Quoron/EEG-semantic-text-relevance

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.

Submitted to ICLR 2025. The raw EEG data and the datasheet will be avaialble after acceptance to avoid disclosure of the authors' identity.

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.