Alljoined1 -- A dataset for EEG-to-Image decoding
This provides a new dataset for researchers in neuroscience and AI working on brain-computer interfaces, but it is incremental as it focuses on data collection rather than novel methods.
The authors tackled the problem of EEG-to-Image decoding by creating Alljoined1, a dataset with 46,080 epochs from 8 participants viewing 10,000 natural images each, aimed at improving signal quality for image reconstruction.
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.