BCIBench: a benchmarking suite for EEG-based brain computer interface

Authors:
Roozbeh Jafari;Omid Dehzangi;Chengzhi Zong;Viswam Nathan
Affiliations:
University of Texas at Dallas, Richardson, TX;University of Texas at Dallas, Richardson, TX;University of Texas at Dallas, Richardson, TX;University of Texas at Dallas, Richardson, TX
Venue:
Proceedings of the 11th Workshop on Optimizations for DSP and Embedded Systems
Year:
2014

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Abstract

Increased demands for applications of brain computer interface (BCI) have led to growing attention towards their low-power embedded processing architecture design. Most clinical, wellness, and entertainment applications of BCI require wearable and portable devices. Better understanding of application characteristics in terms of computational complexity, memory usage, and power consumption can lead to more effective system designs for future wearable BCIs. In this paper, we introduce BCIBench, a benchmarking suite which includes a wide range of algorithms used for pre-processing, feature extraction and classification in BCI applications. We analyze the architectural characteristics of these algorithms such as performance, data-intensiveness and memory behavior. We provide insights into architectural components that can enhance the performance and reduce the power consumption of BCI embedded systems using these applications.