Simões Marco, Borra Davide, Santamaría-Vázquez Eduardo, Bittencourt-Villalpando Mayra, Krzemiński Dominik, Miladinović Aleksandar, Schmid Thomas, Zhao Haifeng, Amaral Carlos, Direito Bruno, Henriques Jorge, Carvalho Paulo, Castelo-Branco Miguel
Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), Institute of Nuclear Sciences Applied to Health (ICNAS), University of Coimbra, Coimbra, Portugal.
Centre for Informatics and Systems (CISUC), Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal.
Front Neurosci. 2020 Sep 18;14:568104. doi: 10.3389/fnins.2020.568104. eCollection 2020.
There is a lack of multi-session P300 datasets for Brain-Computer Interfaces (BCI). Publicly available datasets are usually limited by small number of participants with few BCI sessions. In this sense, the lack of large, comprehensive datasets with various individuals and multiple sessions has limited advances in the development of more effective data processing and analysis methods for BCI systems. This is particularly evident to explore the feasibility of deep learning methods that require large datasets. Here we present the BCIAUT-P300 dataset, containing 15 autism spectrum disorder individuals undergoing 7 sessions of P300-based BCI joint-attention training, for a total of 105 sessions. The dataset was used for the 2019 IFMBE Scientific Challenge organized during MEDICON 2019 where, in two phases, teams from all over the world tried to achieve the best possible object-detection accuracy based on the P300 signals. This paper presents the characteristics of the dataset and the approaches followed by the 9 finalist teams during the competition. The winner obtained an average accuracy of 92.3% with a convolutional neural network based on EEGNet. The dataset is now publicly released and stands as a benchmark for future P300-based BCI algorithms based on multiple session data.
脑机接口（BCI）缺乏用于多会话的P300数据集。公开可用的数据集通常受限于参与者数量少且BCI会话次数不多。从这个意义上讲，缺乏包含不同个体和多个会话的大型综合数据集，限制了BCI系统更有效数据处理和分析方法的发展。这在探索需要大型数据集的深度学习方法的可行性时尤为明显。在此，我们展示了BCIAUT-P300数据集，该数据集包含15名自闭症谱系障碍个体，他们接受了7次基于P300的BCI联合注意力训练，总共105个会话。该数据集用于2019年MEDICON期间举办的2019年国际医学与生物工程联合会（IFMBE）科学挑战赛，在两个阶段中，来自世界各地的团队试图基于P300信号实现尽可能高的目标检测准确率。本文介绍了该数据集的特点以及比赛期间9个决赛入围团队所采用的方法。获胜者使用基于EEGNet的卷积神经网络获得了92.3%的平均准确率。该数据集现已公开发布，成为未来基于多会话数据的基于P300的BCI算法的基准。
