TP 6: Repeated Stochastic Processes in Single Trial EEG-Analysis - Statistical Theory and Methods

Principal Investigator: Jan Beran

Research Team: Haiyan Liu, Klaus Telkmann

There is a growing interest among psychologists and behavioural economists in data generated by repeated observations of a stochastic process. Projects TP2, TP6 aim at answering complex questions in the context of dual processing (automatic and controlled processes) based on data of this type. The standard approach of averaging ERPs usually requires a large number of homogeneous trials. This can be problematic when dealing, for instance, with microeconomic decisions where the number of available repetitions is often small. Moreover, trial averaging ignores variability of automatic and controlled processes across experimental trials. Single-trial analysis is therefore the appropriate approach in the context of this RU. Since the data structure in single trials is more complex and available data sets are relatively small, sophisticated and parsimonious statistical methodology is needed to extract the essential information. In this project this methodology will be developed by considering functional data analysis (FDA) and independent component analysis (ICA) in the context of suitably defined stochastic processes. The data-analytical focus will be on EEG data (ERPs) and their predictive power in modelling human decision making (TP2, TP6). The statistical methods will in corporate temporal and/or spatial dependence structures within the realm of FDA and ICA. This will provide more insight into the process of decision making and its neural correlates


  • On estimation of mean and covariance functions in repeated time series with long-memory errors. Jan Beran and Haiyan Liu, 2014, Lithuanian Mathematical Journal, Vol. 54, pp. 8–34.
  • Semiparametric decomposition of the gender achievement gap: An application for Turkey. Z. Eylem Gevrek and Ruben R. Seiberlich, 2014, Labour Economics, Vol. 31, pp. 27–44.

Working Papers

  • On two sample inference for eigenspaces in FDA with dependent errors. Jan Beran, Haiyan Liu and Klaus Telkmann, 2015.
  • Estimation of eigenvalues, eigenvectors and scores in FDA models with dependent errors. Jan Beran and Haiyan Liu, 2014.

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