A multi-day and high-quality EEG dataset for motor imagery brain-computer interface.

IF 5.8 2区综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES

Scientific Data Pub Date : 2025-03-23 DOI:10.1038/s41597-025-04826-y

Banghua Yang, Fenqi Rong, Yunlong Xie, Du Li, Jiayang Zhang, Fu Li, Guangming Shi, Xiaorong Gao

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引用次数: 0

Abstract

A key challenge in developing a robust electroencephalography (EEG)-based brain-computer interface (BCI) is obtaining reliable classification performance across multiple days. In particular, EEG-based motor imagery (MI) BCI faces large variability and low signal-to-noise ratio. To address these issues, collecting a large and reliable dataset is critical for learning of cross-session and cross-subject patterns while mitigating EEG signals inherent instability. In this study, we obtained a comprehensive MI dataset from the 2019 World Robot Conference Contest-BCI Robot Contest. We collected EEG data from 62 healthy participants across three recording sessions. This experiment includes two paradigms: (1) two-class tasks: left and right hand-grasping, (2) three-class tasks: left and right hand-grasping, and foot-hooking. The dataset comprises raw data, and preprocessed data. For the two-class data, an average classification accuracy of 85.32% was achieved using EEGNet, while the three-class data achieved an accuracy of 76.90% using deepConvNet. Different researchers can reuse the dataset according to their needs. We hope that this dataset will significantly advance MI-BCI research, particularly in addressing cross-session and cross-subject challenges.

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运动图像脑机接口的多天高质量脑电图数据集。

开发基于脑电图（EEG）的鲁棒性脑机接口（BCI）的一个关键挑战是在多天内获得可靠的分类性能。特别是，基于脑电图的运动图像（MI）BCI 面临着变异性大和信噪比低的问题。要解决这些问题，收集大量可靠的数据集对于学习跨会期和跨受试者模式至关重要，同时还能减轻脑电信号固有的不稳定性。在本研究中，我们从 2019 年世界机器人大会竞赛--BCI 机器人竞赛中获得了一个全面的 MI 数据集。我们收集了 62 名健康参赛者在三个记录时段的脑电图数据。该实验包括两种范式：（1）两类任务：抓握左手和右手；（2）三类任务：抓握左手和右手，以及勾脚。数据集包括原始数据和预处理数据。对于两类数据，使用 EEGNet 实现了 85.32% 的平均分类准确率，而对于三类数据，使用 deepConvNet 实现了 76.90% 的准确率。不同的研究人员可以根据自己的需要重复使用该数据集。我们希望该数据集能极大地推动 MI-BCI 研究，尤其是在解决跨会期和跨主体挑战方面。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Scientific Data Social Sciences-Education

CiteScore

11.20

自引率

4.10%

发文量

689

审稿时长

16 weeks

期刊介绍： Scientific Data is an open-access journal focused on data, publishing descriptions of research datasets and articles on data sharing across natural sciences, medicine, engineering, and social sciences. Its goal is to enhance the sharing and reuse of scientific data, encourage broader data sharing, and acknowledge those who share their data. The journal primarily publishes Data Descriptors, which offer detailed descriptions of research datasets, including data collection methods and technical analyses validating data quality. These descriptors aim to facilitate data reuse rather than testing hypotheses or presenting new interpretations, methods, or in-depth analyses.