Detrended Fluctuation, Coherence, and Spectral Power Analysis of Activation Rearrangement in EEG Dynamics During Cognitive Workload

Effects of metronome walking on long-term attractor divergence and correlation structure of gait: a validation study in older people

This study investigates the effects of metronome walking on gait dynamics in older adults, focusing on long-range correlation structures and long-range attractor divergence (assessed by maximum Lyapunov exponents). Sixty older adults participated in indoor walking tests with and without metronome cues. Gait parameters were recorded using two triaxial accelerometers attached to the lumbar region and to the foot. We analyzed logarithmic divergence of lumbar acceleration using Rosenstein's algorithm and scaling exponents for stride intervals from foot accelerometers using detrended fluctuation analysis (DFA). Results indicated a concomitant reduction in long-term divergence exponents and scaling exponents during metronome walking, while short-term divergence remained largely unchanged. Furthermore, long-term divergence exponents and scaling exponents were significantly correlated. Reliability analysis revealed moderate intrasession consistency for long-term divergence exponents, but poor reliability for scaling exponents. Our results suggest that long-term divergence exponents could effectively replace scaling exponents for unsupervised gait quality assessment in older adults. This approach may improve the assessment of attentional involvement in gait control and enhance fall risk assessment.

Evaluation of EEG Signals by Spectral Peak Methods and Statistical Correlation for Mental State Discrimination Induced by Arithmetic Tasks

Bringing out brain activity through the interpretation of EEG signals is a challenging problem that involves combined methods of signal analysis. The issue of classifying mental states induced by arithmetic tasks can be solved through various classification methods, using diverse characteristic parameters of EEG signals in the time, frequency, and statistical domains. This paper explores the results of an experiment that aimed to highlight arithmetic mental tasks contained in the PhysioNet database, performed on a group of 36 subjects. The majority of publications on this topic deal with machine learning (ML)-based classification methods with supervised learning support vector machine (SVM) algorithms, K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Decision Trees (DTs). Also, there are frequent approaches based on the analysis of EEG data as time series and their classification with Recurrent Neural Networks (RNNs), as well as with improved algorithms such as Long Short-Term Memory (LSTM), Bidirectional Long Short-Term Memory (BLSTM), and Gated Recurrent Units (GRUs). In the present work, we evaluate the classification method based on the comparison of domain limits for two specific characteristics of EEG signals: the statistical correlation of pairs of signals and the size of the spectral peak detected in theta, alpha, and beta bands. This study provides some interpretations regarding the electrical activity of the brain, consolidating and complementing the results of similar research. The classification method used is simple and easy to apply and interpret. The analysis of EEG data showed that the theta and beta frequency bands were the only discriminators between the relaxation and arithmetic calculation states. Notably, the F7 signal, which used the spectral peak criterion, achieved the best classification accuracy (100%) in both theta and beta bands for the subjects with the best results in performing calculations. Also, our study found the Fz signal to be a good sensor in the theta band for mental task discrimination for all subjects in the group with 90% accuracy.

Electroencephalogram-based objective assessment of cognitive function level associated with age-related hearing loss

Age-Related Hearing Loss (ARHL) is a common problem in aging. Numerous longitudinal cohort studies have revealed that ARHL is closely related to cognitive function, leading to a significant risk of cognitive decline and dementia. This risk gradually increases with the severity of hearing loss. We designed dual auditory Oddball and cognitive task paradigms for the ARHL subjects, then obtained the Montreal Cognitive Assessment (MoCA) scale evaluation results for all the subjects. Multi-dimensional EEG characteristics helped explore potential biomarkers to evaluate the cognitive level of the ARHL group, having a significantly lower P300 peak amplitude coupled with a prolonged latency. Moreover, visual memory, auditory memory, and logical calculation were investigated during the cognitive task paradigm. In the ARHL groups, the alpha-to-beta rhythm energy ratio in the visual and auditory memory retention period and the wavelet packet entropy value within the logical calculation period were significantly reduced. Correlation analysis between the above specificity indicators and the subjective scale results of the ARHL group revealed that the auditory P300 component characteristics could assess attention resources and information processing speed. The alpha and beta rhythm energy ratio and wavelet packet entropy can become potential indicators to determine working memory and logical cognitive computation-related cognitive ability.

Predicting Tacit Coordination Success Using Electroencephalogram Trajectories: The Impact of Task Difficulty

In this study, we aim to develop a machine learning model to predict the level of coordination between two players in tacit coordination games by analyzing the similarity of their spatial EEG features. We present an analysis, demonstrating the model's sensitivity, which was assessed through three conventional measures (precision, recall, and f1 score) based on the EEG patterns. These measures are evaluated in relation to the coordination task difficulty, as determined by the coordination index (CI). Tacit coordination games are games in which two individuals are requested to select the same option out of a closed set without the ability to communicate. This study aims to examine the effect of the difficulty of a semantic coordination task on the ability to predict a successful coordination between two players based on the compatibility between their EEG signals. The difficulty of each of the coordination tasks was estimated based on the degree of dispersion of the different answers given by the players reflected by the CI. The classification of the spatial distance between each pair of individual brain patterns, analyzed using the random walk algorithm, was used to predict whether successful coordination occurred or not. The classification performance was obtained for each game individually, i.e., for each different complexity level, via recall and precision indices. The results showed that the classifier performance depended on the CI, that is, on the level of coordination difficulty. These results, along with possibilities for future research, are discussed.

Increased brain microvascular hemoglobin concentrations in children with cerebral malaria

Brain swelling is associated with death from cerebral malaria, but it is unclear whether brain swelling is caused by cerebral edema or vascular congestion-two pathological conditions with distinct effects on tissue hemoglobin concentrations. We used near-infrared spectroscopy (NIRS) to noninvasively study cerebral microvascular hemoglobin concentrations in 46 Malawian children with cerebral malaria. Cerebral malaria was defined by the presence of the malaria parasite Plasmodium falciparum on a blood smear, a Blantyre coma score of 2 or less, and retinopathy. Children with uncomplicated malaria (n = 33) and healthy children (n = 29) were enrolled as comparators. Cerebral microvascular hemoglobin concentrations were higher among children with cerebral malaria compared with those with uncomplicated malaria [median (25th, 75th): 145.2 (95.2, 190.0) μM versus 82.9 (65.7, 105.4) μM, P = 0.008]. Cerebral microvascular hemoglobin concentrations correlated with brain swelling score determined by MRI (r = 0.37, P = 0.03). Fluctuations in cerebral microvascular hemoglobin concentrations over a 30-min time period were characterized using detrended fluctuation analysis (DFA). DFA determined self-similarity of the cerebral microvascular hemoglobin concentration signal to be lower among children with cerebral malaria compared with those with uncomplicated malaria [0.63 (0.54, 0.70) versus 0.91 (0.82, 0.94), P < 0.0001]. The lower self-similarity of the hemoglobin concentration signal in children with cerebral malaria suggested impaired regulation of cerebral blood flow. The elevated cerebral tissue hemoglobin concentration and its correlation with brain swelling suggested that excess blood volume, potentially due to vascular congestion, may contribute to brain swelling in cerebral malaria.

Heart-brain Rhythmic Synchronization during Meditation: A Nonlinear Signal Analysis

Background

Heart-brain synchronization is the integration of mind, body, and spirit. It occurs when the electrical activity of the heart and brain is synchronized. In recent years, there has been mounting curiosity to investigate the effects of meditation on heart-brain synchronization with respect to mental and emotional health and well-being. The current investigation aims to explore the rhythmic synchronicity between the brain and the heart during heartfulness meditation (HM) practice.

Materials and Methods

The study was performed on 45 healthy volunteers who were categorized into three equal groups: long-term meditators (LTMs), short-term meditators (STMs), and nonmeditators (NMs). The electroencephalogram (EEG) signals were recorded to measure the prefrontal activity, and electrocardiogram (ECG) signals were recorded to measure the cardiac activity. The data were recorded in four states: baseline, meditation, transmission, and posttransmission. The detrended fluctuation analysis (DFA) method was used for the analysis of EEG and ECG signals.

Results

The result indicates that DFA values of EEG and ECG declined during meditation and transmission states as compared to pre- and postmeditation states. Significant results were obtained for the LTM group in all the states. A positive correlation was also observed between DFA of the heart and brain for the LTM group and no significant correlations were observed for the STM and NM groups.

Conclusion

The shreds of evidence suggest that heart-brain synchronization facilitates mental and emotional stability. HM practice has the potential to regulate the fluctuation of the mind. Regular meditation practice may result in physiological synchrony between cardiac and neural behavior, which can be considered a quality index for meditation practice.

Detrending Moving Average, Power Spectral Density, and Coherence: Three EEG-Based Methods to Assess Emotion Irradiation during Facial Perception

Understanding brain reactions to facial expressions can help in explaining emotion-processing and memory mechanisms. The purpose of this research is to examine the dynamics of electrical brain activity caused by visual emotional stimuli. The focus is on detecting changes in cognitive mechanisms produced by negative, positive, and neutral expressions on human faces. Three methods were used to study brain reactions: power spectral density, detrending moving average (DMA), and coherence analysis. Using electroencephalogram (EEG) recordings from 48 subjects while presenting facial image stimuli from the International Affective Picture System, the topographic representation of the evoked responses was acquired and evaluated to disclose the specific EEG-based activity patterns in the cortex. The theta and beta systems are two key cognitive systems of the brain that are activated differently on the basis of gender. The obtained results also demonstrate that the DMA method can provide information about the cortical networks’ functioning stability, so it can be coupled with more prevalent methods of EEG analysis.

EEG Pattern Classification of Picking and Coordination Using Anonymous Random Walks

Tacit coordination games are games where players are trying to select the same solution without any communication between them. Various theories have attempted to predict behavior in tacit coordination games. Until now, research combining tacit coordination games with electrophysiological measures was mainly based on spectral analysis. In contrast, EEG coherence enables the examination of functional and morphological connections between brain regions. Hence, we aimed to differentiate between different cognitive conditions using coherence patterns. Specifically, we have designed a method that predicts the class label of coherence graph patterns extracted out of multi-channel EEG epochs taken from three conditions: a no-task condition and two cognitive tasks, picking and coordination. The classification process was based on a coherence graph extracted out of the EEG record. To assign each graph into its appropriate label, we have constructed a hierarchical classifier. First, we have distinguished between the resting-state condition and the other two cognitive tasks by using a bag of node degrees. Next, to distinguish between the two cognitive tasks, we have implemented an anonymous random walk. Our classification model achieved a total accuracy value of 96.55%.

An EEG-Based Neuromarketing Approach for Analyzing the Preference of an Electric Car

This study evaluates consumer preference from the perspective of neuroscience when a choice is made among a number of cars, one of which is an electric car. Consumer neuroscience contributes to a systematic understanding of the underlying information processing and cognitions involved in choosing or preferring a product. This study aims to evaluate whether neural measures, which were implicitly extracted from brain activities, can be reliable or consistent with self-reported measures such as preference or liking. In an EEG-based experiment, the participants viewed images of automobiles and their specifications. Emotional and attentional stimuli and the participants' responses, in the form of decisions made, were meticulously distinguished and analyzed via signal processing techniques, statistical tests, and brain mapping tools. Long-range temporal correlations (LRTCs) were also calculated to investigate whether the preference of a product could affect the dynamic of neuronal fluctuations. Statistically significant spatiotemporal dynamical differences were then evaluated between those who select an electric car (which seemingly demands specific memory and long-term attention) and participants who choose other cars. The results showed increased PSD and central-parietal and central-frontal coherences at the alpha frequency band for those who selected the electric car. In addition, the findings showed the emergence of LRTCs or the ability of this group to integrate information over extended periods. Furthermore, the result of clustering subjects into two groups, using statistically significant discriminative EEG measures, was associated with the self-report data. The obtained results highlighted the promising role of intrinsically extracted measures on consumers' buying behavior.

EEG based cognitive task classification using multifractal detrended fluctuation analysis

Locating cognitive task states by measuring changes in electrocortical activity due to various attentional and sensory-motor changes, has been in research interest since last few decades. In this paper, different cognitive states while performing various attentional and visuo-motor coordination tasks, are classified using electroencephalogram (EEG) signal. A non-linear time-series method, multifractal detrended fluctuation analysis (MFDFA) , is applied on respective EEG signal for features. Using MFDFA based features a multinomial classification is achieved. Nine channel EEG signal was recorded for 38 young volunteers (age: 25 ± 5 years, 30 male and 8 female), during six consecutive tasks. First three tasks are related to increasing levels of selective focus vision; next three are reflex and response based computer tasks. Total of 90 features (ten features from each of nine channel) were extracted from Hurst and singularity exponents of MFDFA on EEG signals. After feature selection, a multinomial classifier of six classes using two methods: support vector machine (SVM) and decision tree classifier (DTC). An accuracy of 96.84% using SVM and 92.49% using DTC was achieved.

Spectral changes following resective epilepsy surgery and neurocognitive function in children with epilepsy

Decelerated resting cortical oscillations, high-frequency activity, and enhanced cross-frequency interactions are features of focal epilepsy. The association between electrophysiological signal properties and neurocognitive function, particularly following resective surgery, is, however, unclear. In the current report, we studied intraoperative recordings from intracranial electrodes implanted in seven children with focal epilepsy and analyzed the spectral dynamics both before and after surgical resection of the hypothesized seizure focus. The associations between electrophysiological spectral signatures and each child's neurocognitive profiles were characterized using a partial least squares analysis. We find that extent of spectral alteration at the periphery of surgical resection, as indexed by slowed resting frequency and its acceleration following surgery, is associated with baseline cognitive deficits in children. The current report provides evidence supporting the relationship between altered spectral properties in focal epilepsy and neuropsychological deficits in children. In particular, these findings suggest a critical role of disrupted thalamocortical rhythms, which are believed to underlie the spectral alterations we describe, in both epileptogenicity and neurocognitive function.NEW & NOTEWORTHY Spectral alterations marked by decelerated resting oscillations and ectopic high-frequency activity have been noted in focal epilepsy. We leveraged intraoperative recordings from chronically implanted electrodes pre- and postresection to understand the association between these electrophysiological phenomena and neuropsychological function. We find that the extent of spectral alteration, indexed by slowed resting frequency and its acceleration following resection, is associated with baseline cognitive deficits. These findings provide novel insights into neurocognitive impairments in focal epilepsy.

Screening Tools and Assessment Methods of Cognitive Decline Associated With Age-Related Hearing Loss: A Review

Strong links between hearing and cognitive function have been confirmed by a growing number of cross-sectional and longitudinal studies. Seniors with age-related hearing loss (ARHL) have a significantly higher cognitive impairment incidence than those with normal hearing. The correlation mechanism between ARHL and cognitive decline is not fully elucidated to date. However, auditory intervention for patients with ARHL may reduce the risk of cognitive decline, as early cognitive screening may improve related treatment strategies. Currently, clinical audiology examinations rarely include cognitive screening tests, partly due to the lack of objective quantitative indicators with high sensitivity and specificity. Questionnaires are currently widely used as a cognitive screening tool, but the subject's performance may be negatively affected by hearing loss. Numerous electroencephalogram (EEG) and magnetic resonance imaging (MRI) studies analyzed brain structure and function changes in patients with ARHL. These objective electrophysiological tools can be employed to reveal the association mechanism between auditory and cognitive functions, which may also find biological markers to be more extensively applied in assessing the progression towards cognitive decline and observing the effects of rehabilitation training for patients with ARHL. In this study, we reviewed clinical manifestations, pathological changes, and causes of ARHL and discussed their cognitive function effects. Specifically, we focused on current cognitive screening tools and assessment methods and analyzed their limitations and potential integration.

N-Pep-12 supplementation after ischemic stroke positively impacts frequency domain QEEG

BACKGROUND

N-Pep-12 is a dietary supplement with neuroprotective and pro-cognitive effects, as shown in experimental models and clinical studies on patients after ischemic stroke. We tested the hypothesis that N-Pep-12 influences quantitative electroencephalography (QEEG) parameters in patients with subacute to chronic supratentorial ischemic lesions.

METHODS

We performed secondary data analysis on an exploratory clinical trial (ISRCTN10702895), assessing the efficacy and safety of 90 days of once-daily treatment with 90 mg N-Pep-12 on neurocognitive function and neurorecovery outcome in patients with post-stroke cognitive impairment against a control group. All participants performed two 32-channel QEEG in resting and active states at baseline (30-120 days after stroke) and 90 days later. Power spectral density on the alpha, beta, theta, delta frequency bands, delta/alpha power ratio (DAR), and (delta+theta)/(alpha+beta) ratio (DTABR) were computed and compared across study groups using means comparison and descriptive methods. Secondarily, associations between QEEG parameters and available neuropsychological tests were explored.

RESULTS

Our analysis showed a statistically significant main effect of EEG segments (p<0.001) in alpha, beta, delta, theta, DA, and DTAB power spectral density. An interaction effect between EEG segments and time was noticed in the alpha power. There was a significant difference in theta spectral power between patients with N-Pep-12 supplementation versus placebo at 0.05 alpha level (p=0.023), independent of time points.

CONCLUSION

A 90-day, 90 mg daily administration of N-Pep-12 had significant impact on some QEEG indicators in patients after supratentorial ischemic stroke, confirming possible enhancement of post-stroke neurorecovery. Further research is needed to consolidate our findings.

EEG microstate features according to performance on a mental arithmetic task

In this study, we hypothesized that task performance could be evaluated applying EEG microstate to mental arithmetic task. This pilot study also aimed at evaluating the efficacy of microstates as novel features to discriminate task performance. Thirty-six subjects were divided into good and poor performers, depending on how well they performed the task. Microstate features were derived from EEG recordings during resting and task states. In the good performers, there was a decrease in type C and an increase in type D features during the task compared to the resting state. Mean duration and occurrence decreased and increased, respectively. In the poor performers, occurrence of type D feature, mean duration and occurrence showed greater changes. We investigated whether microstate features were suitable for task performance classification and eleven features including four archetypes were selected by recursive feature elimination (RFE). The model that implemented them showed the highest classification performance for differentiating between groups. Our pilot findings showed that the highest mean Area Under Curve (AUC) was 0.831. This study is the first to apply EEG microstate features to specific cognitive tasks in healthy subjects, suggesting that EEG microstate features can reflect task achievement.

PyBioS: A freeware computer software for analysis of cardiovascular signals

BACKGROUND AND OBJECTIVE

Several software applications have been proposed in the past years as computational tools for assessing biomedical signals. Many of them are focused on heart rate variability series only, with their strengths and limitations depending on the necessity of the user and the scope of the application. Here, we introduce new software, named PyBioS, intended for the analysis of cardiovascular signals, even though any type of biomedical signal can be used. PyBioS has some functionalities that differentiate it from the other software.

METHODS

PyBioS was developed in Python language with an intuitive, user-friendly graphical user interface. The basic steps for using PyBioS comprise the opening or creation (simulation) of signals, their visualization, preprocessing and analysis. Currently, PyBioS has 8 preprocessing tools and 15 analysis methods, the later providing more than 50 metrics for analysis of the signals' dynamics.

RESULTS

The possibility to create simulated signals and save the preprocessed signals is a strength of PyBioS. Besides, the software allows batch processing of files, making the analysis of a large amount of data easy and fast. Finally, PyBioS has plenty of analysis methods implemented, with the focus on nonlinear and complexity analysis of signals and time series.

CONCLUSIONS

Although PyBioS is not intended to overcome all the necessities from users, it has useful functionalities that may be helpful in many situations. Moreover, PyBioS is continuously under improvement and several simulated signals, tools and analysis methods are still to be implemented. Also, a new module is being implemented on it to provide machine learning algorithms for classification and regression of data extracted from the biomedical signals.