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

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

TL;DR

The dataset combines response-based stimulus timing, repetition between blocks and sessions, and diverse image classes with the goal of improving signal quality with the goal of improving signal quality.

Abstract

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.

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

TL;DR

The dataset combines response-based stimulus timing, repetition between blocks and sessions, and diverse image classes with the goal of improving signal quality with the goal of improving signal quality.

Abstract

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.
Paper Structure (16 sections, 4 figures)

This paper contains 16 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Related Work
  3. EEG-to-Image Datasets
  4. fMRI-Image Datasets
  5. Methods and Materials
  6. Participants
  7. Stimuli
  8. Procedure
  9. Hardware Setup
  10. Pre-processing
  11. Filtering
  12. Independent Component Analysis (ICA)
  13. Epoching
  14. Artifact correction
  15. Baseline correction
  16. ...and 1 more sections

Figures (4)

  • Figure 1: Top 12 most frequently occurring supercategories in our dataset.
  • Figure 2: Schematic overview of the structure of trials, blocks, and sessions. Each of the 120 block-specific NSD images is presented twice within each block, and each of the 8 session-specific blocks is presented twice within each session. Each participant performed two sessions on different days. Each of the 10 sessions thus consists of 960 NSD images repeated four times within and across blocks, totaling 9600 unique NSD images per participant.
  • Figure 3: Experimental setup with monitor 80 cm from participant.
  • Figure 4: EEG topographic maps and corresponding signals at all 64 electrodes averaged over a) 3823 events for the fifth participant (left) and b) across all sessions for all participants (43070 events) in the Alljoined1 dataset (right), highlighting individual and common brain activity patterns associated with image presentation. An event is defined as a specific time point in the experiment.