Citation
Roozbeh Jafari, Omid Dehzanghi. "BCIBench: A Benchmarking Suite for EEG-Based Brain Computer Interface". Design, Automation and Test in Europe, 24, March, 2014; Submitted to DATE 2014.

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, parallelism, 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.

Electronic downloads

• http://www.src.org/library/publication/p068848/. (SRC website.)
• http://dl.acm.org/citation.cfm?doid=2568326.2568330

• HTML

  Roozbeh Jafari, Omid Dehzanghi. <a
  href="http://www.terraswarm.org/pubs/190.html"
  >BCIBench: A Benchmarking Suite for EEG-Based Brain
  Computer Interface</a>, Design, Automation and Test in
  Europe, 24, March, 2014; Submitted to DATE 2014.

• Plain text

  Roozbeh Jafari, Omid Dehzanghi. "BCIBench: A
  Benchmarking Suite for EEG-Based Brain Computer
  Interface". Design, Automation and Test in Europe, 24,
  March, 2014; Submitted to DATE 2014.

• BibTeX

  @inproceedings{JafariDehzanghi14_BCIBenchBenchmarkingSuiteForEEGBasedBrainComputerInterface,
      author = {Roozbeh Jafari and Omid Dehzanghi},
      title = {BCIBench: A Benchmarking Suite for EEG-Based Brain
                Computer Interface},
      booktitle = {Design, Automation and Test in Europe},
      day = {24},
      month = {March},
      year = {2014},
      note = {Submitted to DATE 2014.},
      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, parallelism,
                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.},
      URL = {http://terraswarm.org/pubs/190.html}
  }
  

Posted by Roozbeh Jafari on 11 Nov 2013.
Groups: tools