On the extension of ordinal time series analysis for multisite recordings: a new method and its use in discriminating EEG activity during different mental tasks

 
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On the extension of ordinal time series analysis for multisite recordings: a new method and its use in discriminating EEG activity during different mental tasks

Panagopoulou, AI
Dimitriadis, Stavros
Laskaris, Nikolaos
Kostavelis, I.D.

OBJECTIVE: Analyzing ordinal patterns has recently gained high popularity among practitioners of nonlinear dynamics and currently appears as a powerful tool of Symbolic Time Series Analysis capable of revealing changes in the dynamics of EEG data [1]. In its original format, the particular approach is tailored to unidimensional timeseries, hence its straightforward application to brain signals is restricted to single-sensor measurements. The treatment of multisite recordings by analyzing the dynamics in a channelwise fashion is obviously a problematic approach, as it is equivalent to ignoring any coordinated activity patterns emerging from the coherent activation of distinct brain areas. We introduce here a vectorial version of ordinal time series analysis that can fully encompass the multidimensional character of brain’s activations and covariations. By applying it to EEG-data from subjects while performing mental calculations, we show that our approach can differentiate between, otherwise, indistinguishable brain states. METHODS: A sliding window is moving along the N-dimensional time series (each recording site treated as an individual variate). At every step, the window encloses M successive vectors on which we apply a vector-ranking operator and the resulting ranks provides the ordinal pattern associated with the particular signal-segment. This is, always, just one of the permutations of {1,2,…,M}. By simply counting the number of different ordinal patterns, which appear while traversing the whole time-series, we end up with a histogram reflecting the variations in global brain dynamics. Having established this representation, the contrast between distinct brain recordings (and hence different brain states) takes the form of a histogram comparison that can be implemented readily and in various numerical ways. RESULTS: Based on a well-established statistical index, we measured the class-separability between EEG-activations during math calculations and EEG-activations at rest. Using surrogate data analysis, we prove the statistical significance of these measurements. In addition we were able to distinguish between different mathematical tasks. Moreover, the chanellwise approach was found of limited power. CONCLUSIONS: The vectorised version of ordinal time series analysis offers a promising new way to study and characterize brain dynamics.

Conference / Συνέδριο
info:eu-repo/semantics/conferenceObject

Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης (EL)
Aristotle University of Thessaloniki (EN)

Αγγλική γλώσσα

2011-03-19T11:16:43Z


Αριστοτέλειο Πανεπιστήμιο Θεσσαλονίκης, Σχολή Θετικών Επιστημών, Τμήμα Πληροφορικής

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info:eu-repo/semantics/openAccess



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