Interpretation of the Lempel-Ziv complexity measure in the context of biomedical signal analysis

Explainable Depression Classification Based on EEG Feature Selection From Audio Stimuli

With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have been widely proposed. However, existing studies have mostly focused on the accuracy of depression recognition, ignoring the association between features and models. Additionally, there is a lack of research on the contribution of different features to depression recognition. To this end, this study introduces an innovative approach to depression detection using EEG data, integrating Ant-Lion Optimization (ALO) and Multi-Agent Reinforcement Learning (MARL) for feature fusion analysis. The inclusion of Explainable Artificial Intelligence (XAI) methods enhances the explainability of the model's features. The Time-Delay Embedded Hidden Markov Model (TDE-HMM) is employed to infer internal brain states during depression, triggered by audio stimulation. The ALO-MARL algorithm, combined with hyper-parameter optimization of the XGBoost classifier, achieves high accuracy (93.69%), sensitivity (88.60%), specificity (97.08%), and F1-score (91.82%) on a auditory stimulus-evoked three-channel EEG dataset. The results suggest that this approach outperforms state-of-the-art feature selection methods for depression recognition on this dataset, and XAI elucidates the critical impact of the minimum value of Power Spectral Density (PSD), Sample Entropy (SampEn), and Rényi Entropy (Ren) on depression recognition. The study also explores dynamic brain state transitions revealed by audio stimuli, providing insights for the clinical application of AI algorithms in depression recognition.

Entropy and Complexity Tools Across Scales in Neuroscience: A Review

Understanding the brain's intricate dynamics across multiple scales-from cellular interactions to large-scale brain behavior-remains one of the most significant challenges in modern neuroscience. Two key concepts, entropy and complexity, have been increasingly employed by neuroscientists as powerful tools for characterizing the interplay between structure and function in the brain across scales. The flexibility of these two concepts enables researchers to explore quantitatively how the brain processes information, adapts to changing environments, and maintains a delicate balance between order and disorder. This review illustrates the main tools and ideas to study neural phenomena using these concepts. This review does not delve into the specific methods or analyses of each study. Instead, it aims to offer a broad overview of how these tools are applied within the neuroscientific community and how they are transforming our understanding of the brain. We focus on their applications across scales, discuss the strengths and limitations of different metrics, and examine their practical applications and theoretical significance.

An On-Board Executable Multi-Feature Transfer-Enhanced Fusion Model for Three-Lead EEG Sensor-Assisted Depression Diagnosis

The development of affective computing and medical electronic technologies has led to the emergence of Artificial Intelligence (AI)-based methods for the early detection of depression. However, previous studies have often overlooked the necessity for the AI-assisted diagnosis system to be wearable and accessible in practical scenarios for depression recognition. In this work, we present an on-board executable multi-feature transfer-enhanced fusion model for our custom-designed wearable three-lead Electroencephalogram (EEG) sensor, based on EEG data collected from 73 depressed patients and 108 healthy controls. Experimental results show that the proposed model exhibits low-computational complexity (65.0 K parameters), promising Floating-Point Operations (FLOPs) performance (25.6 M), real-time processing (1.5 s/execution), and low power consumption (320.8 mW). Furthermore, it requires only 202.0 KB of Random Access Memory (RAM) and 279.6 KB of Read-Only Memory (ROM) when deployed on the EEG sensor. Despite its low computational and spatial complexity, the model achieves a notable classification accuracy of 95.2%, specificity of 94.0%, and sensitivity of 96.9% under independent test conditions. These results underscore the potential of deploying the model on the wearable three-lead EEG sensor for assisting in the diagnosis of depression.

Comparison analysis between standard polysomnographic data and in-ear-electroencephalography signals: a preliminary study

Study Objectives

Polysomnography (PSG) currently serves as the benchmark for evaluating sleep disorders. Its discomfort makes long-term monitoring unfeasible, leading to bias in sleep quality assessment. Hence, less invasive, cost-effective, and portable alternatives need to be explored. One promising contender is the in-ear-electroencephalography (EEG) sensor. This study aims to establish a methodology to assess the similarity between the single-channel in-ear-EEG and standard PSG derivations.

Methods

The study involves 4-hour signals recorded from 10 healthy subjects aged 18-60 years. Recordings are analyzed following two complementary approaches: (1) a hypnogram-based analysis aimed at assessing the agreement between PSG and in-ear-EEG-derived hypnograms; and (2) a feature- and analysis-based on time- and frequency-domain feature extraction, unsupervised feature selection, and definition of Feature-based Similarity Index via Jensen-Shannon Divergence (JSD-FSI).

Results

We find large variability between PSG and in-ear-EEG hypnograms scored by the same sleep expert according to Cohen's kappa metric, with significantly greater agreements for PSG scorers than for in-ear-EEG scorers (p < .001) based on Fleiss' kappa metric. On average, we demonstrate a high similarity between PSG and in-ear-EEG signals in terms of JSD-FSI-0.79 ± 0.06-awake, 0.77 ± 0.07-nonrapid eye movement, and 0.67 ± 0.10-rapid eye movement-and in line with the similarity values computed independently on standard PSG channel combinations.

Conclusions

In-ear-EEG is a valuable solution for home-based sleep monitoring; however, further studies with a larger and more heterogeneous dataset are needed.

Ongoing Dynamics of Peak Alpha Frequency Characterize Hypnotic Induction in Highly Hypnotic-Susceptible Individuals

Hypnotic phenomena exhibit significant inter-individual variability, with some individuals consistently demonstrating efficient responses to hypnotic suggestions, while others show limited susceptibility. Recent neurophysiological studies have added to a growing body of research that shows variability in hypnotic susceptibility is linked to distinct neural characteristics. Building on this foundation, our previous work identified that individuals with high and low hypnotic susceptibility can be differentiated based on the arrhythmic activity observed in resting-state electrophysiology (rs-EEG) outside of hypnosis. However, because previous work has largely focused on mean spectral characteristics, our understanding of the variability over time of these features, and how they relate to hypnotic susceptibility, is still limited. Here we address this gap using a time-resolved assessment of rhythmic alpha peaks and arrhythmic components of the EEG spectrum both prior to and following hypnotic induction. Using multivariate pattern classification, we investigated whether these neural features differ between individuals with high and low susceptibility to hypnosis. Specifically, we used multivariate pattern classification to investigate whether these non-stationary neural features could distinguish between individuals with high and low susceptibility to hypnosis before and after a hypnotic induction. Our analytical approach focused on time-resolved spectral decomposition to capture the intricate dynamics of neural oscillations and their non-oscillatory counterpart, as well as Lempel-Ziv complexity. Our results show that variations in the alpha center frequency are indicative of hypnotic susceptibility, but this discrimination is only evident during hypnosis. Highly hypnotic-susceptible individuals exhibit higher variability in alpha peak center frequency. These findings underscore how dynamic changes in neural states related to alpha peak frequency represent a central neurophysiological feature of hypnosis and hypnotic susceptibility.

Harmonic enhancement to optimize EOG based ocular activity decoding: A hybrid approach with harmonic source separation and EEMD

Intelligent robotic systems for patients with motor impairments have gained significant interest over the past few years. Various sensor types and human-machine interface (HMI) methods have been developed; however, most research in this area has focused on eye-blink-based binary control with minimal electrode placements. This approach restricts the complexity of HMI systems and does not consider the potential of multiple-activity decoding via static ocular activities. These activities pose a decoding challenge due to non-oscillatory noise components, such as muscle tremors or fatigue. To address this issue, a hybrid preprocessing methodology is proposed that combines harmonic source separation and ensemble empirical mode decomposition in the time-frequency domain to remove percussive and non-oscillatory components of static ocular movements. High-frequency components are included in the harmonic enhancement process. Next, a machine learning model with dual input of time-frequency images and a vectorized feature set of consecutive time windows is employed, leading to a 3.8% increase in performance as compared to without harmonic enhancement in leave-one-session-out cross-validation (LOSO). Additionally, a high correlation is found between the harmonic ratios of the static activities in the Hilbert-Huang frequency spectrum and LOSO performances. This finding highlights the potential of leveraging the harmonic characteristics of the activities as a discriminating factor in machine learning-based classification of EOG-based ocular activities, thus providing a new aspect of activity enrichment with minimal performance loss for future HMI systems.

A novel nonlinear bispectrum analysis for dynamical complex oscillations

In this study, we proposed a novel set of bispectrum in constructing both frequency power and complexity spectrum. The uniform phase empirical mode decomposition (UPEMD) was implemented to obtain nonlinear extraction while guaranteeing explicit frequencies. Lepel-Ziv complexity (LZC) and frequency power per mode were used for comprehensive frequency spectra. To examine the performances of the proposed method and meanwhile optimize routine methodological parameters, either chaotic logistic maps or a default non-stationary simulation in 40 ~ 60 Hz along with several challenges were designed. The simulation results showed the UPEMD-based LZC spectrum distinguishes the degree of complexity, reflecting the bandwidth and noise level of the inputs. The UPEMD-based power spectrum on the other side presents power distribution of nonlinear and nonstationary oscillation across multiple frequencies. In addition, given gait disturbance is an unsolved symptom in adaptive deep brain stimulation (DBS) for Parkinson's disease (PD), meanwhile considering the representative of deep brain activities to the complex oscillations, such data were analyzed further. Our results showed the high-frequency band (45 ~ 80 Hz) of the UPEMD-based LZC spectrum reflects the impact of auditory cues in modulating the complexity of DBS recording. Such an increase in complexity (45 ~ 60 Hz) reduces shortly after the cue was removed. As for the UPEMD-based power spectrum, decreasing power over the higher frequency region (> 30 Hz) was shown with auditory cues. These results manifest the potential of the proposed methods in reflecting gait improvement for PD. The proposed bispectrum reflected both the nonlinear complexity and power spectrum analyses, enabling examining targeted frequencies with refined resolution.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11571-023-09953-z.

Laser Doppler Fluximetry in Cutaneous Vasculature: Methods for Data Analyses

INTRODUCTION

Acquisition of a deeper understanding of microvascular function across physiological and pathological conditions can be complicated by poor accessibility of the vascular networks and the necessary sophistication or intrusiveness of the equipment needed to acquire meaningful data. Laser Doppler fluximetry (LDF) provides a mechanism wherein investigators can readily acquire large amounts of data with minor inconvenience for the subject. However, beyond fairly basic analyses of erythrocyte perfusion (fluximetry) data within the cutaneous microcirculation (i.e., perfusion at rest and following imposed challenges), a deeper understanding of microvascular perfusion requires a more sophisticated approach that can be challenging for many investigators.

METHODS

This manuscript provides investigators with clear guidance for data acquisition from human subjects for full analysis of fluximetry data, including levels of perfusion, single- and multiscale Lempel-Ziv complexity (LZC) and sample entropy (SampEn), and wavelet-based analyses for the major physiological components of the signal. Representative data and responses are presented from a recruited cohort of healthy volunteers, and computer codes for full data analysis (MATLAB) are provided to facilitate efforts by interested investigators.

CONCLUSION

It is anticipated that these materials can reduce the challenge to investigators integrating these approaches into their research programs and facilitate translational research in cardiovascular science.

Thermoplastic clear dental aligners under cyclic compression loading: A mechanical performance analysis using acoustic emission technique

The objective of this work is to analyse the performance of clear aligners made of thermoplastic materials. Within this framework, the damage evolution stages and damage states of the aligners at different cycles of the compressive loading are evaluated using the Acoustic Emission (AE) technique. Three different clear aligner systems were prepared: thermoformed PET-g (polyethylene terephthalate glycol) and PU (polyurethane), and additively manufactured PU. Cyclic compression tests are performed to simulate 22500 swallows. The mechanical results show that the energy absorbed by the thermoformed PET-g aligner remains stable around 4 Nmm throughout the test. Although the PU-based aligners show a higher energy absorption of about 7 Nmm during the initial phase of the cyclic loading, this gradually decreases after 12500 cycles. The time-domain based, and frequency-based parameters of the stress wave acoustic signals generated by the aligners under compression loading are used to identify the damage evolution stages. The machine learning-based AE results reveal the initiation and termination of the different damage states in the aligners and the frequency-based results distinguish the different damage sources. Finally, the microscopy results validated the damage occurrences in the aligners identified by the AE results. The mechanical test results indicate that the thermoformed PET-g has the potential to match the performance and requirements of the dentistry of the popular Invisalign (additively manufactured PU). The AE results have the potential to identify at which cycles the aligners may start losing their functionality.

How well do neural signatures of resting-state EEG detect consciousness? A large-scale clinical study

The assessment of consciousness states, especially distinguishing minimally conscious states (MCS) from unresponsive wakefulness states (UWS), constitutes a pivotal role in clinical therapies. Despite that numerous neural signatures of consciousness have been proposed, the effectiveness and reliability of such signatures for clinical consciousness assessment still remains an intense debate. Through a comprehensive review of the literature, inconsistent findings are observed about the effectiveness of diverse neural signatures. Notably, the majority of existing studies have evaluated neural signatures on a limited number of subjects (usually below 30), which may result in uncertain conclusions due to small data bias. This study presents a systematic evaluation of neural signatures with large-scale clinical resting-state electroencephalography (EEG) signals containing 99 UWS, 129 MCS, 36 emergence from the minimally conscious state, and 32 healthy subjects (296 total) collected over 3 years. A total of 380 EEG-based metrics for consciousness detection, including spectrum features, nonlinear measures, functional connectivity, and graph-based measures, are summarized and evaluated. To further mitigate the effect of data bias, the evaluation is performed with bootstrap sampling so that reliable measures can be obtained. The results of this study suggest that relative power in alpha and delta serve as dependable indicators of consciousness. With the MCS group, there is a notable increase in the phase lag index-related connectivity measures and enhanced functional connectivity between brain regions in comparison to the UWS group. A combination of features enables the development of an automatic detector of conscious states.

Predicting the therapeutic response to valproic acid in childhood absence epilepsy through electroencephalogram analysis using machine learning

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel-Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.

Non-linear processing and reinforcement learning to predict rTMS treatment response in depression

BACKGROUND

Forecasting the efficacy of repetitive transcranial magnetic stimulation (rTMS) therapy can lead to substantial time and cost savings by preventing futile treatments. To achieve this objective, we've formulated a machine learning approach aimed at categorizing patients with major depressive disorder (MDD) into two groups: individuals who respond (R) positively to rTMS treatment and those who do not respond (NR).

METHODS

Preceding the commencement of treatment, we obtained resting-state EEG data from 106 patients diagnosed with MDD, employing 32 electrodes for data collection. These patients then underwent a 7-week course of rTMS therapy, and 54 of them exhibited positive responses to the treatment. Employing Independent Component Analysis (ICA) on the EEG data, we successfully pinpointed relevant brain sources that could potentially serve as markers of neural activity within the dorsolateral prefrontal cortex (DLPFC). These identified sources were further scrutinized to estimate the sources of activity within the sensor domain. Then, we integrated supplementary physiological data and implemented specific criteria to yield more realistic estimations when compared to conventional EEG analysis. In the end, we selected components corresponding to the DLPFC region within the sensor domain. Features were derived from the time-series data of these relevant independent components. To identify the most significant features, we used Reinforcement Learning (RL). In categorizing patients into two groups - R and NR to rTMS treatment - we utilized three distinct classification algorithms including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP). We assessed the performance of these classifiers through a ten-fold cross-validation method. Additionally, we conducted a statistical test to evaluate the discriminative capacity of these features between responders and non-responders, opening the door for further exploration in this field.

RESULTS

We identified EEG features that can anticipate the response to rTMS treatment. The most robust discriminators included EEG beta power, the sum of bispectrum diagonal elements in the delta and beta frequency bands. When these features were combined into a single vector, the classification of responders and non-responders achieved impressive performance, with an accuracy of 95.28 %, specificity at 94.23 %, sensitivity reaching 96.29 %, and precision standing at 94.54 %, all achieved using SVM.

CONCLUSIONS

The results of this study suggest that the proposed approach, utilizing power, non-linear, and bispectral features extracted from relevant independent component time-series, has the capability to forecast the treatment outcome of rTMS for MDD patients based solely on a single pre-treatment EEG recording session. The achieved findings demonstrate the superior performance of our method compared to previous techniques.

The Three-Lead EEG Sensor: Introducing an EEG-Assisted Depression Diagnosis System Based on Ant Lion Optimization

For depression diagnosis, traditional methods such as interviews and clinical scales have been widely leveraged in the past few decades, but they are subjective, time-consuming, and labor-consuming. With the development of affective computing and Artificial Intelligence (AI) technologies, Electroencephalogram (EEG)-based depression detection methods have emerged. However, previous research has virtually neglected practical application scenarios, as most studies have focused on analyzing and modeling EEG data. Furthermore, EEG data is typically obtained from specialized devices that are large, complex to operate, and poorly ubiquitous. To address these challenges, a wearable three-lead EEG sensor with flexible electrodes was developed to obtain prefrontal-lobe EEG data. Experimental measurements show that the EEG sensor achieves promising performance (background noise of no more than 0.91 μVpp, Signal-to-Noise Ratio (SNR) of 26--48 dB, and electrode-skin contact impedance of less than 1 K Ω). In addition, EEG data from 70 depressed patients and 108 healthy controls were collected using the EEG sensor, and the linear and nonlinear features were extracted. The features were then weighted and selected using the Ant Lion Optimization (ALO) algorithm to improve classification performance. The experimental results show that the k-NN classifier achieves a classification accuracy of 90.70%, specificity of 96.53%, and sensitivity of 81.79%, indicating the promising potential of the three-lead EEG sensor combined with the ALO algorithm and the k-NN classifier for EEG-assisted depression diagnosis.

A system based on machine learning for improving sleep

Closed-loop auditory stimulation is one of the well-known and emerging sensory stimulation techniques, which achieves the purpose of sleep regulation by driving the EEG slow oscillation (SO, <1 Hz) through auditory stimulation. The main challenge is to accurately identify the stimulation timing and provide feedback in real-time, which has high requirements on the response time and recognition accuracy of the closed-loop auditory stimulation system. To reduce the impact of systematic errors on the regulation results, most traditional closed-loop auditory stimulation systems try to identify a single feature to determine the timing of stimulus delivery and reduce the system feedback delay by simplifying the calculation. Unlike existing closed-loop regulation systems that identify specific brain features, this paper proposes a closed-loop auditory stimulation sleep regulation system deploying machine learning. The process is: through online sleep real-time automatic staging, tracking the sleep stage to provide feedback quickly, and continuously offering external auditory stimulation at a specific SO phase. This paper uses this system to conduct sleep auditory stimulation regulation experiments on ten subjects. The experimental results show that the sleep closed-loop regulation system proposed in this paper can achieve consistency (effective for almost all subjects in the experiment) and immediate (taking effect immediately after stimulation) modulation effects on SOs. More importantly, the proposed method is superior to existing advanced methods. Therefore, the system designed in this paper has great potential to be more reliable and flexible in sleep regulation.

Altered brain dynamics index levels of arousal in complete locked-in syndrome

Complete locked-in syndrome (CLIS) resulting from late-stage amyotrophic lateral sclerosis (ALS) is characterised by loss of motor function and eye movements. The absence of behavioural indicators of consciousness makes the search for neuronal correlates as possible biomarkers clinically and ethically urgent. EEG-based measures of brain dynamics such as power-law exponent (PLE) and Lempel-Ziv complexity (LZC) have been shown to have explanatory power for consciousness and may provide such neuronal indices for patients with CLIS. Here, we validated PLE and LZC (calculated in a dynamic way) as benchmarks of a wide range of arousal states across different reference states of consciousness (e.g., awake, sleep stages, ketamine, sevoflurane). We show a tendency toward high PLE and low LZC, with high intra-subject fluctuations and inter-subject variability in a cohort of CLIS patients with values graded along different arousal states as in our reference data sets. In conclusion, changes in brain dynamics indicate altered arousal in CLIS. Specifically, PLE and LZC are potentially relevant biomarkers to identify or diagnose the arousal level in CLIS and to determine the optimal time point for treatment, including communication attempts.

Assessing consciousness in patients with disorders of consciousness using soft-clustering

Consciousness is something we experience in our everyday life, more especially between the time we wake up in the morning and go to sleep at night, but also during the rapid eye movement (REM) sleep stage. Disorders of consciousness (DoC) are states in which a person's consciousness is damaged, possibly after a traumatic brain injury. Completely locked-in syndrome (CLIS) patients, on the other hand, display covert states of consciousness. Although they appear unconscious, their cognitive functions are mostly intact. Only, they cannot externally display it due to their quadriplegia and inability to speak. Determining these patients' states constitutes a challenging task. The ultimate goal of the approach presented in this paper is to assess these CLIS patients consciousness states. EEG data from DoC patients are used here first, under the assumption that if the proposed approach is able to accurately assess their consciousness states, it will assuredly do so on CLIS patients too. This method combines different sets of features consisting of spectral, complexity and connectivity measures in order to increase the probability of correctly estimating their consciousness levels. The obtained results showed that the proposed approach was able to correctly estimate several DoC patients' consciousness levels. This estimation is intended as a step prior attempting to communicate with them, in order to maximise the efficiency of brain-computer interfaces (BCI)-based communication systems.

A novel approach to T-cell receptor beta chain (TCRB) repertoire encoding using lossless string compression

MOTIVATION

T-cell receptor beta chain (TCRB) repertoires are crucial for understanding immune responses. However, their high diversity and complexity present significant challenges in representation and analysis. The main motivation of this study is to develop a unified and compact representation of a TCRB repertoire that can efficiently capture its inherent complexity and diversity and allow for direct inference.

RESULTS

We introduce a novel approach to TCRB repertoire encoding and analysis, leveraging the Lempel-Ziv 76 algorithm. This approach allows us to create a graph-like model, identify-specific sequence features, and produce a new encoding approach for an individual's repertoire. The proposed representation enables various applications, including generation probability inference, informative feature vector derivation, sequence generation, a new measure for diversity estimation, and a new sequence centrality measure. The approach was applied to four large-scale public TCRB sequencing datasets, demonstrating its potential for a wide range of applications in big biological sequencing data.

AVAILABILITY AND IMPLEMENTATION

Python package for implementation is available https://github.com/MuteJester/LZGraphs.

Examining electroencephalogram signatures of people with multiple sclerosis using a nonlinear dynamics approach: a systematic review and bibliographic analysis

Background

Considering that brain activity involves communication between millions of neurons in a complex network, nonlinear analysis is a viable tool for studying electroencephalography (EEG). The main objective of this review was to collate studies that utilized chaotic measures and nonlinear dynamical analysis in EEG of multiple sclerosis (MS) patients and to discuss the contributions of chaos theory techniques to understanding, diagnosing, and treating MS.

Methods

Using the preferred reporting items for systematic reviews and meta-analysis (PRISMA), the databases EbscoHost, IEEE, ProQuest, PubMed, Science Direct, Web of Science, and Google Scholar were searched for publications that applied chaos theory in EEG analysis of MS patients.

Results

A bibliographic analysis was performed using VOSviewer software keyword co-occurrence analysis indicated that MS was the focus of the research and that research on MS diagnosis has shifted from conventional methods, such as magnetic resonance imaging, to EEG techniques in recent years. A total of 17 studies were included in this review. Among the included articles, nine studies examined resting-state, and eight examined task-based conditions.

Conclusion

Although nonlinear EEG analysis of MS is a relatively novel area of research, the findings have been demonstrated to be informative and effective. The most frequently used nonlinear dynamics analyses were fractal dimension, recurrence quantification analysis, mutual information, and coherence. Each analysis selected provided a unique assessment to fulfill the objective of this review. While considering the limitations discussed, there is a promising path forward using nonlinear analyses with MS data.

A systematic approach to brain dynamics: cognitive evolution theory of consciousness

The brain integrates volition, cognition, and consciousness seamlessly over three hierarchical (scale-dependent) levels of neural activity for their emergence: a causal or 'hard' level, a computational (unconscious) or 'soft' level, and a phenomenal (conscious) or 'psyche' level respectively. The cognitive evolution theory (CET) is based on three general prerequisites: physicalism, dynamism, and emergentism, which entail five consequences about the nature of consciousness: discreteness, passivity, uniqueness, integrity, and graduation. CET starts from the assumption that brains should have primarily evolved as volitional subsystems of organisms, not as prediction machines. This emphasizes the dynamical nature of consciousness in terms of critical dynamics to account for metastability, avalanches, and self-organized criticality of brain processes, then coupling it with volition and cognition in a framework unified over the levels. Consciousness emerges near critical points, and unfolds as a discrete stream of momentary states, each volitionally driven from oldest subcortical arousal systems. The stream is the brain's way of making a difference via predictive (Bayesian) processing. Its objective observables could be complexity measures reflecting levels of consciousness and its dynamical coherency to reveal how much knowledge (information gain) the brain acquires over the stream. CET also proposes a quantitative classification of both disorders of consciousness and mental disorders within that unified framework.

Machine learning approaches and non-linear processing of extracted components in frontal region to predict rTMS treatment response in major depressive disorder

Predicting the therapeutic result of repetitive transcranial magnetic stimulation (rTMS) treatment could save time and costs as ineffective treatment can be avoided. To this end, we presented a machine-learning-based strategy for classifying patients with major depression disorder (MDD) into responders (R) and nonresponders (NR) to rTMS treatment. Resting state EEG data were recorded using 32 electrodes from 88 MDD patients before treatment. Then, patients underwent 7 weeks of rTMS, and 46 of them responded to treatment. By applying Independent Component Analysis (ICA) on EEG, we identified the relevant brain sources as possible indicators of neural activity in the dorsolateral prefrontal cortex (DLPFC). This was served through estimating the generators of activity in the sensor domain. Subsequently, we added physiological information and placed certain terms and conditions to offer a far more realistic estimation than the classic EEG. Ultimately, those components mapped in accordance with the region of the DLPFC in the sensor domain were chosen. Features extracted from the relevant ICs time series included permutation entropy (PE), fractal dimension (FD), Lempel-Ziv Complexity (LZC), power spectral density, correlation dimension (CD), features based on bispectrum, frontal and prefrontal cordance, and a combination of them. The most relevant features were selected by a Genetic Algorithm (GA). For classifying two groups of R and NR, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) were applied to predict rTMS treatment response. To evaluate the performance of classifiers, a 10-fold cross-validation method was employed. A statistical test was used to assess the capability of features in differentiating R and NR for further research. EEG characteristics that can predict rTMS treatment response were discovered. The strongest discriminative indicators were EEG beta power, the sum of bispectrum diagonal elements in delta and beta bands, and CD. The Combined feature vector classified R and NR with a high performance of 94.31% accuracy, 92.85% specificity, 95.65% sensitivity, and 92.85% precision using SVM. This result indicates that our proposed method with power and nonlinear and bispectral features from relevant ICs time-series can predict the treatment outcome of rTMS for MDD patients only by one session pretreatment EEG recording. The obtained results show that the proposed method outperforms previous methods.

Autonomous Swallow Segment Extraction Using Deep Learning in Neck-Sensor Vibratory Signals From Patients With Dysphagia

Dysphagia occurs secondary to a variety of underlying etiologies and can contribute to increased risk of adverse events such as aspiration pneumonia and premature mortality. Dysphagia is primarily diagnosed and characterized by instrumental swallowing exams such as videofluoroscopic swallowing studies. videofluoroscopic swallowing studies involve the inspection of a series of radiographic images for signs of swallowing dysfunction. Though effective, videofluoroscopic swallowing studies are only available in certain clinical settings and are not always desirable or feasible for certain patients. Because of the limitations of current instrumental swallow exams, research studies have explored the use of acceleration signals collected from neck sensors and demonstrated their potential in providing comparable radiation-free diagnostic value as videofluoroscopic swallowing studies. In this study, we used a hybrid deep convolutional recurrent neural network that can perform multi-level feature extraction (localized and across time) to annotate swallow segments automatically via multi-channel swallowing acceleration signals. In total, we used signals and videofluoroscopic swallowing study images of 3144 swallows from 248 patients with suspected dysphagia. Compared to other deep network variants, our network was superior at detecting swallow segments with an average area under the receiver operating characteristic curve value of 0.82 (95% confidence interval: 0.807-0.841), and was in agreement with up to 90% of the gold standard-labeled segments.

EEG Lempel-Ziv complexity varies with sleep stage, but does not seem to track dream experience

In a recent electroencephalography (EEG) sleep study inspired by complexity theories of consciousness, we found that multi-channel signal diversity progressively decreased from wakefulness to slow wave sleep, but failed to find any significant difference between dreaming and non-dreaming awakenings within the same sleep stage (NREM2). However, we did find that multi-channel Lempel-Ziv complexity (LZC) measured over the posterior cortex increased with more perceptual ratings of NREM2 dream experience along a thought-perceptual axis. In this follow-up study, we re-tested our previous findings, using a slightly different approach. Partial sleep-deprivation was followed by evening sleep experiments, with repeated awakenings and immediate dream reports. Participants reported whether they had been dreaming, and were asked to rate how diverse, vivid, perceptual, and thought-like the contents of their dreams were. High density (64 channel) EEG was recorded throughout the experiment, and mean single-channel LZC was calculated for each 30 s sleep epoch. LZC progressively decreased with depth of non-REM sleep. Surprisingly, estimated marginal mean LZC was slightly higher for NREM1 than for wakefulness, but the difference did not remain significant after adjusting for multiple comparisons. We found no significant difference in LZC between dream and non-dream awakenings, nor any significant relationship between LZC and subjective ratings of dream experience, within the same sleep stage (NREM2). The failure to reproduce our own previous finding of a positive correlation between posterior LZC and more perceptual dream experiences, or to find any other correlation between brain signal complexity and subjective experience within NREM2 sleep, raises the question of whether EEG LZC is really a reliable correlate of richness of experience as such, within the same sleep stage.

Application of Soft-Clustering to Assess Consciousness in a CLIS Patient

Completely locked-in (CLIS) patients are characterized by sufficiently intact cognitive functions, but a complete paralysis that prevents them to interact with their surroundings. On one hand, studies have shown that the ability to communicate plays an important part in these patients' quality of life and prognosis. On the other hand, brain-computer interfaces (BCIs) provide a means for them to communicate using their brain signals. However, one major problem for such patients is the difficulty to determine if they are conscious or not at a specific time. This work aims to combine different sets of features consisting of spectral, complexity and connectivity measures, to increase the probability of correctly estimating CLIS patients' consciousness levels. The proposed approach was tested on data from one CLIS patient, which is particular in the sense that the experimenter was able to point out one time frame Δt during which he was undoubtedly conscious. Results showed that the method presented in this paper was able to detect increases and decreases of the patient's consciousness levels. More specifically, increases were observed during this Δt, corroborating the assertion of the experimenter reporting that the patient was definitely conscious then. Assessing the patients' consciousness is intended as a step prior attempting to communicate with them, in order to maximize the efficiency of BCI-based communication systems.

A Neural Network Framework for Validating Information-Theoretics Parameters in the Applications of Acoustic Emission Technique for Mechanical Characterization of Materials

A multiparameter approach is preferred while utilizing Acoustic Emission (AE) technique for mechanical characterization of composite materials. It is essential to utilize a statistical parameter, which is independent of the sensor characteristics, for this purpose. Thus, a new information-theoretics parameter, Lempel-Ziv (LZ) complexity, is used in this research work for mechanical characterization of Carbon Fibre Reinforced Plastic (CFRP) composites. CFRP specimens in plain weave fabric configurations were tested and the acoustic activity during the loading was recorded. The AE signals were classified based on their peak amplitudes, counts, and LZ complexity indices using k-means++ data clustering algorithm. The clustered data were compared with the mechanical results of the tensile tests on CFRP specimens. The results show that the clustered data are capable of identifying critical regions of failure. The LZ complexity indices of the AE signal can be used as an AE descriptor for mechanical characterization. This is validated by studying the clustered signals in their time-frequency domain using wavelet transform. Finally, a neural network framework based on SqueezeNet was trained using the wavelet scalograms for a quantitative validation of the data clustering approach proposed in this research work. The results show that the proposed method functions at an efficiency of more than 85% for three out of four clustered data. This validates the application of LZ complexity as an AE descriptor for AE signal data analysis.

Associations Between Wearable-Specific Indicators of Physical Activity Behaviour and Insulin Sensitivity and Glycated Haemoglobin in the General Population: Results from the ORISCAV-LUX 2 Study

BACKGROUND

Parameters derived from an acceleration signal, such as the time accumulated in sedentary behaviour or moderate to vigorous physical activity (MVPA), may not be sufficient to describe physical activity (PA) which is a complex behaviour. Incorporating more advanced wearable-specific indicators of PA behaviour (WIPAB) may be useful when characterising PA profiles and investigating associations with health. We investigated the associations of novel objective measures of PA behaviour with glycated haemoglobin (HbA1c) and insulin sensitivity (Quicki index).

METHODS

This observational study included 1026 adults (55% women) aged 18-79y who were recruited from the general population in Luxembourg. Participants provided ≥ 4 valid days of triaxial accelerometry data which was used to derive WIPAB variables related to the activity intensity, accumulation pattern and the temporal correlation and regularity of the acceleration time series.

RESULTS

Adjusted general linear models showed that more time spent in MVPA and a higher average acceleration were both associated with a higher insulin sensitivity. More time accumulated in sedentary behaviour was associated with lower insulin sensitivity. With regard to WIPAB variables, parameters that were indicative of higher PA intensity, including a shallower intensity gradient and higher average accelerations registered during the most active 8 h and 15 min of the day, were associated with higher insulin sensitivity. Results for the power law exponent alpha, and the proportion of daily time accumulated in sedentary bouts > 60 min, indicated that activity which was characterised by long sedentary bouts was associated with lower insulin sensitivity. A greater proportion of time spent in MVPA bouts > 10 min was associated with higher insulin sensitivity. A higher scaling exponent alpha at small time scales (< 90 min), which shows greater correlation in the acceleration time series over short durations, was associated with higher insulin sensitivity. When measured over the entirety of the time series, metrics that reflected a more complex, irregular and unpredictable activity profile, such as the sample entropy, were associated with lower HbA1c levels and higher insulin sensitivity.

CONCLUSION

Our investigation of novel WIPAB variables shows that parameters related to activity intensity, accumulation pattern, temporal correlation and regularity are associated with insulin sensitivity in an adult general population.

From the origins to the stream of consciousness and its neural correlates

There are now dozens of very different theories of consciousness, each somehow contributing to our understanding of its nature. The science of consciousness needs therefore not new theories but a general framework integrating insights from those, yet not making it a still-born "Frankenstein" theory. First, the framework must operate explicitly on the stream of consciousness, not on its static description. Second, this dynamical account must also be put on the evolutionary timeline to explain the origins of consciousness. The Cognitive Evolution Theory (CET), outlined here, proposes such a framework. This starts with the assumption that brains have primarily evolved as volitional subsystems of organisms, inherited from primitive (fast and random) reflexes of simplest neural networks, only then resembling error-minimizing prediction machines. CET adopts the tools of critical dynamics to account for metastability, scale-free avalanches, and self-organization which are all intrinsic to brain dynamics. This formalizes the stream of consciousness as a discrete (transitive, irreflexive) chain of momentary states derived from critical brain dynamics at points of phase transitions and mapped then onto a state space as neural correlates of a particular conscious state. The continuous/discrete dichotomy appears naturally between the brain dynamics at the causal level and conscious states at the phenomenal level, each volitionally triggered from arousal centers of the brainstem and cognitively modulated by thalamocortical systems. Their objective observables can be entropy-based complexity measures, reflecting the transient level or quantity of consciousness at that moment.

Resting-State EEG in Alpha Rhythm May Be Indicative of the Performance of Motor Imagery-Based Brain-Computer Interface

Motor imagery-based brain-computer interfaces (MI-BCIs) have great application prospects in motor enhancement and rehabilitation. However, the capacity to control a MI-BCI varies among persons. Predicting the MI ability of a user remains challenging in BCI studies. We first calculated the relative power level (RPL), power spectral entropy (PSE) and Lempel-Ziv complexity (LZC) of the resting-state open and closed-eye EEG of different frequency bands and investigated their correlations with the upper and lower limbs MI performance (left hand, right hand, both hands and feet MI tasks) on as many as 105 subjects. Then, the most significant related features were used to construct a classifier to separate the high MI performance group from the low MI performance group. The results showed that the features of open-eye resting alpha-band EEG had the strongest significant correlations with MI performance. The PSE performed the best among all features for the screening of the MI performance, with the classification accuracy of 85.24%. These findings demonstrated that the alpha bands might offer information related to the user's MI ability, which could be used to explore more effective and general neural markers to screen subjects and design individual MI training strategies.

Brain signal complexity in adults with Down syndrome: Potential application in the detection of mild cognitive impairment

Background

Down syndrome (DS) is considered the most frequent cause of early-onset Alzheimer’s disease (AD), and the typical pathophysiological signs are present in almost all individuals with DS by the age of 40. Despite of this evidence, the investigation on the pre-dementia stages in DS is scarce. In the present study we analyzed the complexity of brain oscillatory patterns and neuropsychological performance for the characterization of mild cognitive impairment (MCI) in DS.

Materials and methods

Lempel-Ziv complexity (LZC) values from resting-state magnetoencephalography recordings and the neuropsychological performance in 28 patients with DS [control DS group (CN-DS) (n = 14), MCI group (MCI-DS) (n = 14)] and 14 individuals with typical neurodevelopment (CN-no-DS) were analyzed.

Results

Lempel-Ziv complexity was lowest in the frontal region within the MCI-DS group, while the CN-DS group showed reduced values in parietal areas when compared with the CN-no-DS group. Also, the CN-no-DS group exhibited the expected pattern of significant increase of LZC as a function of age, while MCI-DS cases showed a decrease. The combination of reduced LZC values and a divergent trajectory of complexity evolution with age, allowed the discrimination of CN-DS vs. MCI-DS patients with a 92.9% of sensitivity and 85.7% of specificity. Finally, a pattern of mnestic and praxic impairment was significantly associated in MCI-DS cases with the significant reduction of LZC values in frontal and parietal regions (p = 0.01).

Conclusion

Brain signal complexity measured with LZC is reduced in DS and its development with age is also disrupted. The combination of both features might assist in the detection of MCI within this population.

Brain Complexities and Anesthesia: Their Meaning and Measurement

A complex system is often associated with emergence of new phenomena from the interactions between the system's components. General anesthesia reduces brain complexity and so inhibits the emergence of consciousness. An understanding of complexity is necessary for the interpretation of brain monitoring algorithms. Complexity indices capture the "difficulty" of understanding brain activity over time and/or space. Complexity-entropy plots reveal the types of complexity indices and their balance of randomness and structure. Lempel-Ziv complexity is a common index of temporal complexity for single-channel electroencephalogram containing both power spectral and nonlinear effects, revealed by phase-randomized surrogate data. Computing spatial complexities involves forming a connectivity matrix and calculating the complexity of connectivity patterns. Spatiotemporal complexity can be estimated in multiple ways including temporal or spatial concatenation, estimation of state switching, or integrated information. This article illustrates the concept and application of various complexities by providing working examples; a website with interactive demonstrations has also been created.

Investigation of Interlaminar Shear Properties of CFRP Composites at Elevated Temperatures Using the Lempel-Ziv Complexity of Acoustic Emission Signals

Three-point bending tests on Short Beam Shear (SBS) specimens are performed to investigate the interlaminar shear properties of plain weave fabric CFRP composites. The tests are performed in a controlled environmental chamber at two different elevated temperatures. The interlaminar shear properties of the specimens remain largely unaffected by the testing temperature. However, the SEM micrographs show different damage progressions between the specimens tested at 100 °C and 120 °C. Fibre ruptures and longer delamination between the plies, as a result of a high temperature, are observed in the specimens tested at 120 °C, which are not observed in the specimens tested at 100 °C. In addition, the acoustic emission activities during the tests are investigated by using piezoelectric sensors. The information-theoretic parameter, the Lempel-Ziv (LZ) complexity, is calculated for the recorded acoustic signals. The LZ Complexities are used for identifying the occurrence of the first delamination failure in the specimens. Additionally, the two features of the acoustic signals, LZ complexity and Weighted Peak Frequency (W.P-Freq), are used for distinguishing the different damage sources in the CFRP specimens. The results are well-supported by the time-frequency analysis of the acoustic signals using a Continuous Wavelet Transform (CWT).

Functional Connectivity and Complexity in the Phenomenological Model of Mild Cognitive-Impaired Alzheimer's Disease

Background

Functional connectivity and complexity analysis has been discretely studied to understand intricate brain dynamics. The current study investigates the interplay between functional connectivity and complexity using the Kuramoto mean-field model.

Method

Functional connectivity matrices are estimated using the weighted phase lag index and complexity measures through popularly used complexity estimators such as Lempel-Ziv complexity (LZC), Higuchi's fractal dimension (HFD), and fluctuation-based dispersion entropy (FDispEn). Complexity measures are estimated on real and simulated electroencephalogram (EEG) signals of patients with mild cognitive-impaired Alzheimer's disease (MCI-AD) and controls. Complexity measures are further applied to simulated signals generated from lesion-induced connectivity matrix and studied its impact. It is a novel attempt to study the relation between functional connectivity and complexity using a neurocomputational model.

Results

Real EEG signals from patients with MCI-AD exhibited reduced functional connectivity and complexity in anterior and central regions. A simulation study has also displayed significantly reduced regional complexity in the patient group with respect to control. A similar reduction in complexity was further evident in simulation studies with lesion-induced control groups compared with non-lesion-induced control groups.

Conclusion

Taken together, simulation studies demonstrate a positive influence of reduced connectivity in the model imparting a reduced complexity in the EEG signal. The study revealed the presence of a direct relation between functional connectivity and complexity with reduced connectivity, yielding a decreased EEG complexity.

Cortical Complexity and Connectivity during Isoflurane-induced General Anesthesia: A Rat Study

OBJECTIVE

The investigation of neurophysiologic mechanisms of anesthetic drug-induced loss of consciousness (LOC) by using the entropy, complexity, and information integration theories at the mesoscopic level has been a hot topic in recent years. However, systematic research is still lacking.

APPROACH

We analyzed electrocorticography (ECoG) data recorded from nine rats during isoflurane-induced unconsciousness. To characterize the complexity and connectivity changes, we investigated ECoG power, symbolic dynamic-based entropy (i.e., permutation entropy (PE)), complexity (i.e., permutation Lempel-Ziv complexity (PLZC)), information integration (i.e., permutation cross mutual information (PCMI)), and PCMI-based cortical brain networks in the frontal, parietal, and occipital cortical regions.

MAIN RESULTS

Firstly, LOC was accompanied by a raised power in the ECoG beta (12-30 Hz) but a decreased power in the high gamma (55-95 Hz) frequency band in all three brain regions. Secondly, PE and PLZC showed similar change trends in the lower frequency band (0.1-45 Hz), declining after LOC (p<0.05) and increasing after recovery of consciousness (p<0.001). Thirdly, intra-frontal and inter-frontal-parietal PCMI declined after LOC, in both lower (0.1-45Hz) and higher frequency bands (55-95Hz) (p<0.001). Finally, the local network parameters of the nodal clustering coefficient and nodal efficiency in the frontal region decreased after LOC, in both the lower and higher frequency bands (p<0.05). Moreover, global network parameters of the normalized average clustering coefficient and small world index increased slightly after LOC in the lower frequency band. However, this increase was not statistically significant.

SIGNIFICANCE

The PE, PLZC, PCMI and PCMI-based brain networks are effective metrics for qualifying the effects of isoflurane.

Beyond randomness: Evaluating measures of information entropy in binary series

The enormous amount of currently available data demands efforts to extract meaningful information. For this purpose, different measurements are applied, including Shannon's entropy, permutation entropy, and the Lempel-Ziv complexity. These methods have been used in many applications, such as pattern recognition, series classification, and several other areas (e.g., physical, financial, and biomedical). Data in these applications are often presented in binary series with temporal correlations. Herein, we compare the measures of information entropy in binary series conveying short- and long-range temporal correlations characterized by the Hurst exponent H. Combining numerical and analytical approaches, we scrutinize different methods that were not efficient in detecting temporal correlations. To surpass this limitation, we propose a measure called the binary permutation index (BPI). We will demonstrate that BPI efficiently discriminates patterns embedded in the series, offering advantages over previous methods. Subsequently, we collect stock market time series and rain precipitation data as well as perform in vivo electrophysiological recordings in the hippocampus of an experimental animal model of temporal lobe epilepsy, in which the BPI application in both public open source and experimental data is demonstrated. An index is proposed to evaluate information entropy, allowing the ability to discriminate randomness and extract meaningful information in binary time series.

Improving Non-Invasive Aspiration Detection With Auxiliary Classifier Wasserstein Generative Adversarial Networks

Aspiration is a serious complication of swallowing disorders. Adequate detection of aspiration is essential in dysphagia management and treatment. High-resolution cervical auscultation has been increasingly considered as a promising noninvasive swallowing screening tool and has inspired automatic diagnosis with advanced algorithms. The performance of such algorithms relies heavily on the amount of training data. However, the practical collection of cervical auscultation signal is an expensive and time-consuming process because of the clinical settings and trained experts needed for acquisition and interpretations. Furthermore, the relatively infrequent incidence of severe airway invasion during swallowing studies constrains the performance of machine learning models. Here, we produced supplementary training exemplars for desired class by capturing the underlying distribution of original cervical auscultation signal features using auxiliary classifier Wasserstein generative adversarial networks. A 10-fold subject cross-validation was conducted on 2079 sets of 36-dimensional signal features collected from 189 patients undergoing swallowing examinations. The proposed data augmentation outperforms basic data sampling, cost-sensitive learning and other generative models with significant enhancement. This demonstrates the remarkable potential of proposed network in improving classification performance using cervical auscultation signals and paves the way of developing accurate noninvasive swallowing evaluation in dysphagia care.

From temporal to spatial topography: hierarchy of neural dynamics in higher- and lower-order networks shapes their complexity

The brain shows a topographical hierarchy along the lines of lower- and higher-order networks. The exact temporal dynamics characterization of this lower-higher-order topography at rest and its impact on task states remains unclear, though. Using 2 functional magnetic resonance imaging data sets, we investigate lower- and higher-order networks in terms of the signal compressibility, operationalized by Lempel-Ziv complexity (LZC). As we assume that this degree of complexity is related to the slow-fast frequency balance, we also compute the median frequency (MF), an estimation of frequency distribution. We demonstrate (i) topographical differences at rest between higher- and lower-order networks, showing lower LZC and MF in the former; (ii) task-related and task-specific changes in LZC and MF in both lower- and higher-order networks; (iii) hierarchical relationship between LZC and MF, as MF at rest correlates with LZC rest-task change along the lines of lower- and higher-order networks; and (iv) causal and nonlinear relation between LZC at rest and LZC during task, with MF at rest acting as mediator. Together, results show that the topographical hierarchy of lower- and higher-order networks converges with their temporal hierarchy, with these neural dynamics at rest shaping their range of complexity during task states in a nonlinear way.

Applying nonlinear measures to the brain rhythms: an effective method for epilepsy diagnosis

Background

Epilepsy is a neurological disorder from which almost 50 million people have been suffering. These statistics indicate the importance of epilepsy diagnosis. Electroencephalogram (EEG) signals analysis is one of the most common methods for epilepsy characterization; hence, various strategies were applied to classify epileptic EEGs.

Methods

In this paper, four different nonlinear features such as Fractal dimensions including Higuchi method (HFD) and Katz method (KFD), Hurst exponent, and L-Z complexity measure were extracted from EEGs and their frequency sub-bands. The features were ranked later by implementing Relieff algorithm. The ranked features were applied sequentially to three different classifiers (MLPNN, Linear SVM, and RBF SVM).

Results

According to the dataset used for this study, there are five classification problems named ABCD/E, AB/CD/E, A/D/E, A/E, and D/E. In all cases, MLPNN was the most accurate classifier. Its performances for mentioned classification problems were 99.91%, 98.19%, 98.5%, 100% and 99.84%, respectively.

Conclusion

The results demonstrate that KFD is the highest-ranking feature; In addition, beta and theta sub-bands are the most important frequency bands because, for all cases, the top features were KFDs extracted from beta and theta sub-bands. Moreover, high levels of accuracy have been obtained just by using these two features which reduce the complexity of the classification.

The Impact of the COVID-19 Pandemic on the Unpredictable Dynamics of the Cryptocurrency Market

The global economy is under great shock again in 2020 due to the COVID-19 pandemic; it has not been long since the global financial crisis in 2008. Therefore, we investigate the evolution of the complexity of the cryptocurrency market and analyze the characteristics from the past bull market in 2017 to the present the COVID-19 pandemic. To confirm the evolutionary complexity of the cryptocurrency market, three general complexity analyses based on nonlinear measures were used: approximate entropy (ApEn), sample entropy (SampEn), and Lempel-Ziv complexity (LZ). We analyzed the market complexity/unpredictability for 43 cryptocurrency prices that have been trading until recently. In addition, three non-parametric tests suitable for non-normal distribution comparison were used to cross-check quantitatively. Finally, using the sliding time window analysis, we observed the change in the complexity of the cryptocurrency market according to events such as the COVID-19 pandemic and vaccination. This study is the first to confirm the complexity/unpredictability of the cryptocurrency market from the bull market to the COVID-19 pandemic outbreak. We find that ApEn, SampEn, and LZ complexity metrics of all markets could not generalize the COVID-19 effect of the complexity due to different patterns. However, market unpredictability is increasing by the ongoing health crisis.

Robust EEG Based Biomarkers to Detect Alzheimer's Disease

Biomarkers to detect Alzheimer's disease (AD) would enable patients to gain access to appropriate services and may facilitate the development of new therapies. Given the large numbers of people affected by AD, there is a need for a low-cost, easy to use method to detect AD patients. Potentially, the electroencephalogram (EEG) can play a valuable role in this, but at present no single EEG biomarker is robust enough for use in practice. This study aims to provide a methodological framework for the development of robust EEG biomarkers to detect AD with a clinically acceptable performance by exploiting the combined strengths of key biomarkers. A large number of existing and novel EEG biomarkers associated with slowing of EEG, reduction in EEG complexity and decrease in EEG connectivity were investigated. Support vector machine and linear discriminate analysis methods were used to find the best combination of the EEG biomarkers to detect AD with significant performance. A total of 325,567 EEG biomarkers were investigated, and a panel of six biomarkers was identified and used to create a diagnostic model with high performance (≥85% for sensitivity and 100% for specificity).

Atypical neural processing during the execution of complex sensorimotor behavior in autism

Stereotyped behavior is rhythmic, repetitive movement that is essentially invariant in form. Stereotypy is common in several clinical disorders, such as autism spectrum disorders (ASD), where it is considered maladaptive. However, it also occurs early in typical development (TD) where it is hypothesized to serve as the foundation on which complex, adaptive motor behavior develops. This transition from stereotyped to complex movement in TD is thought to be supported by sensorimotor integration. Stereotypy in clinical disorders may persist due to deficits in sensorimotor integration. The present study assessed whether differences in sensorimotor processing may limit the expression of complex motor behavior in individuals with ASD and contribute to the clinical stereotypy observed in this population. Adult participants with ASD and TD performed a computer-based stimulus-tracking task in the presence and absence of visual feedback. Electroencephalography was recorded during the task. Groups were compared on motor performance (root mean square error), motor complexity (sample entropy), and neural complexity (multiscale sample entropy of the electroencephalography signal) in the presence and absence of visual feedback. No group differences were found for motor performance or motor complexity. The ASD group demonstrated greater neural complexity and greater differences between feedback conditions than TD individuals, specifically in signals relevant to sensorimotor processing. Motor performance and motor complexity correlated with clinical stereotypy in the ASD group. These findings support the hypothesis that individuals with ASD have differences in sensorimotor processing when executing complex motor behavior and that stereotypy is associated with low motor complexity.

Multiscale Permutation Lempel-Ziv Complexity Measure for Biomedical Signal Analysis: Interpretation and Application to Focal EEG Signals

This paper analyses the complexity of electroencephalogram (EEG) signals in different temporal scales for the analysis and classification of focal and non-focal EEG signals. Futures from an original multiscale permutation Lempel–Ziv complexity measure (MPLZC) were obtained. MPLZC measure combines a multiscale structure, ordinal analysis, and permutation Lempel–Ziv complexity for quantifying the dynamic changes of an electroencephalogram (EEG). We also show the dependency of MPLZC on several straight-forward signal processing concepts, which appear in biomedical EEG activity via a set of synthetic signals. The main material of the study consists of EEG signals, which were obtained from the Bern-Barcelona EEG database. The signals were divided into two groups: focal EEG signals (n = 100) and non-focal EEG signals (n = 100); statistical analysis was performed by means of non-parametric Mann–Whitney test. The mean value of MPLZC results in the non-focal group are significantly higher than those in the focal group for scales above 1 (p < 0.05). The result indicates that the non-focal EEG signals are more complex. MPLZC feature sets are used for the least squares support vector machine (LS-SVM) classifier to classify into the focal and non-focal EEG signals. Our experimental results confirmed the usefulness of the MPLZC method for distinguishing focal and non-focal EEG signals with a classification accuracy of 86%.

Brain activity complexity has a nonlinear relation to the level of propofol sedation

BACKGROUND

Brain activity complexity is a promising correlate of states of consciousness. Previous studies have shown higher complexity for awake compared with deep anaesthesia states. However, little attention has been paid to complexity in intermediate states of sedation.

METHODS

We analysed the Lempel-Ziv complexity of EEG signals from subjects undergoing moderate propofol sedation, from an open access database, and related it to behavioural performance as a continuous marker of the level of sedation and to plasma propofol concentrations. We explored its relation to spectral properties, to propofol susceptibility, and its topographical distribution.

RESULTS

Subjects who retained behavioural performance despite propofol sedation showed increased brain activity complexity compared with baseline (M=13.9%, 95% confidence interval=7.5-20.3). This was not the case for subjects who lost behavioural performance. The increase was most prominent in frontal electrodes, and correlated with behavioural performance and propofol susceptibility. This effect was positively correlated with high-frequency activity. However, abolishing specific frequency ranges (e.g. alpha or gamma) did not reduce the propofol-induced increase in Lempel-Ziv complexity.

CONCLUSIONS

Brain activity complexity can increase in response to propofol, particularly during low-dose sedation. Propofol-mediated Lempel-Ziv complexity increase was independent of frequency-specific spectral power manipulations, and most prominent in frontal areas. Taken together, these results advance our understanding of brain activity complexity and anaesthetics. They do not support models of consciousness that propose a direct relation between brain activity complexity and states of consciousness.

Complex networks and deep learning for EEG signal analysis

Electroencephalogram (EEG) signals acquired from brain can provide an effective representation of the human's physiological and pathological states. Up to now, much work has been conducted to study and analyze the EEG signals, aiming at spying the current states or the evolution characteristics of the complex brain system. Considering the complex interactions between different structural and functional brain regions, brain network has received a lot of attention and has made great progress in brain mechanism research. In addition, characterized by autonomous, multi-layer and diversified feature extraction, deep learning has provided an effective and feasible solution for solving complex classification problems in many fields, including brain state research. Both of them show strong ability in EEG signal analysis, but the combination of these two theories to solve the difficult classification problems based on EEG signals is still in its infancy. We here review the application of these two theories in EEG signal research, mainly involving brain-computer interface, neurological disorders and cognitive analysis. Furthermore, we also develop a framework combining recurrence plots and convolutional neural network to achieve fatigue driving recognition. The results demonstrate that complex networks and deep learning can effectively implement functional complementarity for better feature extraction and classification, especially in EEG signal analysis.

Overcoming Rest-Task Divide-Abnormal Temporospatial Dynamics and Its Cognition in Schizophrenia

Schizophrenia is a complex psychiatric disorder exhibiting alterations in spontaneous and task-related cerebral activity whose relation (termed "state dependence") remains unclear. For unraveling their relationship, we review recent electroencephalographic (and a few functional magnetic resonance imaging) studies in schizophrenia that assess and compare both rest/prestimulus and task states, ie, rest/prestimulus-task modulation. Results report reduced neural differentiation of task-related activity from rest/prestimulus activity across different regions, neural measures, cognitive domains, and imaging modalities. Together, the findings show reduced rest/prestimulus-task modulation, which is mediated by abnormal temporospatial dynamics of the spontaneous activity. Abnormal temporospatial dynamics, in turn, may lead to abnormal prediction, ie, predictive coding, which mediates cognitive changes and psychopathological symptoms, including confusion of internally and externally oriented cognition. In conclusion, reduced rest/prestimulus-task modulation in schizophrenia provides novel insight into the neuronal mechanisms that connect task-related changes to cognitive abnormalities and psychopathological symptoms.