Efficient subspace based techniques for processing single channel electroencephalogram signals

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dc.contributor.author Maddirala, Ajay Kumar
dc.date.accessioned 2017-11-28T10:25:15Z
dc.date.available 2017-11-28T10:25:15Z
dc.date.issued 2017
dc.identifier.other ROLL No. 11610217
dc.identifier.uri http://gyan.iitg.ernet.in/handle/123456789/879
dc.description Supervisor: Shaik Rafi Ahamed en_US
dc.description.abstract In recent years, the use of portable/wearable EEG systems in health care applications has been increased due its low-power consumption, easy to operate and minimum instrumentation complexity, and reduces the cumbersome to the subject under test. In general, the EEG signals often measured in ambulatory situations, hence, they were contaminated by several artifacts. The presence of these artifacts will degrade the performance of EEG based detection systems. Since the portable EEG devices comprise single or few (at most four) EEG channels, traditional artifact removal techniques, such as blind source separation (BSS), cannot be applied to remove these artifacts. Hence, in this thesis, various subspace based artifact removal techniques for single channel EEG signals were proposed.Singular spectrum analysis (SSA) is a subspace based technique used in this thesis to remove artifacts from single channel EEG signals. Using the frame work of SSA, first, we proposed new grouping criteria to identify the desired signal (EEG signal) subspace. In this criterion, the local mobility of the eigenvectors is considered and employed for removing the motion artifacts from single channel EEG signals. In addition, SSA is combined with ANC to remove the eye blink artifact in EEG epochs recorded for BCI application. en_US
dc.language.iso en en_US
dc.relation.ispartofseries TH-1631;
dc.subject ELECTRONICS AND ELECTRICAL ENGINEERING en_US
dc.title Efficient subspace based techniques for processing single channel electroencephalogram signals en_US
dc.type Thesis en_US


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