Datasets:
metadata
license: apache-2.0
dataset_info:
config_name: data
features:
- name: event
dtype: int64
- name: word
dtype: string
- name: topic
dtype: string
- name: selected_topic
dtype: string
- name: semantic_relevance
dtype: int64
- name: interestingness
dtype: int64
- name: pre-knowledge
dtype: int64
- name: sentence_number
dtype: int64
- name: participant
dtype: string
- name: eeg
dtype:
array2_d:
shape:
- 32
- 2001
dtype: float64
splits:
- name: train
num_bytes: 11925180913
num_examples: 23270
download_size: 11927979870
dataset_size: 11925180913
configs:
- config_name: data
data_files:
- split: train
path: data/train-*
default: true
task_categories:
- text-classification
- token-classification
language:
- en
size_categories:
- 10K<n<100K
We release a novel dataset containing 23,270 time-locked (0.7s) word-level EEG recordings acquired from participants who read both text that was semantically relevant and irrelevant to self-selected topics.
The raw EEG data and the datasheet are available at https://osf.io/xh3g5/.
See code repository for benchmark results.
EEG data acquisition:
Explanations of the variables:
- event corresponds to a specific point in time during EEG data collection and represents the onset of an event (presentation of a word)
- word is a word read by the participant
- topic is the topic of the document to which the word belongs to
- selected topic indicates the topic the participant has selected
- semantic relevance indicates whether the word is semantically relevant (expressed as 1) or semantically irrelevant (expressed as 0) to the topic selected by the participant
- interestingness indicates the participant's interest in the topic of a document
- pre-knowledge indicates the participant's previous knowledge about the topic of the document
- sentence number represents the sentence number to which the word belongs
- eeg - brain recordings having a shape of 32 x 2001 for each word
The dataset can be downloaded and used as follows:
import numpy as np
from datasets import load_dataset
# Load the cleaned version of the dataset
d = load_dataset("Quoron/EEG-semantic-text-relevance", "data")
# See the structure of the dataset
print(d)
# Get the first entry in the dataset
first_entry = d['train'][0]
# Get EEG data as numpy array in the first entry
eeg = np.array(first_entry['eeg'])
# Get a word in the first entry
word = first_entry['word']
We recommend using the Croissant metadata to explore the dataset.
If you use our dataset, please cite:
@unpublished{Gryshchuk2025_EEG-dataset,
author = {Vadym Gryshchuk and Michiel Spapé and Maria Maistro and Christina Lioma and Tuukka Ruotsalo},
title = {An EEG dataset of word-level brain responses for semantic text relevance},
year = {2025},
note = {submitted for publication}
}