Abstracts Track 2022

Area 1 - Complexity in Biology and Biomedical Engineering

Nr: 4

EEG Signal Segmentation for Human Response Detection


Marta Lotka

Abstract: Nonstationary time series can be conceptualized as concatenations of quasi-stationary spells. It is of interest to divide experimentally obtained time-series into quasi-stationary segments, as the partitions could indicate a change of state in the underlying complex system. In particular, such a segmentation method would provide a useful tool for EEG data analysis. Traditionally EEG data were studied using the event-related potential (ERP) technique. It is based on selecting fragments of the EEG data surrounding an event (like the subject making a response) for each trial and then averaging these fragments separately for each electrode. This approach involves discarding possibly relevant information in the averaging process, its results depend on the electrode location and some data preprocessing details, and it cannot be adapted to detect a change in the state of the system (such as detection of an error in the case of EEG recording of a subject performing a task) in a single trial. A segmentation-based method taking advantage of the fluctuation properties could provide an improvement in these regards. In this work, a segmentation algorithm based on the distribution-free Kolmogorov-Smirnov (KS) test proposed by Camargo, Duarte Queirós & Anteneodo (2011) is investigated. The algorithm involves recursively placing partitions such that the KS statistic is maximized (if its value indicates statistical significance and the resulting segments are sufficiently long). The algorithm was tested for both artificial time series and EEG data from 6 subjects performing the Eriksen flanker task – a simple response inhibition task in which each trial involves the subject deciding on pressing one of two buttons based on the stimuli displayed on a screen. Firstly, the method was applied to several types of artificially generated time series that have been used as a model for the EEG (Gaussian and autoregressive series, and 1/f noise). Segmentation quality was assessed with Normalized Mutual Information and with the number of correctly identified partitions. Although the KS segmentation algorithm accurately segments Gaussian series in a wide range of parameters, oversegmentation was found to occur for autoregressive and 1/f series. Preliminary results for the EEG data indicate that the segmentation method detects stimulus ERPs as well as the components related to detection and monitoring of errors, such as the well-documented error-related negativity. Stimulus-locked and response-locked ERP components were obtained from preprocessed data in the time range from -200 ms to 1000 ms for each subject and condition (erroneous/correct response) separately. Segmentation was performed for each trial at minimal segment length of 100 ms. Then probability distribution of a partition at a particular time point. The experimental distributions were compared with probability distributions for surrogate ARMA series fitted to the corresponding data to verify that the results are not artifactual Comparing the probability distribution with the ERP waveform suggests that partitions are most likely where the slope of the ERP waveform is the steepest. Camargo, S., Duarte Queirós, S. M., & Anteneodo, C. (2011). Nonparametric segmentation of nonstationary time series. Physical review. E, Statistical, nonlinear, and soft matter physics, 84(4 Pt 2), 046702. https://doi.org/10.1103/PhysRevE.84.046702.

Area 2 - Complexity in Informatics and Networking

Nr: 3

Change Detection of Complex Networks based on Extended Mixed Membership Stochastic Block Model


Junjie Wang and Chun Fai Lui

Abstract: Change detection of complex networks has attracted much attention in practice and research. Nowadays we can see more and more change detection methods developed based on statistical process control (SPC), which is a prevalent tool in quality control. However, existing SPC techniques have seldom incorporated the community structure among nodes in networks and have shown limited efficiency of change detection. In addition, most of current mixed membership stochastic block models (MSBM) fail to consider nodes’ heterogeneity despite their account of nodes’ multiple community labels. To bridge the research gap, this article extends traditional MSBM by incorporating nodes’ heterogeneity and community information simultaneously. The proposed model can be expressed in a matrix form to enable easy parameter estimation with maximum likelihood estimation (MLE). It also benefits the derivation of monitoring statistic based on generalized likelihood ratio test (GLRT). The finalized change detection method shows higher efficiency than several competing approaches in simulation study and real application.