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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/115627


    Title: Cosine similarity as a sample size-free measure to quantify phase clustering within a single neurophysiological signal
    Authors: 周珮婷
    徐慎謀
    Chou, Elizabeth P.
    Hsu, Shen-Mou
    Contributors: 統計系
    Keywords: Cosine similarity;ITC;Oscillations;Phase;Phase clustering
    Date: 2018-02
    Issue Date: 2018-01-24 17:31:29 (UTC+8)
    Abstract: Background : Phase clustering within a single neurophysiological signal plays a significant role in a wide array of cognitive functions. Inter-trial phase coherence (ITC) is commonly used to assess to what extent phases are clustered in a similar direction over samples. However, this measure is especially dependent on sample size. Although ITC was transformed into ITCz, namely, Rayleigh’s Z, to “correct” for the sample-size effect in previous research, the validity of this strategy has not been formally tested. New method This study introduced cosine similarity (CS) as an alternative solution, as this measure is an unbiased and consistent estimator for finite sample size and is considered less sensitive to the sample-size effect. Results : In a series of studies using either artificial or real datasets, CS was robust against sample size variation even with small sample sizes. Moreover, several different aspects of examinations confirmed that CS could successfully detect phase-clustering differences between datasets with different sample sizes. Comparison with existing methods Existing measures suffer from sample-size effects. ITCz produced a mixed pattern of bias in assessing phase clustering according to sample size, whereas ITC overestimated the degree of phase clustering with small sample sizes. Conclusions : The current study not only reveals the incompetence of the previous “correction” measure, ITCz, but also provides converging evidence showing that CS may serve as an optimal measure to quantify phase clustering.
    Relation: Journal of Neuroscience Methods, Volume 295, Pages 111-120
    Data Type: article
    DOI link: https://doi.org/10.1016/j.jneumeth.2017.12.007
    DOI: 10.1016/j.jneumeth.2017.12.007
    Appears in Collections:[Department of Statistics] Periodical Articles

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