SERAP AYDIN
Her research interests are futuristic neuro-markers and modelling in the field of behavioral & computational neuropsychiatry. She has studied on experimental paradigms and data processing/analysis methods as well as smart classifiers to understand brain dynamics in different states such as emotional, cognitive, resting and sleep.
https://avesis.hacettepe.edu.tr/serap.aydin
https://www.researchgate.net/profile/Serap-Aydin-2
https://loop.frontiersin.org/people/264086/overview
Phone: +903123051494(115)
Address: Hacettepe Unv. Medical Faculty, Basic Science Division, Biophysics Dep. Ankara Türkiye
https://avesis.hacettepe.edu.tr/serap.aydin
https://www.researchgate.net/profile/Serap-Aydin-2
https://loop.frontiersin.org/people/264086/overview
Phone: +903123051494(115)
Address: Hacettepe Unv. Medical Faculty, Basic Science Division, Biophysics Dep. Ankara Türkiye
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reduce the noise level on single sweeps, the SM can be used as a pre-filter before various single sweep analysis methods. The second goal of this study is to to present a new approach to extract single sweep AEPs without using a template signal. The SM and a modified scale-space filter (MSSF) are applied consecutively. The SM is applied
to raw data to increase the SNR. The less-noisy sweeps are then individually filtered with the MSSF. This new approach is assessed in both pseudosimulations and experimental studies. The MSSF is also applied to actual auditory brainstem response (ABR) data to obtain a clear ABR from a relatively small number of sweeps. The wavelet transform coefficients (WTCs) corresponding to the signal and noise become distinguishable after the SM. The MSSF is an effective filter in selecting the WTCs of the noise. The estimated single sweep EPs highly resemble the grand average EP although less number of sweeps are evaluated. Small amplitude variations are observed among the estimations. The MSSF applied to EA of 50 sweeps yields an
ABR that best fits to the grand average of 250 sweeps. We concluded that the combination of SM and MSSF is an efficient tool to obtain clear single sweep AEPs. The MSSF reduces the recording time to one-fifth of that required by EA in template ABR estimation. The proposed approach does not use a template signal (which is generally obtained using the average of small number of sweeps). It provides unprecedented results that support the basic assumptions in the additive signal model.
Keywords: Auditory Evoked Potential, Adaptive filtering, Tikhonov regularization,Wavelet Transform
Discussion and conclusion
In the present study, healthy adults having different cognitive abilities in management of negative emotions in daily life were identified by resting-state Graph Theoretic network measures in both EO and EC states. The individuals were grouped according to their use of positive or negative cognitive/behavioral ERS. For each group, connectivity matrices were estimated by examining DTF based on Granger causality insensitive to volume conduction. BCT was used to compute the network measures from adjacency matrices, i.e. binary transformation of connectivity matrices according to non-overlapped short EEG segments across 61-channel recordings (VEOG recordings were not included in connectivity estimations). The groups were firstly classified by using LSTMNs driven by six different network measures together (CC, LE, GE, T, Q, r) with respect to both states (EO, EC) and frequency band intervals. In comparing both cognitive and behavioral opposing ERS, the highest classification performance was provided by full-band specific measures in EC state that refer the default mode network (DMN) of the brain. Eyes-opening can induce significant neural activities due to many external stimuli (Gorantla 2020). Therefore, eyes-closed resting-state can be conducive to understanding the dynamic characteristics of the brain (Liu and Wu 2020). The current results are compatible with these DMN research.
Regarding EC state, the main full-band specific findings are discussed in following items:
Frequent use of rumination is found to be characterized by high modularity due to maladaptive and repetitive negative thoughts that trigger re-experiencing negative emotions. In recent neuro-imaging studies reveal that depression causes the increase in network modularity in resting-state (, Li, BJ, Friston, K, 2018). It is well known that ruminative thoughts that result in failure to manage negative emotions lead to depression.Therefore, the present electrophysiological findings are clearly consistent with neuro-imaging results.
The frequent use expressive suppression is found to be characterized by high network resilience. Conceptually, functional network resilience has been linked with cognitive skills in both healthy (Stern 2018), and neuro-degenerative disorders (Cabeza 2018). Therefore, neuro-imaging discussions about network resilience supports the present results including the lower resilience originated from rumination and the higher resilience originated from behavioural ERs. Behavioural ERs provide the increase in network integration in comparison to cognitive ERs.
The large number of outliers were commonly observed in LE estimations in each group. These outliers might be originated from age differences among the individuals with varying ages lied between 20 and 65 because of the fact that LE was reported as incremental until adulthood in healthy subjects and then dropped with aging, while GE was found to be almost unchanged over the lifetime (Gao and Gilmore 2011).
In conclusion, Graph Theoretical global connectivity measures are found to be useful in discriminating opposing ERS in resting-state. In other words, the scores of the psychological metrics can be correlated with full-band network measures by means of segregation, integration and modularity of the brain. In particular, both segregation and integration are found to be highly sensitive to not only frequency band interval but also cognitive abilities, while the resilience represented as network assortativity is found to be almost insensitive to frequency interval. Since the brain is composed of spatially embedded complex sub-networks, there must be a balance between integration and segregation of neural information flow result in cognition and behavior in healthy brains (Bullmore and Sporns 2009). The later studies show that the number and strength of neural connections can change with aging, but the optimal balance occurred between neuronal wiring costs and communication efficiency (Bullmore and Sporns 2012; Cao 2014). Thus, the current overall findings can be concluded that cortico-functional balance is impaired by the presence of ruminative and negative thoughts. The present new findings are also compatible with the more recent neuro-imaging studies including structural connectivity analysis based on fMRI (Wang et al. 2021).
Data Availability and Information Sharing Statement
Both EEG data and behavioural test scores are openly available and are distributed along with the a data repository so called LEMON described in reference (Babayan 2019). http://fcon_1000.projects.nitrc.org/indi/retro/MPI_LEMON.html
Discussion:
In the present study, eyes-opened resting state surface EEG measurements were analyzed in order to investigate the possible cross-relation between inter-hemispheric neuronal coherence levels and contrasting cognitive emotion management skills. For this purpose, the features were computed by applying four different functional connectivity metrics to full-band, Alpha-band and Beta-band intervals of EEG series.
Regarding the use of spectral COH in comparison to PLV in discrimination of diversity between individuals having diverse cognitive skills or mental well-being, the application parameters as well as dta collection procedures differ from each other as listed in Table 4. The highest number of EEG recording channels are considered in the present study for classification of contrasting cognitive abilities in healthy individuals. Raw data is primarily filtered by an IIR-Notch filter and short non-overlapped EEG segments are filtered by three FIR filters in extracting full-band (0.5-40.5 Hz), alpha-band and beta-band in EO state, while BP is the mostly used pre-filter in other studies in Table 4. The largest recording interval (120h) and the largest EEG segments are examined in reference study to estimate the outcome of postanoxic coma (Gomez et al., 2021). In this study, the dependency metrics (COH, PLV, MI) are combined to determine a huge feature set, then, classifiers are trained with non-averaged estimations from segments of 5min over 120h. In both Handayani et al., 2018 and Zhang et al., 2014, the individuals are identified by the grand averaged estimations from 50% overlapped long segments (8sec and 10sec). As well, inter-hemispheric functional indicator is defined as he grand averaged dependency estimations from 75% overlapped shorter segments (4sec) in (Dell’Acqua et al., 2021). In the present study, cognitive abilities are identified by non-averaged dependency estimations from non-overlapped short segments (2sec) over 2min rather than the identification of individuals with averaged estimations from overlapped longer segments. In computing spectral COH values, most the studies commonly use WM in combination with PM (Handayani et al., 2018; Dell’Acqua et al., 2021; Zhang et al., 2014), while the others use either FT (Gomez et al., 2021; Mezeiova & Palus, 2012) or WT (Bob & Palus, 2008; Hussain et al., 2018). In the present study, BM is used to estimate spectral COH values based on the assumption that short EEG segments can be modeled by an Auto-Regressive model. So, the COH estimations provided the best results in classification of contrasting cognitive skills.
In analysis of resting-state surface EEG measurements, the following specifications can be proposed:
1) the length of EEG segment should be short as 2sec due to nerve action potential generation and propagation mechanism in addition to post-synaptic neurotransmitter transition during rest without any stimulus.
2) FIR filter should be used to extract specific frequency interval with sensitive and realistic filtering parameters
3) Short EEG segments can be assumed to be modeled by Auto-Regressive model
Averaging process cause loss of information in long EEG measurements due to time-varying post-synaptic potential variations across the cortex. Therefore, non-averaged inter-hemispheric dependency estimations should be used as indicators in detecting specific cognitive or mental states.
WOS:000257692900002
Electroencephalographic complexity and decreased randomness in drug-naive obsessive-compulsive patients
Objective: Studies investigating the complexity in electroencephalography (EEG) in various neuropsychiatric disorders have yielded abnormal results. However, few studies have examined EEG complexity in obsessive-compulsive disorder (OCD).
Methods: An eyes-closed scalp EEG series of 3 minutes was recorded in drug-naive patients with OCD and in healthy controls. Each single trial was segmented into multiple identical epochs using two windows of 10 and 30 seconds. Both Kolmogorov Complexity (KC) values and autoregressive (AR) model orders were estimated to quantify the EEG complexity for segmented EEG epochs.
Results: The EEG complexity, measured by both KC and AR model orders and in estimations using window lengths of 10 and 30 seconds, was lower in the patients than in the controls. In the AR model orders, the 10-second window differentiated the patients and controls better than the 30-second window. Conclusion: OCD is characterized by low EEG complexity, increased regularity, or decreased randomness. Segmentation of EEG signals is useful for their quantitative identification, a smaller window providing a more sensitive characterization of EEG.
Keywords: Autoregressive model order, EEG complexity, Kolmogorov complexity, obsessive compulsive disorder
In conclusion, the brains of drug-naive OCD patients are electrophysiologically less complex, more regular, and more random than the brains of controls. Investigating EEG trace in smaller window lengths may be more successful in differentiating patients and controls. These findings may contribute to the discussions of increased or decreased brain connectivity in the pathologies of the central nervous system when evaluated together with the former and future studies in this area.
Discussion and conclusion
In the present study, four hemispheric connectivity measurements were examined to obtain the electrophysiological arousals on sleep EEG epoches recorded from healthy controls and patients with PPI. All individuals can be classified correctly by using any data mining classifier for both entropy based MI estimations and spectral connectivity measurement so called coherence created by Welch’ method. When the Burg method was performed to compute the power spectral density estimation of sleep EEG epoch in estimating coherence, error free classification can be obtained by using only three classifiers (RBFNetwork, NNge, SMO). Regarding as PCC estimations, one person was misclassified in all classifiers. Concerning phase coherence estimations, one or two individuals were always misclassified.
In particular, lower interhemispheric coherence and lower MI estimations as well as higher PCC values were provided by patients in comparison to controls. In fact, only linear relations between particular hemispheric locations could be observed by using coherence, whereas the MI can measure both linear and nonlinear statistical dependencies of hemispheres in time domain. The results support that the cortex becomes more inactive as the sleep stage goes through from one stage to the next one in non REM sleep periods (stage.1–4), however, the cortex becomes much more active. It means that more neurons will be active in processing the information transmission during REM sleep in REM sleep periods. The higher order statistics of time series can be represented by nonlinear approaches, regarding as the information theory [7]. Therefore, the MI provided the most useful estimations.
The MI can give information in the context of functional connectivity such that its value highly depends on the accuracy of estimated JE derived from probability distribution. The results revealed that temporal dependency of cerebral hemispheres by means of MI can provide a very efficient tool for detection of PPI from sleep EEG recordings. The MI is a measure of statistical dependence between two random time series without making any assumption on the nature of these signals. Since, the duration of each single epoch was long enough, MI estimations gave stable estimates. Another factor making the MI be successful in detecting hemispheric functional changes between controls and PPI is that sleep EEG series are narrow band signals as stated in reference [4].
In the further study, the relationship between sleep stages and information transmission of multi-channel EEG measurements in controls will be investigated. Additionally, MI will be used to analyze sleep EEG series in detecting the effects of mood disorder depending on functional disorganization of the brain.
Discussion and conclusion:
Both cortical normal EEG measurements and intracortical epileptic EEG series, in addition to intracortical ictal records, are analyzed in the present study. Several traditional methods and the ARfit are implemented in Matlab to estimate the optimum AR model orders of these diagnostic records. Among them, the ARfit algorithm is found to be reliable and superior.
In literature, it was stated that the changes on the time series components such as oscillation periods and damping times can be characterized by MVAR models with respect to their SVD pairs [14]. Then, the ARfit module is developed to detect these changes. The current results show that the electrophysiological variations on EEG series can also be identified by using the ARfit. In other words, the meaningful sharp oscillations in EEG can be detected owing to the implementation of the ARfit module. Moreover, neither spurious peaks in the spectrum (in case of too high order), nor loss of spectral detail (in case of excessively low order) are encountered in the assessment of the ARfit.
Also, regarding as the PSD estimations, it can be said that the useful AR model orders can also be estimated by using the algorithms of FPE, AIC and CAT. Nevertheless, FPE, AIC and CAT are known to be heuristic and more subjective choices in many applications [21]. However, the ARfit is not heuristic and it is considerable less computational complex such that the optimum model can be estimated about pmax − pmin + 1 times faster than with those traditional algorithms that require pmax − pmin + 1 separate QR factorizations. The other criterion so called the MDL can not produce the adequate orders. In selecting of AR model order, the methods of FPE and AIC minimize the average error variance for a one-step and an information theoretical function, respectively [21]. The methods of FPE and AIC do not yield consistent estimates of the model order as the length of the time series increases whereas both are asymptotically equivalent [21]. The MDL criterion, also called the Bayesian information criterion, uses a penalty function which provides consistent
estimation of the model order [22].
In the ARfit module, the both the effect of rounding errors and data errors are minimized in the SLSA in association with determined approximate confidence interval [16]. The SLSA is stated as a numerically stable procedure in reference [23]. The using of the SLSA provides to obtain a more reliable residual noise variance. In fact, ARfit solves a regularized estimation problem with respect to an ill-conditioned moment matrix weighted with a regularization parameter. In summary, ARfit module is proposed as very useful, fast and efficient tool in brain activities to estimate a reliable AR model order. The results show that the estimated AR model orders can be used as markers to support the clinical findings in diagnose when ARfit is used.
Conclusion: The regularization methods showed better performance compared to EA. It was observed that, the STR is marginally better than the SR in all cases. Note that the STR method is optimum for smooth solutions whereas the SR allows sharp variations in the solutions. The basis vectors are chosen from the dilated and shifted forms of a mother wavelet which resemble the waveform of the auditory EP. The linear combination of these smooth vectors models the EP. In line with the fact that a sharp variation in the coefficients of this combination is not expected, we have not observed the superiority of SR compared to the STR. In addition, the STR method has less computational complexity than the SR method. Thus, the use of the STR method is proposed instead of the SR for template auditory EP estimation. In conclusion, the STR effectively reduces the experimental time (to one-fourth of that required by EA). Both methods are closely related to Bayesian estimation but there is a distinct property between them: the SR solves a linear system where sharp variations are allowed besides; the STR provides the optimum smooth solution for the same system. Since the waveform of the EP signal is similar to a smooth wave having a fast positive peak and following a slower negative peak, the nature of the STR is more suitable in case of EP estimation. The present experimental and simulation based results support this theoretical suggestion such that the STR provides more SNR enhancement. In both simulations and pseudosimulations, the improvements were 20 dB. Besides, 5 dB improvement was obtained in experimental studies. These data dependent different achievements are originated by their autocorrelation functions which directly form the regularization matrix (L2) of interest such that there were no ripples in both pre and post-stimulus intervals in simulations in contrast to experimental data. In addition, actual background EEG noise is different from a white noise.
Discussion and conclusion:
In the present study, insomnia is analyzed in frequency domain by using both linear (i.e., CF) and nonlinear (i.e., MI) EEG synchronization measures. Both intervals of CF and MI results give that the higher degree of EEG synchronization is observed when the brain can not asleep well. The CF is more useful tool than MI in sleep EEG analysis. Besides, it can be said that the clearest difference between ordered and disordered EEG series can be obtained via observation of frequency domain EEG synchronization for REM stages with respect to the other sleep stages.
In conclusion, the degree of EEG synchronization depends on healthy conditions in insomnia. Much lower CF values are obtained if there is no sleep disorder. Therefore, if people have a sleep disorder, it can be detected by consulting the CF curves of sleep EEG. In other words, if one has no sleep disorder, no high EEG synchronization is observed in association with any sleep stages. So, the CF can be proposed to observe the sleep EEG synchronization for detection of insomnia where the PSD estimations should be computed by using the BM. The basic idea of this proposition is that all the sleep stages are assumed to be modeled by low order AR model. As further works, other synchronization measure based on multichannel applications such as Omega Complexity as a global synchronization [14] and phase synchronization [15] will be addressed for sleep EEG analysis in insomnia in future work.
Discussion and Conclusion: In this study, three datasets consisting of normal and epileptic records in addition to ictal series are classified by using several MLNN architectures. The inputs of the NNs, i.e., the signal features, are computed by addressing the ShanEn, LogEn, and SamEn.
The results show that epileptic records (Set-D and Set-E) show lower entropies in comparison to healthy records (Set-A). In particular, seizure activity produces significantly lower entropies. It means that electro- physiological behavior of epileptogenic regions is less complex than behavior of healthy brain. It can be said that lower entropy indicates the severity of epilepsy. Since the LogEn values meet the most reliable features to analyze the nonlinear dynamics of both cortical and intracortical neuronal interactions, we propose the LogEn values as inputs of the MLNN in discriminat- ing the seizure.
Discussion and Conclusion:
In the present study, contrasting discrete emotional states have been classified with SVMs in accordance with two different kernels (RBFs vs GKs) with respect to methodological variables such as dependency methods (PC vs SC), EEG segmentation largeness (6 sec vs 12 sec), threshold definition in transforming the dependency levels into binary adjacency data (60%max and the mean). The group differences are also obtained using statistical one-way Anova tests and logistic regression modeling. The most useful results are provided by PC in larger segments in accordance with the specified threshold as the mean when RBFs are used as the kernels in SVMs. The main result of the study is to show the close correlations between emotional arousal of affective video film clips and Graph Theoretical complex network measures in terms of both segregation and integration.
Methodologically, superior performance of PC can be explained by following items: (1) PC is based on the linear relationship, while SC is based on the monotonic relationships between two variables, (2) PC can work with un-processed variables, while SC can work with rank-ordered variables. Thus, PC can be proposed for estimation of statistical dependency between EEG segments across the cortex in classifying emotions based on Graph Theoretical network analysis.
SVMs have been frequently used to classify EEG based emotional groups Torres-Valencia (2017); Doma and Pirouz (2020); Saeidi and Karwowski (2021). Since, architecture of SVMs is based on statistical learning theory that provides finding the best decision function, kernel specification affects its performance Debnath and Takahashi (2002). In the present study, RBFs are found to be superior to GKs as reported in references Bajaj and Pachori (2013); Kai (2014); Aydin (2019). In conclusion, classification performance of SVM depends on both feature space, i.e. spectral distribution of the features and kernels.
Regarding the identical dataset DREAMER, the best classification performance was obtained in analysis of shorter EEG segments (6 sec) identified by single-channel phase domain local complexity estimations in Aydin (2020), however, the recent useful results are provided in larger EEG segments (12 sec) represented by global brain network indices across the whole cortex. In Table 4, large variety of window length is given in emotion research based on EEG analysis: Due to decisive differences in both emotional stimulus types (visual, auditory, audio-visual) and experimental paradigms (inter-stimulus-inter duration, stimulus display/presentation time, recording equipment (number of recording electrodes)) as well as state definition (pleasantness in accordance with valance scores, negativity in accordance with arousal scores, discrete emotional state), the proposed segmentation length differs from each other. Moreover, emotional features have been extracted from EEG measurements by examining several methods depending on the goal such as observing neural activities at EEG recording placements (local analysis), quantifying inter-hemispheric neural communications (regional EEG analysis) and understanding the brain network mechanism across the whole cortex (global EEG analysis).
While defining this state can be based on the valence scores of the stimuli (pleasant-unpleasant), researchers have also combine the emotional states, placed in the identical quarter of circumplex emotion model (Fig. 4), in a single category (negative-positive). However, each discrete emotional states have been considered as a different state in accordance with discrete emotion model (Fig. 4) and then each discrete emotional state is identified by EEG based Graph Theoretical network measures in the present study, since both nerve action potentials superimposed by post-synaptic potentials including excitatory and inhibitory neurotransmitter activities are embedded in EEG series. Apart from brain-computer interfaces, it is crucial to assign each emotion as a separate discrete state in accordance with discrete emotion model (see Fig. 4), even if they have similar arousal-valance scores, in not only understanding the functional brain mechanism but also recognizing particular neuropsychiatric diseases characterized by perceptional deficit in computational and behavioural neuroscience. Several emotions (such as fear and anger) are considered as members of a single group in accordance with the identical quarter of arousal-valance dimensions in accordance with circumplex emotion model (see Fig. 4), although neurotransmitter activities embedded in EEG series are quite different in every emotional state.
Therefore, emotions are categorized into basic emotions and their derivatives mentioned as mixed emotions. It’s known that neurotransmitters have a great impact on emotion forming, behaviour and psychiatric disorders Ruhé et al. (2007); Liu et al. (2018); Wang et al. (2020). Three neurotransmitters of serotonin, dopamine and norephinephrine are presented as the most important neurotransmitters in psychopharmacology Schatzberg and Nemeroff (2017). The brief role of them is to change (increase/decrease) post-synaptic potentials of pyramidal nerve cells in the brain. Once temporal and spatial summation of synchronized post-synaptic potentials exceeds the threshold level, nerve action potential is generated and then propagated along with secondary neurons. Both generation and propagation of action potentials provide neural information flow across the cortex. Therefore, external stimulus type (auditory, acoustic, visual, audio-visual, somatosensorial, etc.), duration (time-locked, short duration, long duration), intensity (low-moderate-high) and content (emotional/affective, attentional, working memory, recalling memory, etc.) are all sources in releasing the specified neurotransmitters. Therefore, time-varying post-synaptic potentials including both excitatory and inhibitory actions driven by particular neurotransmitters are embedded in EEG segments. The current findings reveal the close association between emotional arousal score of external video stimuli and functional brain connectivity mechanism due to varying neurotransmitter release at pyramidal nerves
Conclusion: In the present study, a new emotional recognition methodology has been presented. The close relationship between affective stimulus parameters and neuro-cortical activities in young females and males in nine discrete emotional states. For this purpose, PCA is applied to PSTM of short EEG segments.
The primary concept was to observe the gender effect on emotional neuro-complexity levels, and then, the second concept was to observe the usefulness of the proposed complexity markers for emotion recognition. In all applications, EEG measurements were segmented into short epochs of 6 s and 12 s in order to investigate the influence of analysis interval for emotion recognition. Conventional and deep learning networks were trained in not only instant classification but also subject classification manners for both segmentation steps. Regarding those main concepts, the results were compatible with to each other: Females were differed from males in E4 (amusement) through CL-4 for both shorter and larger segments. Thus, CL-4 provided the relatively lower performance for discrimination of E4 from baseline in comparison to recognition of the other emotional states for both segment statements. For recognition of E4 (amusement) and E8 (calmness), classification performances were increased when the subjects were classified through CL-4 instead of instant classification. When the analysis interval was largened from 6 s to 12 s, emotion recognition performances were decreased through all classifiers.
Considering emotional EEG complexity levels mediated by audio-visual affective video films, gender differences became slightly un-avoidable when subjects were classified instead of instant classification. Similarly, each emotional states were differed from baseline with very high CA levels when subjects were classified instead of instant classification. Among conventional and deep learning networks, the most useful classifier was CL-4, i.e., CNN. Although, SVM classifiers provided slightly better performance when the number of features was low, deep learning algorithms, CNNs and LSTMNs were found to be better when the large number of features were examined.
In conclusion, mixed emotions highly modulate the functional connectivity of the amygdala with the other regions of the brain. In particular, regional PCPSTM estimations characterize the dynamic signature of emotion formation depending on individual experiences driven by ongoing perception and cognition processes. From EEG signal processing point of view, primary extraction of PSTM reduces the background EEG, i.e., increases the signal-to-noise ratio. Then, application of PCA on PSTM highlights the main harmonics in association with audio-visual evoked potentials embedded in short epochs. Due to ongoing combination of excitatory and inhibitory post-synaptic potentials as well as Action Potentials (APs), EEG series are time-varying psychophysiological signals. In particular, time-varying audio-visual affective stimuli continuously cause both generation and propagation of nerve APs at auditory, visual and cognitive cortices simultaneously. Therefore, EEG segmentation and analysis of short non-overlapped epochs are crucial pre-processes for emotion recognition. As well, the most useful length of short epoch is found to be 6 s due to AP phases lasting about 2 s.
In recent neuroscience studies, it is highlighted that emotional responsiveness of individuals can be clinical support in not only rare disease so called pre-symptomatic Huntington’s disease [101] but also several important and widespread psychiatric conditions such as unipolar depression [102], Parkinson’s disease with lack of dementia [103] and autism spectrum [104] as well as amygdalar lesions [105]. Full-band PSTMCs can be proposed as single-channel emotion recognition system.
https://ieeexplore.ieee.org/document/8933102
reduce the noise level on single sweeps, the SM can be used as a pre-filter before various single sweep analysis methods. The second goal of this study is to to present a new approach to extract single sweep AEPs without using a template signal. The SM and a modified scale-space filter (MSSF) are applied consecutively. The SM is applied
to raw data to increase the SNR. The less-noisy sweeps are then individually filtered with the MSSF. This new approach is assessed in both pseudosimulations and experimental studies. The MSSF is also applied to actual auditory brainstem response (ABR) data to obtain a clear ABR from a relatively small number of sweeps. The wavelet transform coefficients (WTCs) corresponding to the signal and noise become distinguishable after the SM. The MSSF is an effective filter in selecting the WTCs of the noise. The estimated single sweep EPs highly resemble the grand average EP although less number of sweeps are evaluated. Small amplitude variations are observed among the estimations. The MSSF applied to EA of 50 sweeps yields an
ABR that best fits to the grand average of 250 sweeps. We concluded that the combination of SM and MSSF is an efficient tool to obtain clear single sweep AEPs. The MSSF reduces the recording time to one-fifth of that required by EA in template ABR estimation. The proposed approach does not use a template signal (which is generally obtained using the average of small number of sweeps). It provides unprecedented results that support the basic assumptions in the additive signal model.
Keywords: Auditory Evoked Potential, Adaptive filtering, Tikhonov regularization,Wavelet Transform
Discussion and conclusion
In the present study, healthy adults having different cognitive abilities in management of negative emotions in daily life were identified by resting-state Graph Theoretic network measures in both EO and EC states. The individuals were grouped according to their use of positive or negative cognitive/behavioral ERS. For each group, connectivity matrices were estimated by examining DTF based on Granger causality insensitive to volume conduction. BCT was used to compute the network measures from adjacency matrices, i.e. binary transformation of connectivity matrices according to non-overlapped short EEG segments across 61-channel recordings (VEOG recordings were not included in connectivity estimations). The groups were firstly classified by using LSTMNs driven by six different network measures together (CC, LE, GE, T, Q, r) with respect to both states (EO, EC) and frequency band intervals. In comparing both cognitive and behavioral opposing ERS, the highest classification performance was provided by full-band specific measures in EC state that refer the default mode network (DMN) of the brain. Eyes-opening can induce significant neural activities due to many external stimuli (Gorantla 2020). Therefore, eyes-closed resting-state can be conducive to understanding the dynamic characteristics of the brain (Liu and Wu 2020). The current results are compatible with these DMN research.
Regarding EC state, the main full-band specific findings are discussed in following items:
Frequent use of rumination is found to be characterized by high modularity due to maladaptive and repetitive negative thoughts that trigger re-experiencing negative emotions. In recent neuro-imaging studies reveal that depression causes the increase in network modularity in resting-state (, Li, BJ, Friston, K, 2018). It is well known that ruminative thoughts that result in failure to manage negative emotions lead to depression.Therefore, the present electrophysiological findings are clearly consistent with neuro-imaging results.
The frequent use expressive suppression is found to be characterized by high network resilience. Conceptually, functional network resilience has been linked with cognitive skills in both healthy (Stern 2018), and neuro-degenerative disorders (Cabeza 2018). Therefore, neuro-imaging discussions about network resilience supports the present results including the lower resilience originated from rumination and the higher resilience originated from behavioural ERs. Behavioural ERs provide the increase in network integration in comparison to cognitive ERs.
The large number of outliers were commonly observed in LE estimations in each group. These outliers might be originated from age differences among the individuals with varying ages lied between 20 and 65 because of the fact that LE was reported as incremental until adulthood in healthy subjects and then dropped with aging, while GE was found to be almost unchanged over the lifetime (Gao and Gilmore 2011).
In conclusion, Graph Theoretical global connectivity measures are found to be useful in discriminating opposing ERS in resting-state. In other words, the scores of the psychological metrics can be correlated with full-band network measures by means of segregation, integration and modularity of the brain. In particular, both segregation and integration are found to be highly sensitive to not only frequency band interval but also cognitive abilities, while the resilience represented as network assortativity is found to be almost insensitive to frequency interval. Since the brain is composed of spatially embedded complex sub-networks, there must be a balance between integration and segregation of neural information flow result in cognition and behavior in healthy brains (Bullmore and Sporns 2009). The later studies show that the number and strength of neural connections can change with aging, but the optimal balance occurred between neuronal wiring costs and communication efficiency (Bullmore and Sporns 2012; Cao 2014). Thus, the current overall findings can be concluded that cortico-functional balance is impaired by the presence of ruminative and negative thoughts. The present new findings are also compatible with the more recent neuro-imaging studies including structural connectivity analysis based on fMRI (Wang et al. 2021).
Data Availability and Information Sharing Statement
Both EEG data and behavioural test scores are openly available and are distributed along with the a data repository so called LEMON described in reference (Babayan 2019). http://fcon_1000.projects.nitrc.org/indi/retro/MPI_LEMON.html
Discussion:
In the present study, eyes-opened resting state surface EEG measurements were analyzed in order to investigate the possible cross-relation between inter-hemispheric neuronal coherence levels and contrasting cognitive emotion management skills. For this purpose, the features were computed by applying four different functional connectivity metrics to full-band, Alpha-band and Beta-band intervals of EEG series.
Regarding the use of spectral COH in comparison to PLV in discrimination of diversity between individuals having diverse cognitive skills or mental well-being, the application parameters as well as dta collection procedures differ from each other as listed in Table 4. The highest number of EEG recording channels are considered in the present study for classification of contrasting cognitive abilities in healthy individuals. Raw data is primarily filtered by an IIR-Notch filter and short non-overlapped EEG segments are filtered by three FIR filters in extracting full-band (0.5-40.5 Hz), alpha-band and beta-band in EO state, while BP is the mostly used pre-filter in other studies in Table 4. The largest recording interval (120h) and the largest EEG segments are examined in reference study to estimate the outcome of postanoxic coma (Gomez et al., 2021). In this study, the dependency metrics (COH, PLV, MI) are combined to determine a huge feature set, then, classifiers are trained with non-averaged estimations from segments of 5min over 120h. In both Handayani et al., 2018 and Zhang et al., 2014, the individuals are identified by the grand averaged estimations from 50% overlapped long segments (8sec and 10sec). As well, inter-hemispheric functional indicator is defined as he grand averaged dependency estimations from 75% overlapped shorter segments (4sec) in (Dell’Acqua et al., 2021). In the present study, cognitive abilities are identified by non-averaged dependency estimations from non-overlapped short segments (2sec) over 2min rather than the identification of individuals with averaged estimations from overlapped longer segments. In computing spectral COH values, most the studies commonly use WM in combination with PM (Handayani et al., 2018; Dell’Acqua et al., 2021; Zhang et al., 2014), while the others use either FT (Gomez et al., 2021; Mezeiova & Palus, 2012) or WT (Bob & Palus, 2008; Hussain et al., 2018). In the present study, BM is used to estimate spectral COH values based on the assumption that short EEG segments can be modeled by an Auto-Regressive model. So, the COH estimations provided the best results in classification of contrasting cognitive skills.
In analysis of resting-state surface EEG measurements, the following specifications can be proposed:
1) the length of EEG segment should be short as 2sec due to nerve action potential generation and propagation mechanism in addition to post-synaptic neurotransmitter transition during rest without any stimulus.
2) FIR filter should be used to extract specific frequency interval with sensitive and realistic filtering parameters
3) Short EEG segments can be assumed to be modeled by Auto-Regressive model
Averaging process cause loss of information in long EEG measurements due to time-varying post-synaptic potential variations across the cortex. Therefore, non-averaged inter-hemispheric dependency estimations should be used as indicators in detecting specific cognitive or mental states.
WOS:000257692900002
Electroencephalographic complexity and decreased randomness in drug-naive obsessive-compulsive patients
Objective: Studies investigating the complexity in electroencephalography (EEG) in various neuropsychiatric disorders have yielded abnormal results. However, few studies have examined EEG complexity in obsessive-compulsive disorder (OCD).
Methods: An eyes-closed scalp EEG series of 3 minutes was recorded in drug-naive patients with OCD and in healthy controls. Each single trial was segmented into multiple identical epochs using two windows of 10 and 30 seconds. Both Kolmogorov Complexity (KC) values and autoregressive (AR) model orders were estimated to quantify the EEG complexity for segmented EEG epochs.
Results: The EEG complexity, measured by both KC and AR model orders and in estimations using window lengths of 10 and 30 seconds, was lower in the patients than in the controls. In the AR model orders, the 10-second window differentiated the patients and controls better than the 30-second window. Conclusion: OCD is characterized by low EEG complexity, increased regularity, or decreased randomness. Segmentation of EEG signals is useful for their quantitative identification, a smaller window providing a more sensitive characterization of EEG.
Keywords: Autoregressive model order, EEG complexity, Kolmogorov complexity, obsessive compulsive disorder
In conclusion, the brains of drug-naive OCD patients are electrophysiologically less complex, more regular, and more random than the brains of controls. Investigating EEG trace in smaller window lengths may be more successful in differentiating patients and controls. These findings may contribute to the discussions of increased or decreased brain connectivity in the pathologies of the central nervous system when evaluated together with the former and future studies in this area.
Discussion and conclusion
In the present study, four hemispheric connectivity measurements were examined to obtain the electrophysiological arousals on sleep EEG epoches recorded from healthy controls and patients with PPI. All individuals can be classified correctly by using any data mining classifier for both entropy based MI estimations and spectral connectivity measurement so called coherence created by Welch’ method. When the Burg method was performed to compute the power spectral density estimation of sleep EEG epoch in estimating coherence, error free classification can be obtained by using only three classifiers (RBFNetwork, NNge, SMO). Regarding as PCC estimations, one person was misclassified in all classifiers. Concerning phase coherence estimations, one or two individuals were always misclassified.
In particular, lower interhemispheric coherence and lower MI estimations as well as higher PCC values were provided by patients in comparison to controls. In fact, only linear relations between particular hemispheric locations could be observed by using coherence, whereas the MI can measure both linear and nonlinear statistical dependencies of hemispheres in time domain. The results support that the cortex becomes more inactive as the sleep stage goes through from one stage to the next one in non REM sleep periods (stage.1–4), however, the cortex becomes much more active. It means that more neurons will be active in processing the information transmission during REM sleep in REM sleep periods. The higher order statistics of time series can be represented by nonlinear approaches, regarding as the information theory [7]. Therefore, the MI provided the most useful estimations.
The MI can give information in the context of functional connectivity such that its value highly depends on the accuracy of estimated JE derived from probability distribution. The results revealed that temporal dependency of cerebral hemispheres by means of MI can provide a very efficient tool for detection of PPI from sleep EEG recordings. The MI is a measure of statistical dependence between two random time series without making any assumption on the nature of these signals. Since, the duration of each single epoch was long enough, MI estimations gave stable estimates. Another factor making the MI be successful in detecting hemispheric functional changes between controls and PPI is that sleep EEG series are narrow band signals as stated in reference [4].
In the further study, the relationship between sleep stages and information transmission of multi-channel EEG measurements in controls will be investigated. Additionally, MI will be used to analyze sleep EEG series in detecting the effects of mood disorder depending on functional disorganization of the brain.
Discussion and conclusion:
Both cortical normal EEG measurements and intracortical epileptic EEG series, in addition to intracortical ictal records, are analyzed in the present study. Several traditional methods and the ARfit are implemented in Matlab to estimate the optimum AR model orders of these diagnostic records. Among them, the ARfit algorithm is found to be reliable and superior.
In literature, it was stated that the changes on the time series components such as oscillation periods and damping times can be characterized by MVAR models with respect to their SVD pairs [14]. Then, the ARfit module is developed to detect these changes. The current results show that the electrophysiological variations on EEG series can also be identified by using the ARfit. In other words, the meaningful sharp oscillations in EEG can be detected owing to the implementation of the ARfit module. Moreover, neither spurious peaks in the spectrum (in case of too high order), nor loss of spectral detail (in case of excessively low order) are encountered in the assessment of the ARfit.
Also, regarding as the PSD estimations, it can be said that the useful AR model orders can also be estimated by using the algorithms of FPE, AIC and CAT. Nevertheless, FPE, AIC and CAT are known to be heuristic and more subjective choices in many applications [21]. However, the ARfit is not heuristic and it is considerable less computational complex such that the optimum model can be estimated about pmax − pmin + 1 times faster than with those traditional algorithms that require pmax − pmin + 1 separate QR factorizations. The other criterion so called the MDL can not produce the adequate orders. In selecting of AR model order, the methods of FPE and AIC minimize the average error variance for a one-step and an information theoretical function, respectively [21]. The methods of FPE and AIC do not yield consistent estimates of the model order as the length of the time series increases whereas both are asymptotically equivalent [21]. The MDL criterion, also called the Bayesian information criterion, uses a penalty function which provides consistent
estimation of the model order [22].
In the ARfit module, the both the effect of rounding errors and data errors are minimized in the SLSA in association with determined approximate confidence interval [16]. The SLSA is stated as a numerically stable procedure in reference [23]. The using of the SLSA provides to obtain a more reliable residual noise variance. In fact, ARfit solves a regularized estimation problem with respect to an ill-conditioned moment matrix weighted with a regularization parameter. In summary, ARfit module is proposed as very useful, fast and efficient tool in brain activities to estimate a reliable AR model order. The results show that the estimated AR model orders can be used as markers to support the clinical findings in diagnose when ARfit is used.
Conclusion: The regularization methods showed better performance compared to EA. It was observed that, the STR is marginally better than the SR in all cases. Note that the STR method is optimum for smooth solutions whereas the SR allows sharp variations in the solutions. The basis vectors are chosen from the dilated and shifted forms of a mother wavelet which resemble the waveform of the auditory EP. The linear combination of these smooth vectors models the EP. In line with the fact that a sharp variation in the coefficients of this combination is not expected, we have not observed the superiority of SR compared to the STR. In addition, the STR method has less computational complexity than the SR method. Thus, the use of the STR method is proposed instead of the SR for template auditory EP estimation. In conclusion, the STR effectively reduces the experimental time (to one-fourth of that required by EA). Both methods are closely related to Bayesian estimation but there is a distinct property between them: the SR solves a linear system where sharp variations are allowed besides; the STR provides the optimum smooth solution for the same system. Since the waveform of the EP signal is similar to a smooth wave having a fast positive peak and following a slower negative peak, the nature of the STR is more suitable in case of EP estimation. The present experimental and simulation based results support this theoretical suggestion such that the STR provides more SNR enhancement. In both simulations and pseudosimulations, the improvements were 20 dB. Besides, 5 dB improvement was obtained in experimental studies. These data dependent different achievements are originated by their autocorrelation functions which directly form the regularization matrix (L2) of interest such that there were no ripples in both pre and post-stimulus intervals in simulations in contrast to experimental data. In addition, actual background EEG noise is different from a white noise.
Discussion and conclusion:
In the present study, insomnia is analyzed in frequency domain by using both linear (i.e., CF) and nonlinear (i.e., MI) EEG synchronization measures. Both intervals of CF and MI results give that the higher degree of EEG synchronization is observed when the brain can not asleep well. The CF is more useful tool than MI in sleep EEG analysis. Besides, it can be said that the clearest difference between ordered and disordered EEG series can be obtained via observation of frequency domain EEG synchronization for REM stages with respect to the other sleep stages.
In conclusion, the degree of EEG synchronization depends on healthy conditions in insomnia. Much lower CF values are obtained if there is no sleep disorder. Therefore, if people have a sleep disorder, it can be detected by consulting the CF curves of sleep EEG. In other words, if one has no sleep disorder, no high EEG synchronization is observed in association with any sleep stages. So, the CF can be proposed to observe the sleep EEG synchronization for detection of insomnia where the PSD estimations should be computed by using the BM. The basic idea of this proposition is that all the sleep stages are assumed to be modeled by low order AR model. As further works, other synchronization measure based on multichannel applications such as Omega Complexity as a global synchronization [14] and phase synchronization [15] will be addressed for sleep EEG analysis in insomnia in future work.
Discussion and Conclusion: In this study, three datasets consisting of normal and epileptic records in addition to ictal series are classified by using several MLNN architectures. The inputs of the NNs, i.e., the signal features, are computed by addressing the ShanEn, LogEn, and SamEn.
The results show that epileptic records (Set-D and Set-E) show lower entropies in comparison to healthy records (Set-A). In particular, seizure activity produces significantly lower entropies. It means that electro- physiological behavior of epileptogenic regions is less complex than behavior of healthy brain. It can be said that lower entropy indicates the severity of epilepsy. Since the LogEn values meet the most reliable features to analyze the nonlinear dynamics of both cortical and intracortical neuronal interactions, we propose the LogEn values as inputs of the MLNN in discriminat- ing the seizure.
Discussion and Conclusion:
In the present study, contrasting discrete emotional states have been classified with SVMs in accordance with two different kernels (RBFs vs GKs) with respect to methodological variables such as dependency methods (PC vs SC), EEG segmentation largeness (6 sec vs 12 sec), threshold definition in transforming the dependency levels into binary adjacency data (60%max and the mean). The group differences are also obtained using statistical one-way Anova tests and logistic regression modeling. The most useful results are provided by PC in larger segments in accordance with the specified threshold as the mean when RBFs are used as the kernels in SVMs. The main result of the study is to show the close correlations between emotional arousal of affective video film clips and Graph Theoretical complex network measures in terms of both segregation and integration.
Methodologically, superior performance of PC can be explained by following items: (1) PC is based on the linear relationship, while SC is based on the monotonic relationships between two variables, (2) PC can work with un-processed variables, while SC can work with rank-ordered variables. Thus, PC can be proposed for estimation of statistical dependency between EEG segments across the cortex in classifying emotions based on Graph Theoretical network analysis.
SVMs have been frequently used to classify EEG based emotional groups Torres-Valencia (2017); Doma and Pirouz (2020); Saeidi and Karwowski (2021). Since, architecture of SVMs is based on statistical learning theory that provides finding the best decision function, kernel specification affects its performance Debnath and Takahashi (2002). In the present study, RBFs are found to be superior to GKs as reported in references Bajaj and Pachori (2013); Kai (2014); Aydin (2019). In conclusion, classification performance of SVM depends on both feature space, i.e. spectral distribution of the features and kernels.
Regarding the identical dataset DREAMER, the best classification performance was obtained in analysis of shorter EEG segments (6 sec) identified by single-channel phase domain local complexity estimations in Aydin (2020), however, the recent useful results are provided in larger EEG segments (12 sec) represented by global brain network indices across the whole cortex. In Table 4, large variety of window length is given in emotion research based on EEG analysis: Due to decisive differences in both emotional stimulus types (visual, auditory, audio-visual) and experimental paradigms (inter-stimulus-inter duration, stimulus display/presentation time, recording equipment (number of recording electrodes)) as well as state definition (pleasantness in accordance with valance scores, negativity in accordance with arousal scores, discrete emotional state), the proposed segmentation length differs from each other. Moreover, emotional features have been extracted from EEG measurements by examining several methods depending on the goal such as observing neural activities at EEG recording placements (local analysis), quantifying inter-hemispheric neural communications (regional EEG analysis) and understanding the brain network mechanism across the whole cortex (global EEG analysis).
While defining this state can be based on the valence scores of the stimuli (pleasant-unpleasant), researchers have also combine the emotional states, placed in the identical quarter of circumplex emotion model (Fig. 4), in a single category (negative-positive). However, each discrete emotional states have been considered as a different state in accordance with discrete emotion model (Fig. 4) and then each discrete emotional state is identified by EEG based Graph Theoretical network measures in the present study, since both nerve action potentials superimposed by post-synaptic potentials including excitatory and inhibitory neurotransmitter activities are embedded in EEG series. Apart from brain-computer interfaces, it is crucial to assign each emotion as a separate discrete state in accordance with discrete emotion model (see Fig. 4), even if they have similar arousal-valance scores, in not only understanding the functional brain mechanism but also recognizing particular neuropsychiatric diseases characterized by perceptional deficit in computational and behavioural neuroscience. Several emotions (such as fear and anger) are considered as members of a single group in accordance with the identical quarter of arousal-valance dimensions in accordance with circumplex emotion model (see Fig. 4), although neurotransmitter activities embedded in EEG series are quite different in every emotional state.
Therefore, emotions are categorized into basic emotions and their derivatives mentioned as mixed emotions. It’s known that neurotransmitters have a great impact on emotion forming, behaviour and psychiatric disorders Ruhé et al. (2007); Liu et al. (2018); Wang et al. (2020). Three neurotransmitters of serotonin, dopamine and norephinephrine are presented as the most important neurotransmitters in psychopharmacology Schatzberg and Nemeroff (2017). The brief role of them is to change (increase/decrease) post-synaptic potentials of pyramidal nerve cells in the brain. Once temporal and spatial summation of synchronized post-synaptic potentials exceeds the threshold level, nerve action potential is generated and then propagated along with secondary neurons. Both generation and propagation of action potentials provide neural information flow across the cortex. Therefore, external stimulus type (auditory, acoustic, visual, audio-visual, somatosensorial, etc.), duration (time-locked, short duration, long duration), intensity (low-moderate-high) and content (emotional/affective, attentional, working memory, recalling memory, etc.) are all sources in releasing the specified neurotransmitters. Therefore, time-varying post-synaptic potentials including both excitatory and inhibitory actions driven by particular neurotransmitters are embedded in EEG segments. The current findings reveal the close association between emotional arousal score of external video stimuli and functional brain connectivity mechanism due to varying neurotransmitter release at pyramidal nerves
Conclusion: In the present study, a new emotional recognition methodology has been presented. The close relationship between affective stimulus parameters and neuro-cortical activities in young females and males in nine discrete emotional states. For this purpose, PCA is applied to PSTM of short EEG segments.
The primary concept was to observe the gender effect on emotional neuro-complexity levels, and then, the second concept was to observe the usefulness of the proposed complexity markers for emotion recognition. In all applications, EEG measurements were segmented into short epochs of 6 s and 12 s in order to investigate the influence of analysis interval for emotion recognition. Conventional and deep learning networks were trained in not only instant classification but also subject classification manners for both segmentation steps. Regarding those main concepts, the results were compatible with to each other: Females were differed from males in E4 (amusement) through CL-4 for both shorter and larger segments. Thus, CL-4 provided the relatively lower performance for discrimination of E4 from baseline in comparison to recognition of the other emotional states for both segment statements. For recognition of E4 (amusement) and E8 (calmness), classification performances were increased when the subjects were classified through CL-4 instead of instant classification. When the analysis interval was largened from 6 s to 12 s, emotion recognition performances were decreased through all classifiers.
Considering emotional EEG complexity levels mediated by audio-visual affective video films, gender differences became slightly un-avoidable when subjects were classified instead of instant classification. Similarly, each emotional states were differed from baseline with very high CA levels when subjects were classified instead of instant classification. Among conventional and deep learning networks, the most useful classifier was CL-4, i.e., CNN. Although, SVM classifiers provided slightly better performance when the number of features was low, deep learning algorithms, CNNs and LSTMNs were found to be better when the large number of features were examined.
In conclusion, mixed emotions highly modulate the functional connectivity of the amygdala with the other regions of the brain. In particular, regional PCPSTM estimations characterize the dynamic signature of emotion formation depending on individual experiences driven by ongoing perception and cognition processes. From EEG signal processing point of view, primary extraction of PSTM reduces the background EEG, i.e., increases the signal-to-noise ratio. Then, application of PCA on PSTM highlights the main harmonics in association with audio-visual evoked potentials embedded in short epochs. Due to ongoing combination of excitatory and inhibitory post-synaptic potentials as well as Action Potentials (APs), EEG series are time-varying psychophysiological signals. In particular, time-varying audio-visual affective stimuli continuously cause both generation and propagation of nerve APs at auditory, visual and cognitive cortices simultaneously. Therefore, EEG segmentation and analysis of short non-overlapped epochs are crucial pre-processes for emotion recognition. As well, the most useful length of short epoch is found to be 6 s due to AP phases lasting about 2 s.
In recent neuroscience studies, it is highlighted that emotional responsiveness of individuals can be clinical support in not only rare disease so called pre-symptomatic Huntington’s disease [101] but also several important and widespread psychiatric conditions such as unipolar depression [102], Parkinson’s disease with lack of dementia [103] and autism spectrum [104] as well as amygdalar lesions [105]. Full-band PSTMCs can be proposed as single-channel emotion recognition system.
https://ieeexplore.ieee.org/document/8933102
Highlights:
· In the present study, a new quantitative single-channel EEG marker called as Frequency Specific Complexity for classiffication of maladaptive rumination at resting-state.
· The reliability of the proposed method has been provided by using seven different 5-fold cross-validated classiffiers with respect to both states (eyes-opened vs eyes-closed) and cortical regions.
· The new findings show that maladaptive rumination cause decrease neuronal complexity at mostly anterior regions.
2 sec over single trials of 1 min. Later, transitivity, clustering coefficients, assortativity, global efficiency and modularity are computed from EEG based connectivity matrices produced by each approach. Since the highest classification accuracy of 83.79%
is provided by PC, statistical tests (one-way Anova, pair-wise multiple comparison) and step-wise logistic regression modelling are all examined to detect significant differences between pre- and post- treatment relevant connectivity measures. Statistical boxplots are also shown, as well. Overall results reveal that global brain connectivity can be increased by long-term medication in pediatric ADHD-C in terms of increased segregation & resilience. This is the first study to demonstrate that long-term medication can normalize the functional brain connectivity in ADHD, which is characterized by decreased connectivity compared to controls