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http://dx.doi.org/10.5573/ieie.2015.52.3.136

Performance Measurement of Single-board System for Mobile BCI System  

Lee, Hyo Jong (Div. of Computer Science and Engineering, CAIIT, Chonbuk National University)
Kim, Hyun Kyu (Div. of Computer Science and Engineering, Chonbuk National University)
Gao, Yongbin (Div. of Computer Science and Engineering, Chonbuk National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.52, no.3, 2015 , pp. 136-144 More about this Journal
Abstract
The EEG system can be classified as a wired or wireless device. Each device used for the medical or entertainment purposes. The collected EEG signals from sensor are analyzed using feature extractions. A wireless EEG system provides good portability and convenience, however, it requires a mobile system that has heavy computing power. In this paper a single board system is proposed to handle EEG signal processing for BCI applications. Unfortunately, the computing power of a single board system is limited unlike general desktop systems. Thus, parallel approach using multiple single board systems is investigated. The parallel EEG signal processing system that we built demonstrates superlinear speedup for an EEG signal processing algorithm.
Keywords
Mobile BCI System; FIR Filter; MPI Single-board systems;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 F. Tso, D. R. White, S. Jouet, J. Singer, and D. Pezaros, "The Glasgow Raspberry Pi Cloud: A Scale Model for Cloud Computing Infrastructures," in International Workshop on Resource Management of Cloud Computing (to appear, 2013), 2013.
2 G. S. Sundaram, B. Patibandala, H. Santhanam, S. Gaddam, V. K. Alla, G. R. Prakash, et al., "Bluetooth communication using a touchscreen interface with the Raspberry Pi," in Southeastcon, 2013 Proceedings of IEEE, 2013, pp. 1-4.
3 Gusev, Marjan, and Sasko Ristov. "A superlinear speedup region for matrix multiplication." Concurrency and Computation: Practice and Experience, 2013.
4 L. Djinevski, S. Ristov, and M Gusev. "Superlinear Speedup for Matrix Multiplication in GPU Devices." ICT Innovations 2012. Springer Berlin Heidelberg, 2013. 285-294.
5 EEGLAB. http://sccn.ucsd.edu/eeglab/
6 N. Jmail, M. Gavaret, F. Wendling, A. Kachouri, G. Hamadi, J.-M. Badier, et al., "A comparison of methods for separation of transient and oscillatory signals in EEG," Journal of neuroscience methods, vol. 199, pp. 273-289, 2011.   DOI   ScienceOn
7 A. Campbell, T. Choudhury, S. Hu, H. Lu, M. K. Mukerjee, M. Rabbi, et al., "NeuroPhone: brain-mobile phone interface using a wireless EEG headset," in Proceedings of the second ACM SIGCOMM workshop on Networking, systems, and applications on mobile handhelds, 2010, pp. 3-8.
8 L.-D. Liao, C.-Y. Chen, I.-J. Wang, S.-F. Chen, S.-Y. Li, B.-W. Chen, et al., "Gaming control using a wearable and wireless EEG-based brain-computer interface device with novel dry foam-based sensors," Journal of neuroengineering and rehabilitation, vol. 9, p. 5, 2012.   DOI
9 H.K. Kim and H.J. Lee, "Performance of the Finite Difference Method Using Cache and Shared Memory for Massively Parallel Systems," Journal of the Institute of electronics Engineers of Korea, vol. 50, pp.108-116, 2013.
10 J. Chambers and D. Sanei, EEG Signal Processing, 1th ed.: Wiley-Interscience, 2013.
11 J. G. Proakis and D. K. Manolakis, Digital Signal Processing Principles, Algorithms, and Applications, 4th ed.
12 Raspberry Pi. http://www.raspberrypi.org