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Multi-source Neural Activity Estimation and Sensor Scheduling: Algorithms and Hardware Implementation

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Abstract

Electroencephalography (EEG) and magnetoencephalography (MEG) measurements are used to localize neural activity by solving the electromagnetic inverse problem. In this paper, we propose a new approach based on the particle filter implementation of the probability hypothesis density filter (PF-PHDF) to automatically estimate the unknown number of time-varying neural dipole sources and their parameters using EEG/MEG measurements. We also propose an efficient sensor scheduling algorithm to adaptively configure EEG/MEG sensors at each time step to reduce total power consumption. We demonstrate the improved performance of the proposed algorithms using simulated neural activity data. We map the algorithms onto a Xilinx Virtex-5 field-programmable gate array (FPGA) platform and show that it only takes 10 ms to process 100 data samples using 6,400 particles. Thus, the proposed system can support real-time processing of an EEG/MEG neural activity system with a sampling rate of up to 10 kHz.

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Authors and Affiliations

  1. School of Electrical, Computer and Energy Engineering, Arizona State University, Tempe, AZ, USA

    Lifeng Miao, Stefanos Michael, Narayan Kovvali, Chaitali Chakrabarti & Antonia Papandreou-Suppappola

Authors
  1. Lifeng Miao
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  2. Stefanos Michael
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  3. Narayan Kovvali
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  4. Chaitali Chakrabarti
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  5. Antonia Papandreou-Suppappola
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Corresponding author

Correspondence to Lifeng Miao.

Additional information

This work was supported by the National Science Foundation under Grant No. 0830799.

The real-time estimation of an unknown number of neural sources was discussed in our 2011 IEEE Workshop on Signal Processing Systems paper [1]. In addition, this work presents the probability hypothesis density filter implemented using particle filtering with a new pre-whitening processing algorithm (Section 4); a sensor scheduling algorithm (Section 5) and its hardware implementation (Section 6.2); performance results to demonstrate sensor scheduling for simulated EEG data (Section 7.1); new hardware implementation results with sensor scheduling (Section 7.2); and scalability analysis (Section 7.3).

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Miao, L., Michael, S., Kovvali, N. et al. Multi-source Neural Activity Estimation and Sensor Scheduling: Algorithms and Hardware Implementation. J Sign Process Syst 70, 145–162 (2013). https://doi.org/10.1007/s11265-012-0701-7

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  • DOI: https://doi.org/10.1007/s11265-012-0701-7

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