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ERP Detection Based on Smoothness Priors

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posted on 06.12.2021, 14:02 by Ali MobaienAli Mobaien, Reza Boostani, Mokhtar Mohammadi, Saeid Sanei
Abstract—Objective: Detection of event-related potentials (ERPs) in electroencephalography (EEG) is of great interest in the study of brain responses to various stimuli. This is challenging due to the low signal-to-noise ratio of these deflections. To address this problem, a new scheme to detect the ERPs based on smoothness priors is proposed. Methods: The problem is considered as a binary hypothesis test and solved using a smooth version of the generalized likelihood ratio test (SGLRT). First, we estimate the parameters of probability density functions from the training data under Gaussian assumption. Then, these parameters are treated as known values and the unknown ERPs are estimated under the smoothness constraint. The performance of the proposed SGLRT is assessed for ERP detection in poststimuli EEG recordings of two oddball settings. We compared our method with several powerful methods regarding ERP detection. Results: The presented method outperforms the competing algorithms and improves the classification accuracy. Conclusion: The proposed SGLRT could be employed as a powerful means for different ERP detection schemes. Significance: ERP-based systems (e.g. brain-machine interfaces) mainly suffer from lack of classification accuracy, hence the proposed method is an important step toward real-life applicability of these systems.


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Shiraz University

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