Efficient Bayesian Tracking of Multiple Sources of Neural Activity: Algorithms and Real-Time FPGA Implementation

Abstract

Electrical neural activity detection and tracking have many applications in medical research and brain computer interface technologies. In this thesis, we focus on the development of advanced signal processing algorithms to track neural activity and on the mapping of these algorithms onto hardware to enable real-time tracking. At the heart of these algorithms is particle filtering (PF), a sequential Monte Carlo technique used to estimate the unknown parameters of dynamic systems.First, we analyze the bottlenecks in existing PF algorithms, and we propose a new parallel PF (PPF) algorithm based on the independent Metropolis-Hastings (IMH) algorithm. We show that the proposed PPF-IMH algorithm improves the root mean-squared error (RMSE) estimation performance, and we demonstrate that a parallel implementation of the algorithm results in significant reduction in inter-processor communication. We apply our implementation on a Xilinx Virtex-5 field programmable gate array (FPGA) platform to demonstrate that, for a one-dimensional problem, the PPF-IMH architecture with four processing elements and 1,000 particles can process input samples at 170 kHz by using less than 5% FPGA resources. We also apply the proposed PPF-IMH to waveform-agile sensing to achieve real-time tracking of dynamic targets with high RMSE tracking performance.We next integrate the PPF-IMH algorithm to track the dynamic parameters in neural sensing when the number of neural dipole sources is known. We analyze the computational complexity of a PF based method and propose the use of multiple particle filtering (MPF) to reduce the complexity. We demonstrate the improved performance of MPF using numerical simulations with both synthetic and real data. We also propose an FPGA implementation of the MPF algorithm and show that the implementation supports real-time tracking.For the more realistic scenario of automatically estimating an unknown number of time-varying neural dipole sources, we propose a new approach based on the probability hypothesis density filtering (PHDF) algorithm. The PHDF is implemented using particle filtering (PF-PHDF), and it is applied in a closed-loop to first estimate the number of dipole sources and then their corresponding amplitude, location and orientation parameters. We demonstrate the improved tracking performance of the proposed PF-PHDF algorithm and map it onto a Xilinx Virtex-5 FPGA platform to show its real-time implementation potential. i Finally, we propose the use of sensor scheduling and compressive sensing techniques to reduce the number of active sensors, and thus overall power consumption, of electroencephalography (EEG) systems. We propose an efficient sensor scheduling algorithm which adaptively configures EEG sensors at each measurement time interval to reduce the number of sensors needed for accurate tracking. We combine the sensor scheduling method with PF-PHDF and implement the system on an FPGA platform to achieve real-time tracking. We also investigate the sparsity of EEG signals and integrate com...

Evaluation of an OpenCL-Based FPGA Platform for Particle Filter

“…Each work-item loads the observation vector from the global memory and evaluates the weight of particles from Eq. (8). The result of each work-item is passed to the resampling kernel.…”

“…However, traditional FPGA development is inefficient because FPGA designers have to create cycle-by-cycle descriptions of processors using a Hardware Description Language (HDL). For high-speed and low-power, FPGA implementations of particle-filter-based algorithms have been proposed and are used for various applications such as air traffic management [6], multi-target tracking [7] and tracking current dipole sources of neural activity [8]. In these FPGA implementations, operations for different particles are processed in a pipeline manner.…”

A Bayesian particle filtering method for brain source localisation

“…In this type of modelling, the state of each dipole source is treated as a random unknown target. A number of works have been published under this type of modelling; these include multiple signal classification (MUSIC) related approaches [17], Markov chain Monte Carlo related approaches [18,7], and sequential Monte Carlo (or particle filtering) related approaches [9,12,13,15,14].…”

“…The spherical head model is a relatively old model that assumes the human head is a perfect spherical shape. This model was used in previous work [14,13]. The discrete head model is the 1-layer real head model generated using the BEM method, this model was used in [15,19,20].…”

Gaussian particle filtering in high-dimensional systems

“…In previous work, this information was represented by estimates of the tracked substates. The MPF has been applied to various settings with good results [15,16,17,18]. Recently, we proposed that the individual particle filters exchange particles, where the complexity of the proposed method grows linearly with the number of filters [19].…”