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event
int64 153k
16.3M
| word
stringlengths 1
17
| topic
stringclasses 29
values | selected_topic
stringclasses 25
values | semantic_relevance
int64 0
1
| interestingness
int64 1
9
| pre-knowledge
int64 1
9
| sentence_number
int64 1
6
| participant
stringclasses 7
values | eeg
array 2D |
|---|---|---|---|---|---|---|---|---|---|
154,180 | india | india | india | 1 | 6 | 4 | 1 | TRPB101 | [[-10.841060259135505,-10.632062420707495,-10.363389755796426,-10.03812512836641,-9.65998189087977,-(...TRUNCATED) |
155,576 | officially | india | india | 0 | 6 | 4 | 1 | TRPB101 | [[9.325834761653162,9.352347391744509,9.348355725174311,9.315289868281479,9.25475226806402,9.1685626(...TRUNCATED) |
156,972 | the | india | india | 0 | 6 | 4 | 1 | TRPB101 | [[7.175832587816289,7.1512135640973415,7.045324680002296,6.8555958117888505,6.580382765342812,6.2189(...TRUNCATED) |
158,372 | republic | india | india | 1 | 6 | 4 | 1 | TRPB101 | [[-1.1683129832397616,-1.1738408634024908,-1.1604396376939212,-1.1278177453250862,-1.075984686441013(...TRUNCATED) |
159,768 | of | india | india | 0 | 6 | 4 | 1 | TRPB101 | [[-2.0337258846898387,-2.742911577246869,-3.4226531090445973,-4.067143215373548,-4.671261451868331,-(...TRUNCATED) |
161,168 | india | india | india | 1 | 6 | 4 | 1 | TRPB101 | [[-4.3401094106179645,-4.293276639335403,-4.189685452288608,-4.029471795214887,-3.813451877757515,-3(...TRUNCATED) |
162,564 | is | india | india | 0 | 6 | 4 | 1 | TRPB101 | [[-2.5958169514329192,-3.5502727051602707,-4.50621208717035,-5.457131110849986,-6.396535423535895,-7(...TRUNCATED) |
163,964 | a | india | india | 0 | 6 | 4 | 1 | TRPB101 | [[0.5428521357194859,0.6570861612951572,0.7812142534056442,0.91560999521882,1.060409867691429,1.2153(...TRUNCATED) |
165,360 | country | india | india | 1 | 6 | 4 | 1 | TRPB101 | [[5.607137062798098,5.5681431154076115,5.518219451869366,5.458407073990687,5.389775350209125,5.31366(...TRUNCATED) |
166,756 | in | india | india | 0 | 6 | 4 | 1 | TRPB101 | [[1.2418949516053608,1.068148873158278,0.9008590798919355,0.740867538885073,0.5886853847011844,0.444(...TRUNCATED) |
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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 will be avaialble on January 25, 2025 AOE.
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.
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