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UMBC High Performance Computing Facility
Please note that this page is under construction. We are documenting the 240-node cluster maya that will be available after Summer 2014. Currently, the 84-node cluster tara still operates independently, until it becomes part of maya at the end of Summer 2014. Please see the 2013 Resources Pages under the Resources tab for tara information.
Algorithm Characterization and Implementation for Large Volume, High Resolution Multichannel Electroencephalography Data in Seizure Detection
Tinoosh Mohsenin, Computer Science and Electrical Engineering
Adam Page, Computer Science and Electrical Engineering
Amey Kulkarni, Computer Science and Electrical Engineering
Tim Oates, Computer Science and Electrical Engineering

Ubiquitous bio-sensing for personalized health monitoring is slowly becoming a reality with the increasing availability of small, diverse, robust, high fidelity sensors. This oncoming flood of data begs the question of how we will extract useful information from it. In this paper we explore the use of a variety of representations and machine learning algorithms applied to the task of seizure detection in large volue of high resolution, multi-channel EEG data. We explore classification accuracy, computational complexity and memory requirements with a view toward understanding which approaches are most suitable for such tasks as the number of people involved and the amount of data they produce grows to be quite large. In particular, we show that layered learning approaches such as Deep Belief Networks excel along these dimensions. We also present the implementation of these algorithms on different hardware approaches including Virtex-7 FPGA, GPUs and 65 nm-CMOS ASIC.