Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement
by
Kyungsoo Kim 1, Sung-Ho Lim 1, Jaeseok Lee 1, Won-Seok Kang 2, Cheil Moon 3 and Ji-Woong Choi 1,*
Kyungsoo Kim 1, Sung-Ho Lim 1, Jaeseok Lee 1, Won-Seok Kang 2, Cheil Moon 3 and Ji-Woong Choi 1,*
1
Department of Information & Communication Engineering, Daegu Gyeongbuk Institute of Science and Technology, Daegu 771-813, Korea
2
Wellness Convergence Research Center, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 771-813, Korea
3
Department of Brain & Cognitive Sciences, Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu 771-813, Korea
*
Author to whom correspondence should be addressed.
Academic Editor: Patricia A. Broderick
Sensors 2016, 16(6), 891; https://doi.org/10.3390/s16060891
Received: 1 February 2016 / Revised: 3 June 2016 / Accepted: 9 June 2016 / Published: 16 June 2016
(This article belongs to the Section Biosensors)
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°.
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Keywords:
EEG; ERP; maximum likelihood (ML); time delay estimation (TDE); synchronization
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MDPI and ACS Style
Kim, K.; Lim, S.-H.; Lee, J.; Kang, W.-S.; Moon, C.; Choi, J.-W. Joint Maximum Likelihood Time Delay Estimation of Unknown Event-Related Potential Signals for EEG Sensor Signal Quality Enhancement. Sensors 2016, 16, 891.
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