论文标题
部分可观测时空混沌系统的无模型预测
A streamable large-scale clinical EEG dataset for Deep Learning
论文作者
论文摘要
深度学习彻底改变了各个领域，包括计算机视觉，自然语言处理以及生物医学研究。在神经科学领域，特别是在电生理神经影像学领域内，研究人员开始探索在没有广泛的功能工程的情况下对其数据进行深入学习的预测。大规模数据集的可用性是允许深度学习模型实验的关键方面。我们正在发布第一个大规模临床脑电图数据集，该数据集简化了数据访问和管理的深度学习。该数据集包含从健康脑网络的1,574名少年参与者的收集中制备的眼睛关闭的脑电图数据。我们展示了一个集成此框架的用例，并讨论为什么向社区提供这种神经信息基础设施对于未来的科学发现至关重要。
Deep Learning has revolutionized various fields, including Computer Vision, Natural Language Processing, as well as Biomedical research. Within the field of neuroscience, specifically in electrophysiological neuroimaging, researchers are starting to explore leveraging deep learning to make predictions on their data without extensive feature engineering. The availability of large-scale datasets is a crucial aspect of allowing the experimentation of Deep Learning models. We are publishing the first large-scale clinical EEG dataset that simplifies data access and management for Deep Learning. This dataset contains eyes-closed EEG data prepared from a collection of 1,574 juvenile participants from the Healthy Brain Network. We demonstrate a use case integrating this framework, and discuss why providing such neuroinformatics infrastructure to the community is critical for future scientific discoveries.
