Electroencephalography (EEG) allows recording of cortical activity at high temporal resolution. Creating features useful for the analysis of the EEG recording can be challenging. Here we introduce a new method of pre-processing the time-series for the analysis of the resting state and binary task classification using recurrence quantification analysis (RQA) and compare it with the existing state-of-the-art approach based on signal embedding. To reveal patterns that unfold brain dynamics, we present a new pipeline that does not rely on selection of embedding parameters for RQA. Instead of using EEG time-series signals directly, Short-term Fourier transform (STFT) is used to generate new time-series, based on the power spectra from sliding, overlapping windows. Recurrence plots are created in a standard way from embedded EEG signals, and the STFT vectors. The efficiency of RQA features extracted from such plots is compared in classification of EEG segments that correspond to open and closed eye conditions. In contrast to the common approaches to such analysis, no filtering into separate frequency bands was needed. Differences between the two representations of EEG signals are illustrated using histograms of RQA features and UMAP plots. Classification results at the 95.9% level were obtained using selected features for less than 10 electrodes.
Background The study aimed to determine the resting-state EEG (rsEEG) dynamics quantified using the multivariate Multiscale Entropy (mMSE), and the sex/gender (s/g) differences in the mMSE features. The rsEEG was acquired from 95 healthy adults. For each channel set the AUC, that represents the total complexity, the MaxSlope and AvgEnt referring to the entropy at the fine- and coarse-grained scales, respectively, were extracted. The difference in the entropy between the #9 and #4 timescale (DiffEnt) was also calculated. Results We found the highest AUC for the channel sets corresponding to the somatomotor (SMN), dorsolateral network (DAN) and default mode (DMN) whereas the visual network (VN), limbic (LN), and frontoparietal (FPN) network showed the lowest AUC. The largest MaxSlope were in the SMN, DMN, ventral attention network (VAN), LN and FPN, and the smallest in the VN. The SMN and DAN were characterized by the highest and the LN, FPN, and VN by the lowest AvgEnt. The most stable entropy were for the DAN and VN while the LN showed the greatest drop of entropy at the coarse scales. Women, compared to men, showed higher MaxSlope and DiffEnt but lower AvgEnt in all channel sets and there were no s/g differences in the AUC. Conclusions Novel results of the present study are: 1) an identification of the mMSE features that capture entropy at the fine and the coarse timescales in the channel sets corresponding to the main resting-state networks; 2) an indication of the sex/gender differences in these features.
The study aimed to determine the relationship between the millisecond timing, measured by visual temporal order threshold (TOT), i.e. a minimum gap between two successive stimuli necessary to judge a before-after relation, and resting-state fMRI functional connectivity (rsFC). We assume that the TOT reflects a relatively stable feature of local internal state networks and is associated with rsFC of the temporal parietal junction (TPJ). Sixty five healthy young adults underwent the visual TOT, fluid intelligence (Gf) and an eyes-open resting-state fMRI examination. After controlling for the influence of gender, the higher the TOT, the stronger was the left TPJ’s rsFC with the left postcentral and the right precentral gyri, bilateral putamen and the right supplementary motor area. When the effects of Gf and TOT × Gf interaction were additionally controlled, the TOT—left TPJ’s rsFC relationship survived for almost all above regions with the exception of the left and right putamen. This is the first study demonstrating that visual TOT is associated with rsFC between the areas involved both in sub-second timing and motor control. Current outcomes indicate that the local neural networks are prepared to process brief, rapidly presented, consecutive events, even in the absence of such stimulation.
CelCelem badania pilotażowego było sprawdzenie zależności pomiędzy rozdzielczością czasową w zakresie milisekundowym, pamięcią roboczą oraz inteligencją psychometryczną z uwzględnieniem analizy jakościowej błędów w Teście Matryc Ravena w wersji dla Zaawansowanych TMZ. MetodaTrzydzieści sześć osób (24 mężczyzn i 12 kobiet, w wieku 17–19 lat) wykonało zadanie polegające na prezentowaniu par bodźców w szybkim następstwie czasowym, a następnie rozwiązywało zadanie mierzące pamięć roboczą Automated Operation Span Task Aospan oraz TMZ. Rozdzielczość czasową mierzono za pomocą progu postrzegania kolejności bodźców PPK, wyznaczanego za pomocą algorytmu adaptacyjnego dla poprawności 75%. WynikiWykazano tendencję do rzadszego popełniania błędów typu Błędna Zasada w TMZ przez osoby uzyskujące niskie wartości PPK: rho(34) = 0,46, p < 0,05. Ponadto zaobserwowano związek między wynikami Aospan i TMZ, dla procentu poprawnie odpamiętanych liter (rho(34) = 0,55, p < 0,01), zaś dla procentu poprawnie odpamiętanych sekwencji (rho(34) = 0,43, p = 0,05). KonkluzjePrezentowane badanie jest pierwszym, w którym wykazano związek czasowego opracowywania informacji na poziomie milisekund z typami błędów popełnianymi w teście inteligencji ogólnej. Osoby, które uzyskały wyższe progi postrzegania kolejności bodźców częściej stosowały przy wyborze odpowiedzi jakościowo odmienne od poprawnych reguły rozumowania, co może odzwierciedlać mniejsze zasoby pamięci roboczej potrzebne do odkrycia właściwej reguły.
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