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dc.contributor.authorZhang, Guanghui
dc.date.accessioned2021-07-01T13:01:21Z
dc.date.available2021-07-01T13:01:21Z
dc.date.issued2021
dc.identifier.isbn978-951-39-8746-6
dc.identifier.urihttps://jyx.jyu.fi/handle/123456789/76955
dc.description.abstractCognitive processes are studied, among others, by analyzing event-related potentials/oscillations (ERPs/EROs) with various signal processing techniques. The commonly used processing techniques have, however, various limitations. For example, temporal principal component analysis (t-PCA) assumes, contrary to the actual situation, that waveforms of the PCA-extracted component for all subjects are the same. Also, several PCA-extracted components cannot be analyzed simultaneously since their amplitudes and polarities are diversiform. Moreover, conventional time-frequency analysis (TFA) can not effciently distinguish between evoked EROs mixed in temporal and spectral domains. Additionally, induced EROs are usually investigated using TFA, which ignores the interactions of induced EROs in temporal, spectral, and spatial domains. This thesis develops some EEG analysis algorithms and provides novel frameworks to investigate the cognitive mechanisms of ERPs/EROs. Specifcally, in the frst study, to address the problems in t-PCA, we introduce back-projection theory into t-PCA for solving the problem that several extracted components fail to be analyzed simultaneously. ERPs are extracted from single-trial EEG of an individual subject to address the unreasonable hypothesis in the group PCA analysis. In the second study, we explore evoked EROs to study some cognitive process stages that have not been explained accurately before. This is achieved by frst extracting the ERPs of interest in the time domain using t-PCA and then transforming the reconstructed waveforms of ERPs into time-frequency representations (TFRs). In the third study, we exploit canonical polyadic tensor decomposition to analyze the multi-domain features of induced EROs from the fourth-order tensor formed by TFRs of single-trial EEG data. This enables us to reveal potential interactions of different modes in induced EROs. In conclusion, the thesis introduces some new signal processing techniques and novel frameworks to study the dynamics of ERPs/EROs effciently, which are validated using actual and synthetic EEG/ERP data. Keywords: Event-related potentials/oscillations, principal component analysis, time-frequency analysis, tensor decomposition.en
dc.format.mimetypeapplication/pdf
dc.language.isoeng
dc.publisherJyväskylän yliopisto
dc.relation.ispartofseriesJYU dissertations
dc.relation.haspart<b>Artikkeli I:</b> Zhang, Guanghui; Li, Xueyan; Lu, Yingzhi; Tiihonen, Timo; Chang, Zheng and Cong, Fengyu (2021). Single-trial-based Temporal Principal Component Analysis on Extracting Event-related Potentials of Interest for an Individual Subject. <i>To be submitted.</i>
dc.relation.haspart<b>Artikkeli II:</b> Zhang, G., Tian, L., Chen, H., Li, P., Ristaniemi, T., Wang, H., Li, H., Chen, H., & Cong, F. (2017). Effect of parametric variation of center frequency and bandwidth of morlet wavelet transform on time-frequency analysis of event-related potentials. In <i>Y. Jia, J. Du, & W. Zhang (Eds.), CISC 2017 : Proceedings of 2017 Chinese Intelligent Systems Conference (pp. 693-702). Springer Nature Singapore Pte Ltd.. Lecture Notes in Electrical Engineering, 459. </i> DOI: <a href="https://doi.org/10.1007/978-981-10-6496-8_63"target="_blank">10.1007/978-981-10-6496-8_63</a>
dc.relation.haspart<b>Artikkeli III:</b> Zhang, G., Li, X., & Cong, F. (2020). Objective Extraction of Evoked Event-Related Oscillation from Time-Frequency Representation of Event-Related Potentials. <i>Neural Plasticity, 2020, Article 8841354.</i> DOI: <a href="https://doi.org/10.1155/2020/8841354"target="_blank">10.1155/2020/8841354</a>
dc.relation.haspart<b>Artikkeli IV:</b> Zhang, G., Zhang, C., Cao, S., Xia, X., Tan, X., Si, L., Wang, C., Wang, X., Zhou, C., Ristaniemi, T., & Cong, F. (2020). Multi-domain Features of the Non-phase-locked Component of Interest Extracted from ERP Data by Tensor Decomposition. <i>Brain Topography, 33(1), 37-47.</i> DOI: <a href="https://doi.org/10.1007/s10548-019-00750-8"target="_blank">10.1007/s10548-019-00750-8</a>
dc.relation.haspart<b>Artikkeli V:</b> Zhang, Guanghui; Li, Xueyan; Wang, Xiulin; Liu, Wenya; Zhu, Yongjie; Wang, Xiaoshuang; Mahini, Reza; Fu, Rao; Chang, Zheng; Tiihonen, Timo and Cong, Fengyu (2021). Signal Processing Techniques for Event-related Potentials: from Single-way to Multi-way Component Analysis. <i>To be submitted.</i>
dc.rightsIn Copyright
dc.titleMethods to extract multi-dimensional features of event-related brain activities from EEG data
dc.typeDiss.
dc.identifier.urnURN:ISBN:978-951-39-8746-6
dc.relation.issn2489-9003
dc.rights.copyright© The Author & University of Jyväskylä
dc.rights.accesslevelopenAccess
dc.type.publicationdoctoralThesis
dc.format.contentfulltext
dc.rights.urlhttp://rightsstatements.org/page/InC/1.0/?language=en
dc.date.digitised


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