An adaptive noise canceller based on QLMS algorithm for removing EOG artifacts in EEG recordings
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Date
2017
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Publisher
Institute of Electrical and Electronics Engineers Inc.
Access Rights
info:eu-repo/semantics/closedAccess
Abstract
In this paper, a novel adaptive noise canceller (ANC) based on the quaternion valued least mean square algorithm (QLMS) is designed in order to remove electrooculography (EOG) artifacts from electroencephalography (EEG) recordings. The measurement real-valued EOG and EEG signals (FP1, FP2, AF3 and AF4) are first modeled as four-dimensional processes in the quaternion domain. The EOG artifacts are then removed from the EEG signals in the quaternion domain by using the ANC based on QLMS algorithm. The quaternion representation of these signals allows us to remove EOG artifacts from all channels at the same time instead of removing the EOG artifacts in each EEG recordings separately. The simulation results support the proposed approach. © 2017 IEEE.
Description
2017 International Artificial Intelligence and Data Processing Symposium, IDAP 2017 -- 16 September 2017 through 17 September 2017 -- -- 115012
Keywords
Adaptive noise canceller, EEG and EOG signals, Quaternion domain, Quaternion least mean square
Journal or Series
IDAP 2017 - International Artificial Intelligence and Data Processing Symposium
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