High bandwidth EEG data hosting and analytics in cloud-based storage systems

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In latest years, with the advent of Big Data, especially in the area of biometrics data collected from wearablemonitoring devices, there is a high need for Cloud-based solutions (include large scale distributed storagesystems) for storage and high bandwidth on-the-fly data analysis. Examples of such biometric data are theElectroencephalography (EEG) data that become available from EEG wearable devices and offer newopportunities for contextualized research applications by providing access to dense array, high quality, rawEEG data. Thus, the emergence of high frequency EEG sampling in a scalp based device (a "Holter monitor"for the brain) will assist physicians in diagnosing, assessing and treating a myriad of neurological conditionsand help enable new discovery. In particular, our industry partner, Avertus, has developed a headset for highfrequency sampling that is both comfortable to wear in non-hospital settings and also highly accurate, with along term vision to predict problems and deliver treatments in real-time. However, a key problem for ITcompanies that collect large amounts of biometrics data on-the-fly is their need for real-time solutions foranomaly detection in the collected data. In our case of the EEG headset, high frequency sampling generateslarge amounts of raw data (e.g., an estimate of 5GB/patient/day) that requires significant processing,necessitating a distributed processing platform for both research (discovery and repository) and clinical utility -initially on a batch basis, but also including streaming analytic capability. In this work we propose to develop acloud based data analytics and storage platform for hosting and processing streaming data from Avertusheadsets. The focus is on two aspects: (a) on-the-fly biometric data analysis and anomaly detection ofstreaming data from the Avertus users, and (b) on-the-fly analysis of computer system data for anomalydetection with regards to the system behaviour. We expect that the proposed solution will minimize the dataanalysis search space of the stored data. We will evaluate our solutions in collaboration with the partnerorganization for types of human and system biometrics data on various distributed storage systems.