DC: Small: EEGMine: A Distributed Framework for Learning on EEG Data obtained from Epilepsy Patients

计算学习系统中心（CCLS）正在与哥伦比亚大学医学院（CNOC）神经病学系的计算神经生理学实验室（CNL）合作，开发一个分布式框架，用于从癫痫患者获得的颅内EEG数据的数据管理和机器学习。Schevon和Emerson博士已经开始了一项密集的二维微电极阵列的试验，该阵列可以以每个通道高达30 kHz的采样率进行长时间记录。到目前为止，已经收集了大约30 TB的数据。大量复杂的脑电图数据迫使我们重新思考如何处理这种“数据雪崩”。用于存储和分析的数据中心的设计特别具有挑战性，因为在单个服务器上存储数据的传统方法不允许在合理的时间内计算机器学习算法。此外，由于收集数据的条件，多种类型和来源的噪音无处不在;必须对数据进行广泛清理，并仔细标记潜在的缉获前兆。该项目正在研究为EEGMine数据中心开发集群体系结构（使用Apache Hadoop）的机制，该体系结构包含可靠的存储和备份;开发机器学习算法库（EEGMine- ML库）并解决其可扩展性问题，可能会利用MapReduce编程范式。这项研究将对癫痫和计算机科学研究产生直接影响。由于人类来源的微电极EEG数据的独特性和价值，使数据共享和远距离协作成为癫痫发作预测社区的一项有益工作。筛选TB级复杂EEG数据的最实用方法是将集群上的分布式存储与本地处理相结合，以准备数据并生成可用作机器学习算法输入的元数据，从而能够识别生理学上重要的模式。从教育的角度来看，该项目将有利于EWarn研究小组，这是CCLS和CNOC的一部分，通过培训他们在信号处理，机器学习和EEG的基础知识。网址：http://www1.ccls.columbia.edu/~dutta/EEGMine

The Center for Computational Learning Systems (CCLS) is collaborating with the Computational Neurophysiology Laboratory (CNL) in the Department of Neurology, Columbia University Medical School (CUMC) to develop a distributed framework for data management and machine learning on intracranial EEG data obtained from patients suffering from epilepsy. Drs. Schevon and Emerson have initiated a trial of a dense, two-dimensional microelectrode array which can record over long periods of time at a sampling rate of up to 30 kHz per channel. To date approximately 30 TB of data has been collected. The large volume of complex EEG data compels us to rethink how we will deal with this "data avalanche." The design of a data center for storage and analysis is particularly challenging since traditional methods of storing data on a single server do not allow machine learning algorithms to be computed within a reasonable time. Further, due to the conditions under which the data is collected, noise of multiple types and sources is pervasive; the data must be extensively cleaned and potential seizure precursors carefully labeled. The project is investigating mechanisms to develop a cluster architecture (using Apache Hadoop) for the EEGMine Data Center that incorporates reliable storage and backup; developing a library of machine learning algorithms (EEGMine- ML library) and addressing their scalability issues, potentially leveraging the MapReduce programming paradigm. This research will have immediate impact for both epilepsy and computer science research. Because of the uniqueness and value of human-derived microelectrode EEG data, it would be beneficial for the seizure prediction community to enable data sharing and long-distance collaborations. The most practical means of sifting through terabytes of complex EEG data is to combine distributed storage on a cluster with local processing to prepare data and generate meta-data that can be used as inputs for machine learning algorithms thus enabling identification of physiologically significant patterns. From an education perspective, the project will benefit the EWarn Research Group which is part of CCLS and CUMC by training them in signal processing, machine learning and basics of EEG. Website Address: http://www1.ccls.columbia.edu/~dutta/EEGMine