Datasets:
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---
dataset_info:
features:
- name: image
dtype: image
- name: conditioning_image
dtype:
array2_d:
shape:
- 128
- 440
dtype: float32
- name: caption
dtype: string
- name: label_folder
dtype: string
- name: label
dtype: int32
- name: subject
dtype: int32
splits:
- name: train
num_bytes: 2518963666.125
num_examples: 7959
- name: test
num_bytes: 626837215.625
num_examples: 1987
- name: validation
num_bytes: 630326693.75
num_examples: 1994
download_size: 3410667247
dataset_size: 3776127575.5
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: train
path: data/train-*
- split: validation
path: data/validation-*
language:
- en
tags:
- EEG
- Image
pretty_name: EEG-Based Visual Classification Dataset
---
# EEG-Based Visual Classification Dataset
This dataset is used in the paper "Guess What I Think: Streamlined EEG-to-Image Generation with Latent Diffusion Models" available on [arxiv](https://arxiv.org/abs/2410.02780) and the code is available at this [repository](https://github.com/LuigiSigillo/GWIT).
The dataset is constructed to be used in controlnet scenarios.
This dataset includes EEG data from 6 subjects. The recording protocol included 40 object classes with 50 images each, taken from the ImageNet dataset, giving a total of 2,000 images. Visual stimuli were presented to the users in a block-based setting, with images of each class shown consecutively in a single sequence. Each image was shown for 0.5 seconds. A 10-second black screen (during which we kept recording EEG data) was presented between class blocks. The collected dataset contains in total 11,964 segments (time intervals recording the response to each image); 36 have been excluded from the expected 6×2,000 = 12,000 segments due to low recording quality or subjects not looking at the screen, checked by using the eye movement data. Each EEG segment contains 128 channels, recorded for 0.5 seconds at 1 kHz sampling rate, represented as a 128×L matrix, with L about 500 being the number of samples contained in each segment on each channel. The exact duration of each signal may vary, so we discarded the first 20 samples (20 ms) to reduce interference from the previous image and then cut the signal to a common length of 440 samples (to account for signals with L < 500). The dataset includes data already filtered in three frequency ranges: 14-70Hz, 5-95Hz and 55-95Hz.
Download dataset Link to dataset files: https://tinyurl.com/eeg-visual-classification
```python
from datasets import load_dataset
split = "train" #could be one of "train", "test", "validation"
data = load_dataset('luigi-s/EEG_Image_CVPR_ALL_subj', split=split).with_format(type='torch')
eeg = data[0]['conditioning_image']
image = data[0]['image']
caption = data[0]['caption'])
subject = data[0]['subject'])
label = data[0]['label'])
``` |