A fast fixed-point algorithm for independent component analysis
Neural Computation
A beamforming particle filter for EEG dipole source localization
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Non-contact Low Power EEG/ECG Electrode for High Density Wearable Biopotential Sensor Networks
BSN '09 Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks
Sensor selection via convex optimization
IEEE Transactions on Signal Processing
Source localization using recursively applied and projected (RAP)MUSIC
IEEE Transactions on Signal Processing
High sample rate array architectures for median filters
IEEE Transactions on Signal Processing
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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.