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Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement

Title
Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement
Authors
Kim, K[Kim, Kyungsoo]Lim, SH[Lim, Sung-Ho]Lee, J[Lee, Jaeseok]Kang, WS[Kang, Won-Seok]Moon, C[Moon, Cheil]Choi, JW[Choi, Ji-Woong]
DGIST Authors
Kim, K[Kim, Kyungsoo]; Lim, SH[Lim, Sung-Ho]; Lee, J[Lee, Jaeseok]; Kang, WS[Kang, Won-Seok]Moon, C[Moon, Cheil]Choi, JW[Choi, Ji-Woong]
Issue Date
2016-06
Citation
Sensors, 16(6)
Type
Article
Article Type
Article
Keywords
Bioelectric PhenomenaBiomedical Signal ProcessingBrain Computer InterfaceConventional SchemesEEGElectroencephalographyEnterprise Resource PlanningERPEvent-Related Potential SignalsEvent-Related PotentialsHuman Brain FunctionsInterfaces (Computer)Joint Maximum LikelihoodMaximum Likelihood (ML)Maximum Likelihood EstimationSignal-to-Noise Power RatioSignal-to-Noise RatioSignal ProcessingSynchronizationTime DelayTime Delay Estimation (TDE)Variable Delays
ISSN
1424-8220
Abstract
Electroencephalograms (EEGs) measure a brain signal that contains abundant information about the human brain function and health. For this reason, recent clinical brain research and brain computer interface (BCI) studies use EEG signals in many applications. Due to the significant noise in EEG traces, signal processing to enhance the signal to noise power ratio (SNR) is necessary for EEG analysis, especially for non-invasive EEG. A typical method to improve the SNR is averaging many trials of event related potential (ERP) signal that represents a brain’s response to a particular stimulus or a task. The averaging, however, is very sensitive to variable delays. In this study, we propose two time delay estimation (TDE) schemes based on a joint maximum likelihood (ML) criterion to compensate the uncertain delays which may be different in each trial. We evaluate the performance for different types of signals such as random, deterministic, and real EEG signals. The results show that the proposed schemes provide better performance than other conventional schemes employing averaged signal as a reference, e.g., up to 4 dB gain at the expected delay error of 10°. © 2016 by the authors; licensee MDPI, Basel, Switzerland.
URI
http://hdl.handle.net/20.500.11750/2267
DOI
10.3390/s16060891
Publisher
MDPI AG
Related Researcher
Files:
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Collection:
Information and Communication EngineeringETC1. Journal Articles
Brain and Cognitive SciencesMoon Lab1. Journal Articles
Brain and Cognitive SciencesETC1. Journal Articles
Information and Communication EngineeringETC1. Journal Articles
Information and Communication EngineeringETC1. Journal Articles


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