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 languageEnglish (US)
Pages (from-to)145-162
Number of pages18
JournalJournal of Signal Processing Systems
Volume70
Issue number2
DOIs
StatePublished - Feb 2013

Fingerprint

Magnetoencephalography
Electroencephalography
Hardware Implementation
Scheduling algorithms
Scheduling Algorithm
Hardware
Sensor
Sensors
Particle Filter
Inverse problems
Dipole
Field Programmable Gate Array
Power Consumption
Field programmable gate arrays (FPGA)
Time-varying
Inverse Problem
Electric power utilization
Filter
Sampling
Real-time

Keywords

  • Dipole source modeling
  • FPGA implementation
  • Neural activity
  • Parallel architecture
  • Particle filter
  • Power consumption
  • Probability hypothesis density filter
  • Sensor scheduling

ASJC Scopus subject areas

  • Hardware and Architecture
  • Information Systems
  • Signal Processing
  • Theoretical Computer Science
  • Control and Systems Engineering
  • Modeling and Simulation

Cite this

Multi-source neural activity estimation and sensor scheduling : Algorithms and hardware implementation. / Miao, Lifeng; Michael, Stefanos; Kovvali, Narayan; Chakrabarti, Chaitali; Papandreou-Suppappola, Antonia.

In: Journal of Signal Processing Systems, Vol. 70, No. 2, 02.2013, p. 145-162.

Research output: Contribution to journalArticle

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