EEG Complexity Research Articles

The Impact of the Natural Level of Blood Biochemicals on Electroencephalographic Markers in Healthy People.

This study aims to investigate the association between the natural level of blood biomarkers and electroencephalographic (EEG) markers. Resting EEG theta, alpha (ABP), beta, and gamma frequency band powers were selected as linear EEG markers indicating the level of EEG power, and Higuchi's fractal dimension (HFD) as a nonlinear EEG complexity marker reflecting brain temporal dynamics. The impact of seven different blood biomarkers, i.e., glucose, protein, lipoprotein, HDL, LDL, C-reactive protein, and cystatin C, was investigated. The study was performed on a group of 52 healthy participants. The results of the current study show that one linear EEG marker, ABP, is correlated with protein. The nonlinear EEG marker (HFD) is correlated with protein, lipoprotein, C-reactive protein, and cystatin C. A positive correlation with linear EEG power markers and a negative correlation with the nonlinear complexity marker dominate in all brain areas. The results demonstrate that EEG complexity is more sensitive to the natural level of blood biomarkers than the level of EEG power. The reported novel findings demonstrate that the EEG markers of healthy people are influenced by the natural levels of their blood biomarkers related to their everyday dietary habits. This knowledge is useful in the interpretation of EEG signals and contributes to obtaining information about people quality of life and well-being.

Metacontrol Regulates Creative Thinking: An EEG Complexity Analysis Based on Multiscale Entropy.

Previous studies have shown that creative thinking is associated with metacontrol, but its neural basis is unknown. The present study explored the neural basis of both by assessing EEG complexity through multiscale entropy. Subjects were engaged in a metacontrol task and an Alternative Uses Task, grouped according to task performance, and the EEG was analysed by multiscale entropy. The results showed that EEG complexity was significantly higher in the high-metacontrol and high-creativity groups than in the low-metacontrol and low-creativity groups, respectively, at high time scales. The metacontrol adaptability score and multipurpose task score were significantly and positively correlated with the EEG complexity at multiple electrode sites. It suggests that metacontrol and creativity are dependent on the activation of long-duration neural networks.

Differentiating neurodegenerative diseases based on EEG complexity

Neurodegenerative diseases are common causes of impaired mobility and cognition in the elderly. Among them, tauopathies and α-synucleinopathies were considered. The neurodegenerative processes and relative differential diagnosis were addressed through a qEEG non-linear analytic method. Study aims were to test accuracy of the power law exponent β applied to EEG in differentiating neurodegenerative diseases and to explore differences in neuronal connectivity among different neurodegenerative processes based on β. N = 230 patients with a diagnosis of tauopathy or α-synucleinopathy and at least one artifact-free EEG recording were selected. Periodogram was applied to EEG signal epochs from continuous recordings. Power law exponent β was determined by the slope of the signal power spectrum versus frequency in logarithmic scale. A data-driven clustering based on β values was performed to identify independent subgroups. Data-driven clustering based on β differentiated tauopathies (overall lower β values) from α-synucleinopathies (higher β values) with high sensitivity and specificity. Tauopathies also presented lower values in the correlation coefficients matrix among frontal sites of recording. In conclusion, significant differences in β values were found between tauopathies and α-synucleinopathies. Hence, β is proposed as a possible biomarker of differential diagnosis and neuronal connectivity.

Comparison of Brain Activity between Patients with Parkinson Disease Dementia and Patients Affected by Dementia with Lewy Body through EEG Analysis

Objective: Parkinson's disease dementia (PDD) and Dementia with Lewy bodies (DLB) are two syndromes categorized under synucleinopathy, sharing comparable symptoms. The identification of biomarkers would offer an accurate approach for improved diagnosis, treatment, and monitoring of treatment efficacy for these distinct forms of dementia. Method: This study utilized spectral analysis and nonlinear dynamic analysis to compare electroencephalogram (EEG) characteristics between PDD and DLB patients. EEG data was collected from 30 PDD patients, 36 DLB patients, and 36 healthy subjects at rest. Following a conditioning phase to minimize noise and eliminate artifacts, we derived spectral and complexity features using Welch's method and sample entropy. Analysis of variance with repeated measures was performed to compare spectral features and nonlinear dynamics of brain activity between the groups. Results: Post hoc comparison showed that in the control group, the power of delta and theta bands was lower and the power of alpha and beta bands was higher than in patients with PDD and DLB. (P < 0.05). In the theta and alpha bands, the PDD group showed greater power than the DLB group (P < 0.05). Furthermore, there was a significant main effect of diagnosis (F = 4.67, P = 0.007), and also the diagnosis by region interaction for complexity values (F = 4.58, P = 0.009). Post hoc analysis showed that the EEG complexity of the control group was significantly higher than that of the PDD and DLB groups in the frontal, central, temporal and parietal regions (P < 0.05). Moreover, the EEG complexity of the PDD group was significantly higher than that of the DLB group in the central, temporal and parietal regions (P < 0.05). Conclusion: Although both PDD and DLB had almost similar patterns compared to the control group, they showed differences in the EEG power spectrum and its nonlinear dynamics. Our findings indicated marked diffuse slowing and lower cortical complexity or activity in DLB patients compared to PDD in all regions, especially in the central, temporal and parietal areas.

Depression detection based on the temporal-spatial-frequency feature fusion of EEG

Depression is a prevalent affective psychiatric disorder projected to be the leading contributor to the world’s disease burden by 2030. Due to its high prevalence and low recognition rate, an objective and effective detection method is urgently needed. Deep learning methods based on electroencephalography (EEG) have shown significant potential in depression detection. However, excessive channels can increase redundancy and computational complexity in EEG, while irrelevant channels may reduce accuracy. Additionally, existing models often overlook the complementarity between the temporal-, spatial-, and frequency-domain features of EEG, limiting their detection capabilities. To address these issues, we propose a method that fuses the temporal, spatial, and frequency domain features of EEG to enhance the detection accuracy while eliminating redundant channels. We introduce an EEG channel selection method based on frequency domain weighting that automatically adjusts the channel weights to select the EEG channels that best capture spatial information across the delta, theta, alpha, beta, and gamma bands, thereby optimizing the extraction of spatial-frequency features. In addition, we designed a multiscale spatiotemporal convolutional attention network to extract the spatiotemporal features of EEG. In this network, the multiscale convolutional attention module enhanced the model’s ability to capture spatial features, whereas the temporal trend-aware self-attention module extracted long-term temporal features by analyzing global correlations across different time points. Experimental results on the MODMA dataset show that our method achieved a 97.24% detection accuracy, surpassing current state-of-the-art models. This study offers a novel approach for constructing depression detection models, providing a foundation for future research and application.

Impairment of inhibitory control due to repetitive subconcussions from indirect brain impacts: Evidence from event-related potentials and resting-state EEG complexity in parachuters

The present study aims to investigate the unknown relationship between inhibitory control and repetitive subconcussion induced by the indirect brain impacts. We enrolled 28 parachuters exposed to repetitive subconcussion (SC) and 27 matched health controls (HC). Parachuters who have completed at least 70 actual parachuting (71–112 times) and at least 1500 simulated platform jumps (1500–4500 times) were included in the SC group. The SC group had a reduced accuracy rate in both the Stroop congruent and incongruent conditions. Larger N2 and N450 amplitudes were elicited in the frontal regions of the SC group, which indicate compensatory adaptations to the deficit in conflict monitoring. The reduced frontal resting-state EEG complexity in full-band (1–40 Hz) may demonstrate the frontal structural damage following the indirect brain impacts of repetitive subconcussion. Pearson correlation analysis showed that in the SC group, the frontal beta-band sample entropy values are positively correlated with the accuracy rate of the Stroop incongruent condition, suggesting the frontal beta-band sample entropy values may serve as potential electrophysiological markers of impaired inhibitory control after indirectly repetitive brain impacts. This study provides the robust evidence that repetitive subconcussion resulting from indirect brain impacts may lead to impairment of inhibitory control.

METHODS OF MATHEMATICAL ANALYSIS AND PROCESSING OF EEG SIGNALS: OVERVIEW OF THE CURRENT STATE AND LATEST TRENDS

Aim. With the help of retrospective analysis, establish the current level of achievements in the field of applying methods of mathematical analysis and processing of biomedical signals, in particular EEG, and characterize the main trends. Materials and methods. The research was conducted using theoretical methods, by studying and analyzing freely available literature, publications, materials of working groups of domestic and foreign scientists over the past few years. Results. The analysis of scientific works devoted to the development and development of EEG showed that over the past five years there has been a deepening of the tendency to separate the methods of improving the hardware and software parts of electroencephalographic complexes. The last of them, in turn, can be divided into several main directions. The first direction is the improvement of real-time EEG analysis methods, including automatic processing included in the software of EEG complexes. The second direction combines mathematical methods of signal analysis to detect existing pathologies and predict the onset of pathological conditions, such as statistical, spectral-correlation analysis, building mathematical models. The next direction brings together the application of mathematical methods for EEG signal processing and further use in deep learning, the use of convolutional neural networks to detect epileptic seizures, sleep disorders, differentiation of neuropsychiatric disorders, etc. The direction of using EEG in prosthetics deserves special attention. Methods of mathematical processing of the EEG signal are widely implemented in the development of bionic prostheses. The main achievements in these directions are considered in detail in the work. Conclusions. While the improvement of the material and technical part of the EEG is focused on the issues of improving the methods of recording biological signals, finding optimal methods of suppressing noise without losing the useful signal and does not currently have breakthrough results, the methods of analysis and processing of the already received signal have developed rapidly. The use of EEG to detect pathological conditions, the study of the neurophysiological basis of cognitive processes, etc., has always attracted the interest of scientists, but the use of neural networks for EEG processing and analysis and their implementation in robotics developments has given this direction a new impetus.

Investigating Cortical Complexity in Mixed Dementia through Nonlinear Dynamic Analyses: A Resting-State EEG Study.

Objective: Dementia is a broad term referring to a decline in problem-solving abilities, language skills, memory, and other cognitive functions to a degree that it significantly disrupts everyday activities. The underlying cause of dementia is the impairment or loss of nerve cells and their connections within the brain. The particular symptoms experienced are contingent upon specific regions of the brain affected by this damage. In this research, we aimed to investigate the nonlinear dynamics of the mixed demented brain compared to healthy subjects using electroencephalogram (EEG) analysis. Method : For this purpose, EEG was recorded from 66 patients with mixed dementia and 65 healthy subjects during rest. After signal preprocessing, sample entropy and Katz fractal dimension analyses were applied to the preprocessed EEG data. Analysis of variance with repeated measures was utilized to compare the nonlinear dynamics of brain activity between dementia and healthy states and partial correlation analysis was employed to explore the relationship between EEG complexity measures and cognitive and neuropsychiatric symptoms of patients. Results: Based on repeated measures ANOVA, there was a significant main effect between groups for both Katz fractal dimension (F = 4.10, P = 0.01) and sample entropy (F = 4.81, P = 0.009) measures. Post hoc comparisons revealed that EEG complexity was significantly reduced in dementia mainly in the occipitoparietal and temporal areas (P < 0.05). MMSE scores were positively correlated with EEG complexity measures, while NPI scores were negatively correlated with EEG complexity measures, mainly in the occipitoparietal and temporal areas (P < 0.05). Moreover, using a KNN classifier, all significant complexity measures yielded the best classification performance with an accuracy of 98.05%, sensitivity of 97.03% and specificity of 99.16% in detecting dementia. Conclusion: This study demonstrated a unique dynamic system within the brain impacted by dementia that results in more predictable patterns of cortical activity mainly in the occipitoparietal and temporal areas. These abnormal patterns were associated with patients' cognitive capacity and neuropsychiatric symptoms.

Unveiling Neurophysiological Markers of Consciousness Levels through EEG Exploration

The concept of consciousness levels typically refers to various aspects and tiers related to an individual’s cognition, perception, thinking, and awareness. Although neurophysiological markers have not yet been definitively identified to distinguish between these nuanced levels, this paper introduces a robust marker, the Approximate Entropy (ApEn), which quantifies the complexity of EEG signals to differentiate states of altered consciousness. Utilizing ApEn, we analyze EEG data from the frontal lobe—a region closely associated with consciousness—in states indicative of severely altered conditions, specifically anesthesia, coma, and brain death. To enhance the precision of consciousness levelassessment, we employ a Support Vector Machine (SVM) model, which classifies the states based on EEG complexity measures. This approach not only provides valuable insights into the neural correlations associated with changes in these critical states but also underscores the potential of combining quantitative EEG analysis with machine learning techniques to advance our understanding of consciousness. The findings demonstrate that EEG complexity, when analyzed using ApEn coupled with SVM classification, offers a novel and effective method for assessing and distinguishing between degrees of consciousness. This approach promises significant implications for clinical diagnostics and patient monitoring.

Effective relax acquisition: a novel approach to classify relaxed state in alpha band EEG-based transformation

A relaxed state is essential for effective hypnotherapy, a crucial component of mental health treatments. During hypnotherapy sessions, neurologists rely on the patient’s relaxed state to introduce positive suggestions. While EEG is a widely recognized method for detecting human emotions, analyzing EEG data presents challenges due to its multi-channel, multi-band nature, leading to high-dimensional data. Furthermore, determining the onset of relaxation remains challenging for neurologists. This paper presents the Effective Relax Acquisition (ERA) method designed to identify the beginning of a relaxed state. ERA employs sub-band sampling within the Alpha band for the frequency domain and segments the data into four-period groups for the time domain analysis. Data enhancement strategies include using Window Length (WL) and Overlapping Shifting Windows (OSW) scenarios. Dimensionality reduction is achieved through Principal Component Analysis (PCA) by prioritizing the most significant eigenvector values. Our experimental results indicate that the relaxed state is predominantly observable in the high Alpha sub-band, particularly within the fourth period group. The ERA demonstrates high accuracy with a WL of 3 s and OSW of 0.25 s using the KNN classifier (90.63%). These findings validate the effectiveness of ERA in accurately identifying relaxed states while managing the complexity of EEG data.Graphical abstract

Unraveling the complexity of rapid eye movement microstates: insights from nonlinear EEG analysis.

Although rapid eye movement (REM) sleep is conventionally treated as a unified state, it comprises two distinct microstates: phasic and tonic REM. Recent research emphasizes the importance of understanding the interplay between these microstates, hypothesizing their role in transient shifts between sensory detachment and external awareness. Previous studies primarily employed linear metrics to probe cognitive states, such as oscillatory power, while in this study, we adopt Lempel-Ziv Complexity (LZC), to examine the nonlinear features of electroencephalographic (EEG) data from the REM microstates and to gain complementary insights into neural dynamics during REM sleep. Our findings demonstrate a noteworthy reduction in LZC during phasic REM compared to tonic REM states, signifying diminished EEG complexity in the former. Additionally, we noted a negative correlation between decreased LZC and delta band power, along with a positive correlation with alpha band power. This study highlights the potential of nonlinear EEG metrics, particularly LZC, in elucidating the distinct features of REM microstates. Overall, this research contributes to advancing our understanding of the complex dynamics within REM sleep and opens new avenues for exploring its implications in both clinical and nonclinical contexts.

Analysis of EEG characteristics in Alzheimer’s disease

Alzheimer’s disease (AD) brings a heavy burden to society and families because of the high misdiagnosis rate, prevalence rate, and disability rate. At present, the treatment of AD still faces great challenges. Early intervention can effectively delay the disease progression and improve the burden of symptom care. However, the key to early intervention is accurately identifying and assessing the disease. The examination is difficult to be widely used because of its high invasive cost. There is an urgent need for a simple, convenient, and non-invasive diagnosis of AD. Studies have shown that the brain number of AD patients is slower than that of healthy people, and the complexity of EEG is disturbed. Clinical practice has proved that the specificity and sensitivity of α, β, δ, θ absolute power and α/θ absolute power ratio, especially θ absolute power and α/θ absolute power ratio in the model, are important electrophysiological indexes for AD diagnosis EEG. Non-invasive and economical electrophysiological techniques can characterize and identify the EEG characteristics of AD patients from different angles. They are relatively simple to implement, and patients have a high degree of cooperation. Therefore, EEG, as a diagnostic method of AD, can improve the accuracy of differential diagnosis and early detection of AD and quantify the severity of the disease. It has the advantage of non-invasive convenience and price, which is of great significance for early diagnosis of AD.

EEG complexity in emotion conflict task in individuals with psychiatric disorders

Analyzing EEG complexity may help to elucidate complex brain dynamics in individuals with psychiatric disorders and provide insight into neural connectivity and its relationship with deficits such as emotion-related impulsivity. EEG complexity was calculated through multiscale entropy and compared between a heterogeneous psychiatric patient group and a healthy control group during the emotion conflict resolution task. Twenty-eight healthy adults and ten psychiatric patients were recruited and compared on the multiscale entropy of EEG acquired in the task. Our results revealed a lower multiscale entropy in the psychiatric patient group compared to the healthy group during the task. This decrease in multiscale entropy suggests reduced long-range interaction between the left frontal region and other brain regions during the emotion conflict resolution task among psychiatric patients. Notably, a positive correlation was observed between multiscale entropy and impulsivity measures in the psychiatric patient group, where the higher the EEG complexity during the emotion regulation task, the higher the level of self-reported impulsivity in the psychiatric patients. Such impulsivity was evident in both healthy individuals and psychiatric patients, with healthy individuals showing shorter reaction times on incongruent conditions compared to congruent conditions and psychiatric patients displaying similar reaction times in both conditions, This study highlights the significance of investigating EEG complexity and its potential applications in the transdiagnostic exploration of impulsivity in psychiatric disorders.

EEG complexity measures for detecting mind wandering during video-based learning

This study explores the efficacy of various EEG complexity measures in detecting mind wandering during video-based learning. Employing a modified probe-caught method, we recorded EEG data from participants engaged in viewing educational videos and subsequently focused on the discrimination between mind wandering (MW) and non-MW states. We systematically investigated various EEG complexity metrics, including metrics that reflect a system’s regularity like multiscale permutation entropy (MPE), and metrics that reflect a system’s dimensionality like detrended fluctuation analysis (DFA). We also compare these features to traditional band power (BP) features. Data augmentation methods and feature selection were applied to optimize detection accuracy. Results show BP features excelled (mean area under the receiver operating characteristic curve (AUC) 0.646) in datasets without eye-movement artifacts, while MPE showed similar performance (mean AUC 0.639) without requiring removal of eye-movement artifacts. Combining all kinds of features improved decoding performance to 0.66 mean AUC. Our findings demonstrate the potential of these complexity metrics in EEG analysis for mind wandering detection, highlighting their practical implications in educational contexts.

One size does not fit all: notable individual variation in brain activity correlates of antidepressant treatment response.

To date, no robust electroencephalography (EEG) markers of antidepressant treatment response have been identified. Variable findings may arise from the use of group analyses, which neglect individual variation. Using a combination of group and single-participant analyses, we explored individual variability in EEG characteristics of treatment response. Resting-state EEG data and Montgomery-Åsberg Depression Rating Scale (MADRS) symptom scores were collected from 43 patients with depression before, at 1 and 12 weeks of pharmacotherapy. Partial least squares (PLS) was used to: 1) identify group differences in EEG connectivity (weighted phase lag index) and complexity (multiscale entropy) between eventual medication responders and non-responders, and 2) determine whether group patterns could be identified in individual patients. Responders showed decreased alpha and increased beta connectivity, and early, widespread decreases in complexity over treatment. Non-responders showed an opposite connectivity pattern, and later, spatially confined decreases in complexity. Thus, as in previous studies, our group analyses identified significant differences between groups of patients with different treatment outcomes. These group-level EEG characteristics were only identified in ~40-60% of individual patients, as assessed quantitatively by correlating the spatiotemporal brain patterns between groups and individual results, and by independent raters through visualization. Our single-participant analyses suggest that substantial individual variation exists, and needs to be considered when investigating characteristics of antidepressant treatment response for potential clinical applicability. https://clinicaltrials.gov, identifier NCT00519428.

Investigating the interpretability of schizophrenia EEG mechanism through a 3DCNN-based hidden layer features aggregation framework

Background and objectiveElectroencephalogram (EEG) signals record brain activity, with growing interest in quantifying neural activity through complexity analysis as a potential biological marker for schizophrenia. Presently, EEG complexity analysis primarily relies on manual feature extraction, which is subjective and yields varied findings in studies involving schizophrenia and healthy controls. MethodsThis study aims to leverage deep learning methods for enhanced EEG complexity exploration, aiding early schizophrenia screening and diagnosis. Our proposed approach utilizes a three-dimensional Convolutional Neural Network (3DCNN) to extract enhanced data features for early schizophrenia identification and subsequent complexity analysis. Leveraging the spatiotemporal capabilities of 3DCNN, we extract advanced latent features and employ knowledge distillation to reintegrate these features into the original channels, creating feature-enhanced data. ResultsWe employ a 10-fold cross-validation strategy, achieving the average accuracies of 99.46% and 98.06% in subject-dependent experiments on Dataset 1(14SZ and 14HC) and Dataset 2 (45SZ and 39HC). The average accuracy for subject-independent is 96.04% and 92.67% on both datasets. Feature extraction and classification are conducted on both the re-aggregated data and the original data. Our results demonstrate that re-aggregated data exhibit superior classification performance and a more stable training process after feature extraction. In the complexity analysis of re-aggregated data, we observe lower entropy features in schizophrenic patients compared to healthy controls, with more pronounced differences in the temporal and frontal lobes. Analyzing Katz's Fractal Dimension (KFD) across three sub-bands of lobe channels reveals the lowest α band KFD value in schizophrenia patients. ConclusionsThis emphasizes the ability of our method to enhance the discrimination and interpretability in schizophrenia detection and analysis. Our approach enhances the potential for EEG-based schizophrenia diagnosis by leveraging deep learning, offering superior discrimination capabilities and richer interpretive insights.

Fractal Analysis of Electrophysiological Signals to Detect and Monitor Depression: What We Know So Far?

Depression is currently one of the most complicated public health problems with the rising number of patients, increasing partly due to pandemics, but also due to increased existential insecurities and complicated aetiology of disease. Besides the tsunami of mental health issues, there are limitations imposed by ambiguous clinical rules of assessment of the symptoms and obsolete and inefficient standard therapy approaches. Here we are summarizing the neuroimaging results pointing out the actual complexity of the disease and novel attempts to detect depression that are evidence-based, mostly related to electrophysiology. It is repeatedly shown that the complexity of resting-state EEG recorded in patients suffering from depression is increased compared to healthy controls. We are discussing here how that can be interpreted and what we can learn about future effective therapies. Also, there is evidence that novel options of treatment, like different modalities of electromagnetic stimulation, are successful just because they are capable of decreasing that aberrated complexity. And complexity measures extracted from electrophysiological signals of depression patients can serve as excellent features for further machine learning models in order to automatize detection. In addition, after initial detection and even selection of responders for further therapy route, it is possible to monitor the therapeutic flow for one person, which leads us to possible tailored treatment for patients suffering from depression.

EEG Complexity Analysis of Brain States, Tasks and ASD Risk.

Autism spectrum disorder is an increasingly prevalent and debilitating neurodevelopmental condition and an electroencephalogram (EEG) diagnostic challenge. Despite large amounts of electrophysiological research over many decades, an EEG biomarker for autism spectrum disorder (ASD) has not been found. We hypothesized that reductions in complex dynamical system behaviour in the human central nervous system as part of the macroscale neuronal function during cognitive processes might be detectable in whole EEG for higher-risk ASD adults. In three studies, we compared the medians of correlation dimension, largest Lyapunov exponent, Higuchi's fractal dimension, multiscale entropy, multifractal detrended fluctuation analysis and Kolmogorov complexity during resting, cognitive and social skill tasks in 20 EEG channels of 39 adults over a range of ASD risk. We found heterogeneous complexity distribution with clusters of hierarchical sequences pointing to potential cognitive processing differences, but no clear distinction based on ASD risk. We suggest that there is indication of statistically significant differences between complexity measures of brain states and tasks. Though replication of our studies is needed with a larger sample, we believe that our electrophysiological and analytic approach has potential as a biomarker for earlier ASD diagnosis.

A novel approach to schizophrenia Detection: Optimized preprocessing and deep learning analysis of multichannel EEG data

Schizophrenia diagnosis, characterized by cognitive deficits, hallucinations, and delusions, poses challenges due to its complex nature. Electroencephalogram (EEG) signals provide insights into underlying brain states, enabling efficient and accurate diagnosis. This study introduces a novel approach, utilizing the Mutation-boosted Archimedes Optimization (MAO) algorithm to optimize preprocessing steps for improved EEG data quality. The spatial and temporal patterns are extracted from multichannel EEG data through Convolutional Neural Networks (CNNs) based Long Short-Term Memory (LSTM) network. Entropy-related features, including spectral and permutation entropy, are integrated to capture EEG complexity. The Schizophrenia Detection layer leverages these features for classification. The fitness function combines classification accuracy and noise reduction, optimizing preprocessing and classification performance. Experimental evaluation on a diverse dataset validates the effectiveness of the proposed approach. Achieving accuracy of 98.2%, F1-score of 97.2%, specificity of 97.5%, precision of 98.5%, and sensitivity of 98.9%, the method surpasses existing techniques. Moreover, the MAO algorithm enhances signal quality, evident through improved Signal-to-Noise Ratio (SNR), Signal-to-Interference Ratio (SIR), and reduced artifact rejection rates. Overall, this research demonstrates the potential of the Mutation-boosted Archimedes Optimization algorithm in enhancing EEG data preprocessing for accurate schizophrenia detection. The integration of deep learning architectures and advanced optimization techniques presents a promising avenue for improving mental disorder diagnoses through EEG signal analysis.

The EEG complexity, information integration and brain network changes in minimally conscious state patients during general anesthesia

Objective. General anesthesia (GA) can induce reversible loss of consciousness. Nonetheless, the electroencephalography (EEG) characteristics of patients with minimally consciousness state (MCS) during GA are seldom observed. Approach. We recorded EEG data from nine MCS patients during GA. We used the permutation Lempel–Ziv complexity (PLZC), permutation fluctuation complexity (PFC) to quantify the type I and II complexities. Additionally, we used permutation cross mutual information (PCMI) and PCMI-based brain network to investigate functional connectivity and brain networks in sensor and source spaces. Main results. Compared to the preoperative resting state, during the maintenance of surgical anesthesia state, PLZC decreased (p < 0.001), PFC increased (p < 0.001) and PCMI decreased (p < 0.001) in sensor space. The results for these metrics in source space are consistent with sensor space. Additionally, node network indicators nodal clustering coefficient (NCC) (p < 0.001) and nodal efficiency (NE) (p < 0.001) decreased in these two spaces. Global network indicators normalized average path length () (p < 0.01) and modularity (Q) (p < 0.05) only decreased in sensor space, while the normalized average clustering coefficient () and small-world index () did not change significantly. Moreover, the dominance of hub nodes is reduced in frontal regions in these two spaces. After recovery of consciousness, PFC decreased in the two spaces, while PLZC, PCMI increased. NCC, NE, and frontal region hub node dominance increased only in the sensor space. These indicators did not return to preoperative levels. In contrast, global network indicators and Q were not significantly different from the preoperative resting state in sensor space. Significance. GA alters the complexity of the EEG, decreases information integration, and is accompanied by a reconfiguration of brain networks in MCS patients. The PLZC, PFC, PCMI and PCMI-based brain network metrics can effectively differentiate the state of consciousness of MCS patients during GA.