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--- |
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license: apache-2.0 |
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dataset_info: |
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config_name: data |
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features: |
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- name: event |
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dtype: int64 |
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- name: word |
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dtype: string |
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- name: topic |
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dtype: string |
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- name: selected_topic |
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dtype: string |
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- name: semantic_relevance |
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dtype: int64 |
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- name: interestingness |
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dtype: int64 |
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- name: pre-knowledge |
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dtype: int64 |
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- name: sentence_number |
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dtype: int64 |
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- name: participant |
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dtype: string |
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- name: eeg |
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dtype: |
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array2_d: |
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shape: |
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- 32 |
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- 2001 |
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dtype: float64 |
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splits: |
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- name: train |
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num_bytes: 11925180913 |
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num_examples: 23270 |
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download_size: 11927979870 |
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dataset_size: 11925180913 |
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configs: |
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- config_name: data |
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data_files: |
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- split: train |
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path: data/train-* |
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default: true |
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task_categories: |
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- text-classification |
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- token-classification |
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language: |
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- en |
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size_categories: |
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- 10K<n<100K |
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--- |
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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. |
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Submitted to ICLR 2025. The raw EEG data and the datasheet will be avaialble after acceptance to avoid disclosure of the authors' identity. |
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See [code repository][1] for benchmark results. |
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EEG data acquisition: |
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Explanations of the variables: |
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- **event** corresponds to a specific point in time during EEG data collection and represents the onset of an event (presentation of a word) |
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- **word** is a word read by the participant |
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- **topic** is the topic of the document to which the **word** belongs to |
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- **selected topic** indicates the topic the participant has selected |
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- **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 |
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- **interestingness** indicates the participant's interest in the topic of a document |
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- **pre-knowledge** indicates the participant's previous knowledge about the topic of the document |
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- **sentence number** represents the sentence number to which the word belongs |
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- **eeg** - brain recordings having a shape of 32 x 2001 for each word |
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The dataset can be downloaded and used as follows: |
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```py |
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import numpy as np |
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from datasets import load_dataset |
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# Load the cleaned version of the dataset |
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d = load_dataset("Quoron/EEG-semantic-text-relevance", "data") |
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# See the structure of the dataset |
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print(d) |
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# Get the first entry in the dataset |
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first_entry = d['train'][0] |
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# Get EEG data as numpy array in the first entry |
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eeg = np.array(first_entry['eeg']) |
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# Get a word in the first entry |
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word = first_entry['word'] |
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``` |
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We recommend using the Croissant metadata to explore the dataset. |
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[1]: https://anonymous.4open.science/r/EEG-semantic-text-relevance-651D |
