FPGA Implementation of RLSE Algorithm for Multichannel Brain Imaging

Muhammad Shahid Nazir; Haroon-Ur-Rasheed Khan; Abubaker Akram; Bhagesh Maheshwari; Muhammad Aqil

doi:10.22581/muet1982.2101.21

Author(s)

Muhammad Shahid Nazir Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS),Nilore, Islamabad, Pakistan.

Haroon-Ur-Rasheed Khan Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS),Nilore,Islamabad, Pakistan.

Abubaker Akram Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS),Nilore, Islamabad, Pakistan.

Bhagesh Maheshwari Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS),Nilore,Islamabad, Pakistan.

Muhammad Aqil Department of Electrical Engineering, Pakistan Institute of Engineering and Applied Sciences (PIEAS),Nilore,Islamabad, Pakistan.

Abstract

This paper describes the implementation of a computationally efficient embedded system on an Field Programmable Gate Array (FPGA) platform for real-time brain activity estimation with multiple channels. The brain signals from multiple channels are considered as output of independent linear systems with unknown parameters representing the brain activity in corresponding channels. Multiple adaptive Recursive LeastSquares Estimation (RLSE) cores are implemented in FPGA to independently estimate the brain activity in each channel concurrently. The proposed RLSE-FPGA system provides dedicated (no time or resource sharing) and parallel processing environment. The universal asynchronous receiver transmitter core is also developed to communicate the measured and estimated parameters supported by storage facility programmed as shared memory. The computational precision is guaranteed by deploying a 32-bit floating point core for all the variables. The validation carried out by real Functional Near-Infrared Spectroscopy dataset and comparative analysis with the previously reported result, demonstrates the effectiveness of the proposed system. The computational cost endorses the effectiveness of concurrent processing of multiple channels? data in a sample before the arrival of the next sample. The proposed methodology has potential in real-time medical, military and industrial applications.

Publication Details

Page(s) 241-250

DOI 10.22581/muet1982.2101.21

Published Journal: Mehran University Research Journal of Engineering and Technology, Volume: 40, Issue: 1, Year: 2021

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Keywords

Brain Imaging, FPGA, Recursive Least Squares Estimation