4 research outputs found
Real-time Regular Expression Matching
This paper is devoted to finite state automata, regular expression matching,
pattern recognition, and the exponential blow-up problem, which is the growing
complexity of automata exponentially depending on regular expression length.
This paper presents a theoretical and hardware solution to the exponential
blow-up problem for some complicated classes of regular languages, which caused
severe limitations in Network Intrusion Detection Systems work. The article
supports the solution with theorems on correctness and complexity.Comment: 17 pages, 11 figure
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EEG microstates: Functional significance and short-term test-retest reliability
Appendix A: Supplementary data to this article can be found online at https://doi. org/10.1016/j.ynirp.2022.100089.Copyright /© 2022 The Authors. EEG signal power, may have clinical relevance; however, their functional significance and test-retest reliability remain unclear. To investigate the functional significance of the canonical EEG microstate classes and their pairwise transitions, and to establish their within-session test-retest reliability, we recorded 36-channel EEGs in 20 healthy volunteers during three eyes-closed conditions: mind-wandering, verbalization (silently repeating the word ‘square’ every 2 s), and visualization (visualizing a square every 2 s). Each condition lasted 3 min and the sequence of three conditions was repeated four times (two runs of two sequence repetitions). The participants' alertness and their sense of effort during the experiment were rated using visual-analogue scales. The EEG data were 2–20 Hz bandpass-filtered and analysed into the four canonical microstate classes: A, B, C, and D. EEG microstate classes C and D were persistently more dominant than classes A and B in all conditions. Of the first-order microstate parameters, average microstate duration was most reliable. The duration of class D microstate was longer during mind-wandering (106.8 ms) than verbalization (102.2 ms) or visualization (99.8 ms), with a concomitantly higher coverage (36.4% vs. 34.7% and 35.2%), but otherwise there was no clear association of the four microstate classes to particular mental states. The test-retest reliability was higher at the beginning of each run, together with higher average alpha power and subjective ratings of alertness. Only the transitions between classes C and D (from C to D in particular) were significantly higher than what would be expected from the respective microstates' occurrences. The transition probabilities, however, did not distinguish between conditions, and their test-retest reliability was overall lower than that of the first-order parameters such as duration and coverage. Further studies are needed to establish the functional significance of the canonical EEG microstate classes. This might be more fruitfully achieved by looking at their complex syntax beyond pairwise transitions. To ensure greater test-retest reliability of microstate parameters, study designs should allow for shorter experimental runs with regular breaks, particularly when using EEG microstates as clinical biomarkers.BIAL Foundation (grant number: 183/16)
Simulating and Reconstructing Neurodynamics with Epsilon-Automata Applied to Electroencephalography (EEG) Microstate Sequences
We introduce new techniques to the analysis of neural spatiotemporal dynamics
via applying -machine reconstruction to electroencephalography (EEG)
microstate sequences. Microstates are short duration quasi-stable states of the
dynamically changing electrical field topographies recorded via an array of
electrodes from the human scalp, and cluster into four canonical classes. The
sequence of microstates observed under particular conditions can be considered
an information source with unknown underlying structure. -machines
are discrete dynamical system automata with state-dependent probabilities on
different future observations (in this case the next measured EEG microstate).
They artificially reproduce underlying structure in an optimally predictive
manner as generative models exhibiting dynamics emulating the behaviour of the
source. Here we present experiments using both simulations and empirical data
supporting the value of associating these discrete dynamical systems with
mental states (e.g. mind-wandering, focused attention, etc.) and with clinical
populations. The neurodynamics of mental states and clinical populations can
then be further characterized by properties of these dynamical systems,
including: i) statistical complexity (determined by the number of states of the
corresponding -automaton); ii) entropy rate; iii) characteristic
sequence patterning (syntax, probabilistic grammars); iv) duration, persistence
and stability of dynamical patterns; and v) algebraic measures such as
Krohn-Rhodes complexity or holonomy length of the decompositions of these. The
potential applications include the characterization of mental states in
neurodynamic terms for mental health diagnostics, well-being interventions,
human-machine interface, and others on both subject-specific and
group/population-level