Online and automated reliable system design to remove blink and muscle artefact in EEG

Bhardwaj, S and Jadhav, P and Adapa, B and Acharyya, Amit and Naik, G R (2015) Online and automated reliable system design to remove blink and muscle artefact in EEG. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 25-29 Aug, 2015, Milan.

Full text not available from this repository. (Request a copy)


Electroencephalograms (EEGs) are progressively emerging as a significant measure of brain activity and are very effective tool for the diagnosis and treatment of mental and brain diseases and disorders including sleep apnea, Alzheimer's disease and Neurodevelopmental disorders. However, EEG signal is mixed with other biological signals including Ocular and Muscular artefacts making it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded by the medical practitioners resulting less accurate diagnosis. In this paper we propose a real-time low-complexity and reliable system design methodology to remove these artefacts and noise in an automated fashion to aid online diagnosis under the pervasive personalized healthcare set-up without the need of any reference electrode. The simulation and hardware performance of the proposed methodology are measured and compared in terms of correlation and regression statistics lying above 80% and 67% which are much improved over the state-of-the art methodologies.

[error in script]
IITH Creators:
IITH CreatorsORCiD
Acharyya, Amit
Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Electrodes, Electroencephalography, Feature extraction, Hardware, Medical services, Muscles, Wavelet transforms
Subjects: Others > Electricity
Divisions: Department of Electrical Engineering
Depositing User: Team Library
Date Deposited: 21 Jan 2016 06:32
Last Modified: 29 Aug 2017 11:04
Publisher URL:
Related URLs:

Actions (login required)

View Item View Item
Statistics for RAIITH ePrint 2140 Statistics for this ePrint Item