Alljoined1 -A dataset for EEG-to-Image decoding

Jonathan Xu (1) , Bruno Aristimunha (2, 3, 4) , Max Emanuel Feucht (5) , Emma Qian , Charles Liu (1) , Tazik Shahjahan (1) , Martyna Spyra , Steven Zifan Zhang (6) , Nicholas Short (6) , Jioh Kim (6) , Paula Perdomo (6) , Ricky Renfeng Mao (1) , Yashvir Sabharwal , Michael Ahedor , Moaz Shoura (6) , Adrian Nestor (6)

1 University of Waterloo [Waterloo]
2 TAU - TAckling the Underspecified
3 LISN - Laboratoire Interdisciplinaire des Sciences du Numérique
4 UFABC - Universidade Federal do ABC = Federal University of ABC = Université Fédérale de l'ABC [Brazil]
5 VU - Vrije Universiteit Amsterdam [Amsterdam]
6 University of Toronto

We present Alljoined1, a dataset built specifically for EEG-to-Image decoding. Recognizing that an extensive and unbiased sampling of neural responses to visual stimuli is crucial for image reconstruction efforts, we collected data from 8 participants looking at 10,000 natural images each. We have currently gathered 46,080 epochs of brain responses recorded with a 64-channel EEG headset. The dataset combines response-based stimulus timing, repetition between blocks and sessions, and diverse image classes with the goal of improving signal quality. For transparency, we also provide data quality scores.

Mots clés: Neurons and Cognition (q-bio.NC); Artificial Intelligence (cs.AI); FOS: Biological sciences; FOS: Computer and information sciences; I.5.1; I.6.3; I.2.6; K.3.2

Domaines: Informatique [cs]

Jonathan Xu, Bruno Aristimunha, Max Emanuel Feucht, Emma Qian, Charles Liu, et al.. Alljoined1 -A dataset for EEG-to-Image decoding. Workshop Data Curation and Augmentation in Medical Imaging at 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, May 2024, Settle, United States. ⟨10.48550/arXiv.2404.05553⟩. ⟨hal-04743819⟩