Removal of EMG Artifacts from Multichannel EEG Signal Using Automatic Dynamic Segmentation and Adaptive Thresholding with Multilevel Decomposed Wavelets
Journal Title: IOSR Journals (IOSR Journal of Electrical and Electronics Engineering) - Year 2017, Vol 12, Issue 3
Abstract
Background: Like the brain, muscles also generate electrical signals. These signals are picked up by EEG electrodes and become muscle artifacts in EEG recording. MA is looking like fast oscillations. EEG signals disturbed by MA have larger amplitudes than normal EEG signals. Human beings have a large number of muscles all over their bodies. The muscle movement that happens near electrodes, such as teeth squeezing, jaw clenching, forehead movements will have a huge impact on the power spectrum of EEG signals. Usually, presence of the MA in EEG signals will increase the power of EEG signals in the frequency band from roughly 20Hz to 50Hz. Materials and methods: 16 channel EEG signals are acquired with EMG artifacts. The subject is instructed to do jaw clenching, forehead movement, teeth squeezing at different instances during the time of recording. The captured EEG signal is imported in MATLAB. Sampling rate used is 1024 Hz. Statistical parameters like PSNR, RMSE are used for comparison. EMG artifacts frequently affected the EEG signal and contaminated valuable information. The present work deals with novel automatic dynamic size independent components based on statistical information of signal and development of multilevel decomposition with adaptive threshold for removing of EMG artifacts. Automatic and dynamic segmentation is the major feature of this method. A particular segment can be analyzed and processed independent of other segments. The present adaptive threshold method is best suitable for removal of muscle artifacts. Results: Present method is better for suppression of EMG artifacts and preserves brain neural activity information as compared with static segmentation. Conclusion: Automatic dynamic segmentation method with adaptive thresholding of multilevel decomposed wavelets is showing superior performance over conventional static segmentation method. It removes EMG artifacts significantly by preserving brain neural activity.
Authors and Affiliations
K. P. Paradeshi, Research Scholar, Professor Dr. U. D. Kolekar
Keywords
Related Articles
- EP ID EP387287
- DOI 10.9790/1676-1203043035.
- Views 203
- Downloads 0
Dynamic Reconfiguration of Analog VLSI System: A Review
Dynamic reconfigurable system came in to limelight in the current generation of VLSI design. Till now area, performance, cost and power are the priorities of design engineers overlooking the Multifunctionality of a singl...
Analysis and Development of a Reliability Forecasting Model for Injection Substation
This paper is aimed at analyzing and developing a reliability forecasting model for an Injection Substation; a case study of Ugbowo 33/11kV Injection Substation. This project seeks to determine the current reliability st...
Flying with Internet of Things via Global Structure
According to growth of high definition technologies and their wide applications in each part of our life, these lead to globalize a new aspect to be an umbrella to cover and connect all things in the world. Internet of t...
Power Control in a Microgrid
A micro-grid is a unifying of micro-sources together with a load and an optional energy storage device within a vicinity. The micro-grid can be operated in either autonomous, non-autonomous or a dual operation. In an aut...
Cost Comparison in LED Lighting and Selection Criteria in LEDs
In this study, LED selection criteria, comparative technical data of LEDs and other lamps and price analyzes are evaluated. In addition, today’s development of LED technology is examined and the reasons for using LED lam...