Open AccessA Robust System for Detection of Artifacts from EEG Brain Recordings
Author(s) -
Janga Vijay Kumar,
P. Siva Kota Reddy
Publication year - 2019
Publication title -
international journal of innovative technology and exploring engineering
Language(s) - English
Resource type - Journals
ISSN - 2278-3075
DOI - 10.35940/ijitee.b6909.129219
Subject(s) - computer science , artifact (error) , artificial intelligence , pattern recognition (psychology) , electroencephalography , subspace topology , noise (video) , principal component analysis , robust principal component analysis , dimensionality reduction , signal (programming language) , identification (biology) , noise reduction , epilepsy , interference (communication) , computer vision , speech recognition , image (mathematics) , neuroscience , psychology , computer network , channel (broadcasting) , botany , biology , programming language
Epilepsy is a chronic disorder and has the propensity of two or more brain. Analysis of EEG is the primary method for the diagnosis of epilepsy. Contamination of eye movement and blink artifacts presence in EEG data becomes more complicated to the doctors during the diagnosis period. Earlier detection of these artifacts gives a significant advantage of refining the Epilepsy identification process. In this regard, a robust subspace detection method is applied to detect the target signal in noise with possible interference-artifacts, then a dimensionality reduction model, with the combination of fast Independent and Robust Principal Component Analysis (FICA and rPCA) is implemented for identification of artifacts from EEG brain recordings. To perform this the proposed detection method uses synthetic data and artifact contaminated data. The extracted target subspace signal is considered as the input for rPCA and FICA. The ROC analysis is developed as a standard methodology to quantify the detectors' ability to correctly distinguish the target of interest (artifacts) from the background noise in the system.