THINGS-MEG is a magnetoencephalography (MEG) dataset for object cognition research, released by the National Institute of Mental Health of the National Institutes of Health (NIH), the Max Planck Institute for Human Cognition and Brain Sciences in Germany, and the University of Giessen Medical School, among other institutions. It records millisecond-level electromagnetic brain activity when subjects view images of objects, and is used to analyze the temporal dynamics of object processing.

This dataset belongs to THINGS-data, and the relevant research papers are as follows:THINGS-data, a multimodal collection of large-scale datasets for investigating object representations in human brain and behaviorTHINGS-data contains 1,854 object concepts and 26,107 manually selected and labeled images of natural scene objects.

In the MEG experiment, participants viewed a representative subset of the THINGS images. The experiment consisted of 12 independent sessions (N=4 participants) containing 22,448 unique images covering all 1,854 object categories. The images were presented rapidly and sequentially (with an average interval of approximately 1.5 ± 0.2 seconds), requiring participants to maintain central fixation throughout. To ensure attentional engagement, an exotic stimulus detection task was included, requiring participants to respond to occasionally appearing artificially generated anomalous images. Furthermore, a subset of images (n=200) was repeatedly presented in each session to analyze representation stability.

In addition to the core task-state MEG data, the following auxiliary data were also collected:

• T1-weighted structural MRI (for cortical source localization)
• Eye movement data recorded during the experiment (used to confirm that the subject maintained central fixation).