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.
Original language | English (US) |
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Pages (from-to) | 145-162 |
Number of pages | 18 |
Journal | Journal of Signal Processing Systems |
Volume | 70 |
Issue number | 2 |
DOIs | |
State | Published - Feb 2013 |
Keywords
- Dipole source modeling
- FPGA implementation
- Neural activity
- Parallel architecture
- Particle filter
- Power consumption
- Probability hypothesis density filter
- Sensor scheduling
ASJC Scopus subject areas
- Control and Systems Engineering
- Theoretical Computer Science
- Signal Processing
- Information Systems
- Modeling and Simulation
- Hardware and Architecture