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Alljoined1 -- A dataset for EEG-to-Image decoding

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arxiv 2404.05553 v3 pith:65A5Y72Y submitted 2024-04-08 q-bio.NC cs.AI

Jonathan Xu , Bruno Aristimunha , Max Emanuel Feucht , Emma Qian , Charles Liu , Tazik Shahjahan , Martyna Spyra , Steven Zifan Zhang
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Nicholas Short Jioh Kim Paula Perdomo Ricky Renfeng Mao Yashvir Sabharwal Michael Ahedor Moaz Shoura Adrian Nestor
This is my paper · ORCID
classification q-bio.NCcs.AI
keywords datasetalljoined1datadecodingeeg-to-imageimagequalityresponses
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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. We publicly release the dataset and all code at https://linktr.ee/alljoined1.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Scaling laws for decoding images from brain activity

    eess.IV 2025-01 conditional novelty 6.0 of 10

    Across four non-invasive brain-imaging devices, image-decoding accuracy grows log-linearly with recorded data per subject and shows little gain from adding subjects.

  2. Comprehensive Review of EEG-to-Output Research: Decoding Neural Signals into Images, Videos, and Audio

    cs.CV 2024-12 reject novelty 2.0 of 10

    A PRISMA-style review of EEG-to-output decoding claims to analyze 1,800 studies but omits the flow diagram, study list, and quantitative synthesis needed to back that claim.

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