An Electroencephalography (EEG) dataset utilizing rich text stimuli can advance the understanding of how the brain encodes semantic information and contribute to semantic decoding in brain-computer interface (BCI). Addressing the scarcity of EEG datasets featuring Chinese linguistic stimuli, we present the ChineseEEG dataset, a high-density EEG dataset complemented by simultaneous eye-tracking recordings. This dataset was compiled while 10 participants silently read approximately 11 hours of Chinese text from two well-known novels. This dataset provides long-duration EEG recordings, along with pre-processed EEG sensor-level data and semantic embeddings of reading materials extracted by a pre-trained natural language processing (NLP) model.

For more detailed information about our dataset, you can reach our preprint paper on bioRxiv: ChineseEEG: A Chinese Linguistic Corpora EEG Dataset for Semantic Alignment and Neural Decoding.

You can find the dataset via the ChineseNeuro Symphony community (CHNNeuro) in Science Data Bank platform (https://doi.org/10.57760/sciencedb.CHNNeuro.00007) or via Openneuro (https://openneuro.org/datasets/ds004952).

This repository contains all the code to reproduce the experiment and data processing procedure in our paper. It aims to provide a comprehensive paradigm for the establishment of an EEG dataset based on Chinese linguistic corpora. It seeks to facilitate the advancement of technologies related to EEG-based semantic decoding and brain-computer interfaces.

The project is mainly divided into four modules. The script cut_chinese_novel.py in the novel_segmentation_and_text_embeddings folder contains the code to prepare the stimulation materials from source materials. The script play_novel.py in the experiment module contains code for the experiment, including text stimuli presentation and control of the EGI device and Tobii Glasses 3 eye-tracker. The script preprocessing.py in data_preprocessing_and_alignment module contains the main part of the code to apply pre-processing on EEG data. The script align_eeg_with_sentence.py in the same module contains code to align the EEG segments with corresponding text contents and text embeddings. The docker module contains the Docker image required for deploying and running the code, as well as tutorials on how to use Docker for environment deployment. For detailed information on each module, please refer to the README document in the respective module.

Our EEG recording and pre-processing pipeline is as follows:

During the experiment, The EEG (electroencephalography) data were collected by a 128-channel EEG system with Geodesic Sensor Net (EGI Inc., Eugene, OR, USA, Geodesic EEG System 400 series (egi.com)). The montage system of this device is GSN-HydroCel-128.We recorded the data at a sampling rate of 1000 Hz.

The 128-channel EEG system with Geodesic Sensor Net (GSN) by EGI is a sophisticated brain activity recording tool designed for high-resolution neuroscientific research. This system features an array of evenly spaced sensors providing complete scalp coverage, ensuring detailed spatial data collection without the need for interpolation. Coupled with the advanced Net Amps 400 amplifiers and intuitive Net Station 5 software, it delivers low noise, high sensitivity EEG data acquisition, and powerful data analysis capabilities, making it an ideal choice for dynamic and expanding research environments.

We utilized Tobii Pro Glasses 3 (Tobii Pro Glasses 3 | Latest in wearable eye tracking - Tobii) to record the participants' eye movement trajectories to inspect whether they followed the instructions of the experiment, that is, their gaze should move along with the red highlighted text.

The Tobii Pro Glasses 3 are advanced wearable eye trackers. They are capable of capturing natural viewing behavior in real-world environments, providing powerful insights from a first-person perspective. The device features 16 illuminators and four eye cameras integrated into scratch-resistant lenses, a wide-angle scene camera, and a built-in microphone, allowing for a comprehensive capture of participant behavior and environmental context. Its eye tracking is reliable across different populations, unaffected by eye color or shape. The Tobii Pro Glasses 3 operates with a high sampling rate of 50 Hz or 100 Hz. It supports a one-point calibration procedure.

In the preparation phase of the experiment, we initially fitted participants with EEG caps and eye trackers, maintaining a distance of 67 cm from the screen. We emphasized to the participants that they should keep their heads still during the experiment, and their gaze should follow the red highlighted text as shown in the figure.

After ensuring the participants fully understood the instructions, we commenced the experimental procedure. Initially, there was an eye tracker calibration phase, followed by a practice reading phase, and finally the formal reading phase. Each formal reading phase lasted for approximately 30 minutes. The experimental setup is as below:

Typically, you can follow steps below to execute the code for preparing experimental materials, conducting the experiment, and carrying out subsequent data analysis.

Firstly, please ensure that your code running environment is properly set up. You have the option to create a Docker container for this purpose or directly install the necessary packages on your personal computer.

If you choose to use Docker, you can refer to the detailed tutorial provided here. If you plan to install the packages in your local environment, the required packages and their corresponding version information can be found in the requirement.txt file located in the project's root directory.

This step primarily involves preparing the textual reading materials needed for the experiment. You need to first convert your materials into the specific format below:

Chinese_novel.txt
Ch0
This is the preface of the novel
Ch1
Chapter 1 of the novel
Ch2
Chapter 2 of the novel
...
...
...

then run the cut_Chinese_novel.py script located in the novel_segmentation folder to perform sentence segmentation of the novel text:

python cut_Chinese_novel.py --divide_nums=<chapter numbers of the cutting point> --Chinese_novel_path=<path to your .txt file of the novel>

We have uploaded the text materials we use in our experiment to the text materials release, including the Chinese versions of two well-known novels, The Little Prince and Garnett Dream.

For detailed information on format requirements and script execution commands, please visit the novel_segmentation_and_text_embeddings module for more details.

Once we have obtained the text materials cut into the specific format, we can run the experimental program using play_novel.py in the experiment module. This program will present these text materials according to a specific experimental paradigm and record the participants' EEG and eye movement data. Before running the program, please ensure that the path to the text materials is correctly set and that the EEG and eye-tracking devices are properly connected. Use the following command to run the program:

python play_novel.py --add_mark --add_eyetracker  --preface_path=<your preface path> --host_IP=<host IP> --egi_IP=<egi IP> --eyetracker_hostname=<eyetracker serial number> --novel_path=<your novel path> --isFirstSession

For detailed information on the specific experimental paradigm, related parameter settings, and more, please refer to the experiment module for further details.

After completing the experimental data collection for all participants, we can use the preprocessing.py in the data_preprocessing module for data preprocessing. Our preprocessing workflow includes a series of steps such as data segmentation, downsampling, filtering, bad channel interpolation, independent component analysis (ICA), and re-referencing. During the bad channel interpolation and ICA phases, we have implemented automated algorithms, but we also provide options for manual intervention to ensure accuracy. All parameters for these methods can be modified by adjusting the settings in the code.

For detailed information on the preprocessing workflow, explanations of the code, and parameter settings, please refer to the data_preprocessing module.

We offer the embeddings of the reading materials. The text stimuli in each run has a corresponding embedding file saved in .npy format. These text embeddings provide a foundation for a series of subsequent studies, including the alignment analysis of EEG and textual data in the representation space, as well as tasks like EEG language decoding. For detailed information, please refer to the novel_segmentation_and_text_embeddings module.

After you have your texts, text embeddings and runs of EEG data, you can align them to do subsequent analysis. We offer you code to align the EEG data to its corresponding texts and embeddings. For detailed information, please refer to the data_preprocessing_and_alignment module.

• Mou Xinyu - Coder for all parts of the project, Data processing.

• He Cuilin - Experiment conductor, Data processing.

• Tan Liwei - Experiment conductor, Data processing.

• Zhang Jianyu - Coder for Chinese corpus segmentation and EEG random masking.

• Tian Yan - Experiment conductor

• Chen Yizhe - Experimental instrument debugging

  Feel free to contact us if you have any questions about the project !!!

• Wu Haiyan - University of Macau

• [Liu Quanying] - Southern University of Science and Technology

• Wang Xindi

• [Wang Qing] - Shanghai Jiao Tong University

• [Chen Zijiao] - National University of Singapore

• [Yang Yu-Fang] - Freie Universität Berlin

• [Hu Chuanpeng] - Nanjing Normal University

• [Xu Ting] - Center for the Integrative Developmental Neuroscience, Child Mind Institute, New York

• [Cao Miao] - Swinburne University of Technology

• Liang Huadong - iFLYTEK Co., LTD

This work was mainly supported by the MindD project of Tianqiao and Chrissy Chen Institute(TCCI), the Science and Technology Development Fund (FDCT) of Macau [0127/2020/A3, 0041/2022/A], the Natural Science Foundation of Guangdong Province(2021A1515012509), Shenzhen-Hong Kong-Macao Science and Technology Innovation Project (Category C) (SGDX2020110309280100), and the SRG of University of Macau (SRG2020-00027-ICI). We also thank all research assistants who provided general support in participant recruiting and data collection.