Freely download and explore a range of EEG studies including hundreds of raw recordings. Each study is fully documented with every event code described and every experimental paradigm explained in detail.

Click here to browse available studies.

Studies -> Imagined Emotion

Description: Subjects listened to voice recordings that suggest an emotional feeling and ask subjects to imagine an emotional scenario or to recall an experience in which they have felt that emotion before.

Attachment Download Edit Description
xml_style.xsl Download
study_description.html Download
libpeerconnection.log Download
debuglog.txt Download
study_description.xml Download
publications.zip Download
License.txt Download
Readme.txt Download
SCCN.jpg Download

What is HeadIT?

The Human Electrophysiology, Anatomic Data, and Integrated Tools (HeadIT) resource software allows researchers to store their raw EEG study data and (if they so choose) to enable named collaborator and/or public downloads.

The goal of HeadIT is to store and make available fully annotated raw data files for analysis, re-analysis, and meta-analysis. HeadIT uses EEG Study Schema (ESS) files to provide task, data collection, and subject metadata, including Hierarchical Event Descriptor (HED) tag descriptions of all identified experimental events.

How can I download the data?

You do not need a HeadIT account to download publicly available HeadIT studies. You must agree to the HeadIT Data Use Agreement and Terms of Use. You will need to create an account in order to enable your collaborators to share their private data with you.

Can I upload my data?

First create an account in the HeadIT system, then go to ‘Studies’ tab and click on the ‘New’ button.

Who developed HeadIT?

HeadIT is a project of the Swartz Center for Computational Neuroscience (SCCN) of the University of California, San Diego. Its development has been funded by U.S. National Institutes of Health grants R01-MH084819 (Makeig, Grethe PIs) and R01-NS047293 (Makeig PI).

EEG archiving and analysis: Scott Makeig, Nima Bigdely Shamlo, Matthew Grivich (SCCN)
Database programming: Jeffrey Grethe, Tadesse Sefer (CRBS, UCSD)
Web design: Yingying Wang