TY - GEN
T1 - Online and automated reliable system design to remove blink and muscle artefact in EEG
AU - Bhardwaj, Swati
AU - Jadhav, Pranit
AU - Adapa, Bhagyaraja
AU - Acharyya, Amit
AU - Naik, Ganesh R.
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/11/4
Y1 - 2015/11/4
N2 - 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.
AB - 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.
KW - Blink and Muscular Artefacts
KW - EEG
KW - FastICA
KW - Pervasive Healthcare
UR - http://www.scopus.com/inward/record.url?scp=84953337009&partnerID=8YFLogxK
U2 - 10.1109/EMBC.2015.7319951
DO - 10.1109/EMBC.2015.7319951
M3 - Conference contribution
C2 - 26737851
AN - SCOPUS:84953337009
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 6784
EP - 6787
BT - 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2015
Y2 - 25 August 2015 through 29 August 2015
ER -