractory epilepsy requiring surgically implanted intracranial electrodes in cortical and subcortical locations. One or multiple contacts on these electrodes were stimulated while simultaneously recording BOLD-fMRI activity in a block design. Multiple run

Participants

26

Size

18.6 GB

The MEG files contain a channel with triggers necessary for event marking and timing. Separate event files with onsets are provided in the participant directories for completeness only; the MEG triggers should be used for actual onsets in analysis. The delay between the trigger and the visual onset of an on-screen event sent by the projector is approximately 20 ms, as estimated using a photodiode. Memory phase triggers: At the onset of a trial, the first trigger represents the category (1-8) of

Participants

25

Size

1.68 MB

Welcome to the resting state EEG dataset collected at the University of San Diego and curated by Alex Rockhill at the University of Oregon. Please email arockhil@uoregon.edu before submitting a manuscript to be published in a peer-reviewed journal using this data, we wish to ensure that the data to be analyzed and interpreted with scientific integrity so as not to mislead the public about findings that may have clinical relevance. The purpose of this is to be responsible stewards of the data wi

Participants

31

Size

545 MB

Dataset: Joint EEG-fMRI recording during affective music listening. This dataset was recorded from 21 healthy adult participants viia a joint EEG-fMRI modality while they listened to a set of music stimuli chosen and generated to produce different affective (emotional) reponses. Participants self-reported their felt affective states as they listened to the music. The full experiment description can be found in our paper (Daly et.al., 2019). Data recorded in 2016 Published in 2019

Participants

21

Size

15.3 GB

Participants

10

Size

8.52 GB

0. Sections ------------ 1. Project 2. Dataset 3. Terms of Use 4. Contents 5. Method and Processing 1. PROJECT ------------ Title: Brain-Computer Music Interface for Monitoring and Inducing Affective States (BCMI-MIdAS) Dates: 2012-2017 Funding organisation: Engineering and Physical Sciences Research Council (EPSRC) Grant no.: EP/J003077/1 2. DATASET ------------ EEG data from an affective Music Brain-Computer: online real-time control. Description: This dataset accom

Participants

8

Size

2.60 GB

Participants

19

Size

6.10 GB