EEG Analysis Research Articles

EEG Analyses of visual cue effects on executed movements

BackgroundIn electroencephalographic (EEG) or electrocorticographic (ECoG) experiments, visual cues are commonly used for timing synchronization but may inadvertently induce neural activity and cognitive processing, posing challenges when decoding self-initiated tasks. New methodTo address this concern, we introduced four new visual cues (Fade, Rotation, Reference, and Star) and investigated their impact on brain signals. Our objective was to identify a cue that minimizes its influence on brain activity, facilitating cue-effect free classifier training for asynchronous applications, particularly aiding individuals with severe paralysis. Results22 able-bodied, right-handed participants aged 18–30 performed hand movements upon presentation of the visual cues. Analysis of time-variability between movement onset and cue-aligned data, grand average MRCP, and classification outcomes revealed significant differences among cues. Rotation and Reference cue exhibited favorable results in minimizing temporal variability, maintaining MRCP patterns, and achieving comparable accuracy to self-paced signals in classification. Comparison with existing methodsOur study contrasts with traditional cue-based paradigms by introducing novel visual cues designed to mitigate unintended neural activity. We demonstrate the effectiveness of Rotation and Reference cue in eliciting consistent and accurate MRCPs during motor tasks, surpassing previous methods in achieving precise timing and high discriminability for classifier training. ConclusionsPrecision in cue timing is crucial for training classifiers, where both Rotation and Reference cue demonstrate minimal variability and high discriminability, highlighting their potential for accurate classifications in online scenarios. These findings offer promising avenues for refining brain-computer interface systems, particularly for individuals with motor impairments, by enabling more reliable and intuitive control mechanisms.

Early automated classification of neonatal hypoxic-ischemic encephalopathy − An aid to the decision to use therapeutic hypothermia

ObjectiveThe study aimed to address the challenge of early assessment of neonatal hypoxic-ischemic encephalopathy (HIE) severity to identify candidates for therapeutic hypothermia (TH). The objective was to develop an automated classification model for neonatal EEGs, enabling accurate HIE severity assessment 24/7. MethodsEEGs recorded within 6 h of life after perinatal anoxia were visually graded into 3 severity groups (HIE French Classification) and quantified using 6 qEEG markers measuring amplitude, continuity and frequency content. Machine learning models were developed on a dataset of 90 EEGs and validated on an independent dataset of 60 EEGs. ResultsThe selected model achieved an overall accuracy of 80.6% in the development phase and 80% in the validation phase. Notably, the model accurately identified 28 out of 30 children for whom TH was indicated after visual EEG analysis, with only 2 cases (moderate EEG abnormalities) not recommended for cooling. ConclusionsThe combination of clinically relevant qEEG markers led to the development of an effective automated EEG classification model, particularly suited for the post-anoxic latency phase. This model successfully discriminated neonates requiring TH. SignificanceThe proposed model has potential as a bedside clinical decision support tool for TH.

Proprioception enhancement for robot assisted neural rehabilitation: a dynamic electrical stimulation based method and preliminary results from EEG analysis

Objective.In recent years, the robot assisted (RA) rehabilitation training has been widely used to counteract defects of the manual one provided by physiotherapists. However, since the proprioception feedback provided by the robotic assistance or the manual methods is relatively weak for the paralyzed patients, their rehabilitation efficiency is still limited. In this study, a dynamic electrical stimulation (DES) based proprioception enhancement and the associated quantitative analysis methods have been proposed to overcome the limitation mentioned above.Approach.Firstly, the DES based proprioception enhancement method was proposed for the RA neural rehabilitation. In the method, the relationship between the surface electromyogram (sEMG) envelope of the specified muscle and the associated joint angles was constructed, and the electrical stimulation (ES) pulses for the certain joint angles were designed by consideration of the corresponding sEMG envelope, based on which the ES can be dynamically regulated during the rehabilitation training. Secondly, power spectral density, source estimation, and event-related desynchronization of electroencephalogram, were combinedly used to quantitatively analyze the proprioception from multiple perspectives, based on which more comprehensive and reliable analysis results can be obtained. Thirdly, four modes of rehabilitation training tasks, namely active, RA, DES-RA, and ES-only training, were designed for the comparison experiment and validation of the proposed DES based proprioception enhancement method.Main results.The results indicated that the activation of the sensorimotor cortex was significantly enhanced when the DES was added, and the cortex activation for the DES-RA training was similar to that for the active training. Meanwhile, relatively consistent results from the multiple perspectives were obtained, which validates the effectiveness and robustness of the proposed proprioception analysis method.Significance.The proposed methods have the potential to be applied in the practical rehabilitation training to improve the rehabilitation efficiency.

Sleep stages and brain oscillations: An in-depth analysis using quantitative and mathematical models

This study explores the intricate relationship between sleep stages and brain oscillations, utilizing quantitative EEG analysis and mathematical models. We examine the distinct EEG characteristics of light sleep, deep sleep, and REM sleep, focusing on power spectral density (PSD), coherence, and phase synchronization analyses. The Ising model and Kuramoto model provide frameworks for understanding the synchronization dynamics and phase relationships of neuronal populations across sleep stages. Our findings highlight significant variations in oscillatory activity, coherence, and phase synchronization, offering insights into the neural mechanisms underlying sleep regulation. Additionally, we discuss the implications of these findings for sleep disorders such as insomnia, sleep apnea, and narcolepsy, demonstrating how mathematical models can predict therapeutic outcomes and guide targeted interventions. This comprehensive analysis contributes to the understanding of sleep architecture and the development of novel treatments for sleep disorders.

Classification of mental workload with EEG analysis by using effective connectivity and a hybrid model of CNN and LSTM

Estimation of mental workload from electroencephalogram (EEG) signals aims to accurately measure the cognitive demands placed on an individual during multitasking mental activities. By analyzing the brain activity of the subject, we can determine the level of mental effort required to perform a task and optimize the workload to prevent cognitive overload or underload. This information can be used to enhance performance and productivity in various fields such as healthcare, education, and aviation. In this paper, we propose a method that uses EEG and deep neural networks to estimate the mental workload of human subjects during multitasking mental activities. Notably, our proposed method employs subject-independent classification. We use the “STEW” dataset, which consists of two tasks, namely “No task” and “simultaneous capacity (SIMKAP)-based multitasking activity”. We estimate the different workload levels of two tasks using a composite framework consisting of brain connectivity and deep neural networks. After the initial preprocessing of EEG signals, an analysis of the relationships between the 14 EEG channels is conducted to evaluate effective brain connectivity. This assessment illustrates the information flow between various brain regions, utilizing the direct Directed Transfer Function (dDTF) method. Then, we propose a deep hybrid model based on pre-trained Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) for the classification of workload levels. The accuracy of the proposed deep model achieved 83.12% according to the subject-independent leave-subject-out (LSO) approach. The pre-trained CNN + LSTM approaches to EEG data have been found to be an accurate method for assessing the mental workload.

Latent Prototype-Based Clustering: A Novel Exploratory Electroencephalography Analysis Approach.

Electroencephalography (EEG)-based applications in brain-computer interfaces (BCIs), neurological disease diagnosis, rehabilitation, etc., rely on supervised approaches such as classification that requires given labels. However, with the ever-increasing amount of EEG data, incomplete or incorrectly labeled or unlabeled EEG data are increasing. It likely degrades the performance of supervised approaches. In this work, we put forward a novel unsupervised exploratory EEG analysis solution by clustering based on low-dimensional prototypes in latent space that are associated with the respective clusters. Having the prototype as a baseline of each cluster, a compositive similarity is defined to act as the critic function in clustering, which incorporates similarities on three levels. The approach is implemented with a Generative Adversarial Network (GAN), termed W-SLOGAN, by extending the Stein Latent Optimization for GANs (SLOGAN). The Gaussian Mixture Model (GMM) is utilized as the latent distribution to adapt to the diversity of EEG signal patterns. The W-SLOGAN ensures that images generated from each Gaussian component belong to the associated cluster. The adaptively learned Gaussian mixing coefficients make the model remain effective in dealing with an imbalanced dataset. By applying the proposed approach to two public EEG or intracranial EEG (iEEG) epilepsy datasets, our experiments demonstrate that the clustering results are close to the classification of the data. Moreover, we present several findings that were discovered by intra-class clustering and cross-analysis of clustering and classification. They show that the approach is attractive in practice in the diagnosis of the epileptic subtype, multiple labelling of EEG data, etc.

Is parental overcontrol a specific form of child maltreatment? Insights from a resting state EEG connectivity study

IntroductionRecent studies suggest that parental overcontrol could be considered a specific form of childhood trauma (CT). Although previous research has shown that CT alters the functional and structural architecture of large-scale networks in the brain, the neural basis associated with parental overcontrol has not been sufficiently explored. Therefore, the main aim of the current study was to investigate the relationship between parental overcontrol and electroencephalography (EEG) triple network (TN) functional connectivity during the resting state (RS) condition in a non-clinical sample (N = 71; 39 females, mean age 23.94 ± 5.89 SD). MethodsEEG was recorded during 5 min of RS with eyes closed. All participants were asked to self-report maternal and paternal overcontrol, CT and general psychopathology. All EEG analyses were performed using the exact low-resolution electromagnetic tomography software (eLORETA). ResultsOur results showed a significant positive correlation between maternal overcontrol and theta connectivity between the salience network and the central executive network. This connectivity pattern was independently associated with maternal overcontrol even when controlling for relevant confounding variables, including the severity of CT and the general level of psychopathology. This neurophysiological pattern may reflect a predisposition to detect and respond to potentially threatening stimuli in the environment, which is typically associated with excessive overcontrol. ConclusionsOur findings support the hypothesis that parental overcontrol should be considered a form of CT in all respects independent of the forms traditionally studied in the literature (i.e., emotional abuse, physical abuse, sexual abuse, and physical and emotional neglect).

Effects of acute administration of 4-allyl-2,6-dimethoxyphenol in mouse models of seizures

Epilepsy, a chronic neurological disorder characterized by recurrent unprovoked seizures, presents a substantial challenge in approximately one-third of cases exhibiting resistance to conventional pharmacological treatments. This study investigated the effect of 4-allyl-2,6-dimethoxyphenol, a phenolic compound derived from various natural sources, in different models of induced seizures and its impact on animal electroencephalographic (EEG) recordings. Adult male Swiss albino mice were pre-treated (i.p.) with a dose curve of 4-allyl-2,6-dimethoxyphenol (50, 100, or 200 mg/kg), its vehicle (Tween), or standard antiepileptic drug (Diazepam; or Phenytoin). Subsequently, the mice were subjected to different seizure-inducing models – pentylenetetrazole (PTZ), 3-mercaptopropionic acid (3-MPA), pilocarpine (PILO), or maximal electroshock seizure (MES). EEG analysis was performed on other animals surgically implanted with electrodes to evaluate brain activity. Significant results revealed that animals treated with 4-allyl-2,6-dimethoxyphenol exhibited increased latency to the first myoclonic jerk in the PTZ and PILO models; prolonged latency to the first tonic-clonic seizure in the PTZ, 3-MPA, and PILO models; reduced total duration of tonic-clonic seizures in the PTZ and PILO models; decreased intensity of convulsive seizures in the PTZ and 3-MPA models; and diminished mortality in the 3-MPA, PILO, and MES models. EEG analysis indicated an increase in the percentage of total power attributed to beta waves following 4-allyl-2,6-dimethoxyphenol administration. Notably, the substance protected from behavioral and electrographic seizures in the PTZ model, preventing increases in the average amplitude of recording signals while also inducing an increase in the participation of theta and gamma waves. These findings suggest promising outcomes for the tested phenolic compound across diverse pre-clinical seizure models, highlighting the need for further comprehensive studies to elucidate its underlying mechanisms and validate its clinical relevance in epilepsy management.

Detection of Unfocused EEG Epochs by the Application of Machine Learning Algorithm.

Electroencephalography (EEG) is a non-invasive method used to track human brain activity over time. The time-locked EEG to an external event is known as event-related potential (ERP). ERP can be a biomarker of human perception and other cognitive processes. The success of ERP research depends on the laboratory conditions and attentiveness of the test subjects. Specifically, the inability to control experimental variables has reduced ERP research in the real world. This study collected EEG data under various experimental circumstances within an auditory oddball paradigm experiment to enable the use of ERP as an active biomarker in normal laboratory conditions. Then, ERP epochs were analyzed to identify unfocused epochs, affected by typical artifacts and external distortion. For the initial comparison, the ability of four unsupervised machine learning algorithms (MLAs) was evaluated to identify unfocused epochs. Then, their accuracy was compared with the human inspection and a current EEG analysis tool (EEGLab). All four MLAs were typically 95-100% accurate. In summary, our analysis finds that humans might miss subtle differences in the regular ERP patterns, but MLAs could efficiently identify those. Thus, our analysis suggests that unsupervised MLAs perform better for detecting unfocused ERP epochs compared with the other two standard methods.

Efficient predefined time adaptive neural network for motor execution EEG signal classification based brain-computer interaction

Nowadays, Electroencephalogram (EEG) devices that do not require invasive procedures get more attraction. Brain-Computer Interface (BCI) systems use EEG analysis to identify users' mental states, cognitive shifts, and stimuli-induced reactions. The Motor Execution (ME) paradigm is a vital control paradigm that holds great significance in this framework. In this manuscript, an Efficient Predefined Time Adaptive Neural Network for EEG-Based Brain-Computer Interaction in Motor Execution Classification (EPTNN-BCI-EEG) is proposed. Initially, the input signals are collected from EEG Dataset. The input signals are preprocessed using Robust M-Type Error-State Kalman Filter (RMESKF) to remove the artifacts and baseline signals. Then, the pre-processed signals are given to Signed Cumulative Distribution Transform (SCDT) for feature extraction. SCDT is used to extract Spatial and temporal features. Afterward, Efficient Predefined Time Adaptive Neural Network (EPTNN) is used to classify the EEG gender and signal, such as Audio/Video and Male/Female. In General, EPTNN does not expose any adoption of optimization systems to determine optimal parameters to classify the EEG gender and signal. Hence, Bitwise Arithmetic Optimization Algorithm (BAOA) is proposed to optimize the EPTNN. The proposed EPTNN-BCI-EEG approach is implemented in MATLAB and the performance metrics, such as Recall, Accuracy, Root Mean Square Error (RMSE), F1-score, Specificity, ROC, Computation time are assessed. The performance of EPTNN-BCI-EEG approach provides 17.82 %, 28.95 %, and 19.2 % higher accuracy, 24.38 %, 34.53 %, and 18.78 % higher recall, 26.7 %, 24.2 %, and 32.7 % higher specificity when analysed with the existing methods.

Visual Electroencephalography Assessment in the Diagnosis and Prognosis of Cognitive Disorders.

Electroencephalography (EEG) is a noninvasive diagnostic tool that can be of diagnostic value in patients with cognitive disorders. In recent years, increasing emphasis has been on quantitative EEG analysis, which is not easily accessible in clinical practice. The aim of this study was to assess the diagnostic and prognostic value of visual EEG assessment to distinguish different causes of cognitive disorders. Patients with cognitive disorders from a specialized memory clinic cohort underwent routine workup including EEG, neuropsychological testing and brain imaging. Electroencephalography parameters including posterior dominant rhythm, background activity, and response to photic stimulation (intermittent photic stimulation) were visually scored. Final diagnosis was made by an expert panel. A total of 501 patients were included and underwent full diagnostic workup. One hundred eighty-three patients had dementia (111 Alzheimer disease, 30 vascular dementia, 15 frontotemporal dementia, and 9 dementia with Lewy bodies), 66 patients were classified as mild cognitive impairment, and in 176, no neurologic diagnosis was made. Electroencephalography was abnormal in 60% to 90% of patients with mild cognitive impairment and dementia, most profoundly in dementia with Lewy bodies and Alzheimer disease, while frontotemporal dementia had normal EEG relatively often. Only 30% of those without neurologic diagnosis had EEG abnormalities, mainly a diminished intermittent photic stimulation response. Odds ratio of conversion to dementia was 6.1 [1.5-24.7] for patients with mild cognitive impairment with abnormal background activity, compared with those with normal EEG. Visual EEG assessment has diagnostic and prognostic value in clinical practice to distinguish patients with memory complaints without underlying neurologic disorder from patients with mild cognitive impairment or dementia.

Study on the classification of sleep stages in EEG signals based on DoubleLinkSleepCLNet.

The classification of sleep stages based on Electroencephalogram (EEG) changes has significant implications for evaluating sleep quality and sleep status. Most polysomnography (PSG) systems have a limited number of channels and do not achieve optimal classification performance due to a paucity of raw data. To leverage the data characteristics and enhance the classification accuracy, we propose and evaluate a novel dual-link deep neural network model, 'DoubleLinkSleepCLNet'. The DoubleLinkSleepCLNet model performs feature extraction and efficient classification on both the raw EEG and the EEG processed with the Hilbert transform. It leverages the frequency domain and time domain feature modules, resulting in superior performance compared to other models. The DoubleLinkSleepCLNet model, using the 2 Raw/2 Hilbert data modes, achieved the highest classification performance with an accuracy of 88.47%. The average accuracy of the EEG was improved by approximately 4.08% after the application of the Hilbert transform. Additionally, Convolutional Neural Network (CNN) demonstrated superior performance in processing phase information, whereas Long Short-Term Memory (LSTM) excelled in handling time series data. The application of the Hilbert transform to EEG data, followed by processing it with a convolutional neural network, enhances the accuracy of the model. These findings introduce novel concepts for accelerating sleep stage prediction research, suggesting potential applications of these methods to other EEG analyses.

Early Burst Suppression Similarity Association with Structural Brain Injury Severity on MRI After Cardiac Arrest.

Identical bursts on electroencephalography (EEG) are considered a specific predictor of poor outcomes in cardiac arrest, but its relationship with structural brain injury severity on magnetic resonance imaging (MRI) is not known. This was a retrospective analysis of clinical, EEG, and MRI data from adult comatose patients after cardiac arrest. Burst similarity in first 72h from the time of return of spontaneous circulation were calculated using dynamic time-warping (DTW) for bursts of equal (i.e., 500ms) and varying (i.e., 100-500ms) lengths and cross-correlation for bursts of equal lengths. Structural brain injury severity was measured using whole brain mean apparent diffusion coefficient (ADC) on MRI. Pearson's correlation coefficients were calculated between mean burst similarity across consecutive 12-24-h time blocks and mean whole brain ADC values. Good outcome was defined as Cerebral Performance Category of 1-2 (i.e., independence for activities of daily living) at the time of hospital discharge. Of 113 patients with cardiac arrest, 45 patients had burst suppression (mean cardiac arrest to MRI time 4.3days). Three study participants with burst suppression had a good outcome. Burst similarity calculated using DTW with bursts of varying lengths was correlated with mean ADC value in the first 36h after cardiac arrest: Pearson's r: 0-12h: -0.69 (p = 0.039), 12-24h: - 0.54 (p = 0.002), 24-36h: - 0.41 (p = 0.049). Burst similarity measured with bursts of equal lengths was not associated with mean ADC value with cross-correlation or DTW, except for DTW at 60-72h (- 0.96, p = 0.04). Burst similarity on EEG after cardiac arrest may be associated with acute brain injury severity on MRI. This association was time dependent when measured using DTW.

Resting State EEG Analysis for Schizophrenia: from Alpha-Rhythm Reduction to Microstates Assessment

Background: due to the emergence of new technologies for analyzing of EEG signal, many new researches in this field have appeared in recent years, including those investigating EEG parameters of schizophrenia. The aim: this publication provides an overview of actual studies on the possibilities of using the assessment of resting state EEG recordings in the diagnostics and prognosis of schizophrenia course. Material and methods: publications were selected in eLibrary, PubMed, Google Scholar and CNKI databases using the keywords: “psychosis”, “schizophrenia”, “EEG”, “resting state”. Methodologically, the atricle is a narrative literature review. Thirty-three sources were selected for analysis. Discussion and conclusion: according to the data available to present date qualitive and quantitative assessment of resting EEGs cannot be used for the instrumental diagnosis of schizophrenia because the most commonly detected increase in the proportion of slow-wave activity is seen in a several disorders. However, some quantitative spectral estimates of resting state EEG could be used to identify poor prognosis response to antipsychotic therapy, as well as for objective assessment of the dynamics of the mental state. Estimation of the power of slow resting EEG rhythms and other methods of assessing the connectivity of different neural networks could be considered as potential markers of the presence of a specific endophenotype. Modern digital technologies, including machine learning and artificial intelligence algorithms, make it possible to identify resting EEG of the schizophrenic patients from healthy controls with accuracy, sensitivity and specificity more than 95%. EEG microstates assessment, which can be used to assess the functioning of large neuronal ensembles, are one of the methods for detecting the endophenotype of schizophrenia.

Insights from EEG analysis of evoked memory recalls using deep learning for emotion charting

Affect recognition in a real-world, less constrained environment is the principal prerequisite of the industrial-level usefulness of this technology. Monitoring the psychological profile using smart, wearable electroencephalogram (EEG) sensors during daily activities without external stimuli, such as memory-induced emotions, is a challenging research gap in emotion recognition. This paper proposed a deep learning framework for improved memory-induced emotion recognition leveraging a combination of 1D-CNN and LSTM as feature extractors integrated with an Extreme Learning Machine (ELM) classifier. The proposed deep learning architecture, combined with the EEG preprocessing, such as the removal of the average baseline signal from each sample and extraction of EEG rhythms (delta, theta, alpha, beta, and gamma), aims to capture repetitive and continuous patterns for memory-induced emotion recognition, underexplored with deep learning techniques. This work has analyzed EEG signals using a wearable, ultra-mobile sports cap while recalling autobiographical emotional memories evoked by affect-denoting words, with self-annotation on the scale of valence and arousal. With extensive experimentation using the same dataset, the proposed framework empirically outperforms existing techniques for the emerging area of memory-induced emotion recognition with an accuracy of 65.6%. The EEG rhythms analysis, such as delta, theta, alpha, beta, and gamma, achieved 65.5%, 52.1%, 65.1%, 64.6%, and 65.0% accuracies for classification with four quadrants of valence and arousal. These results underscore the significant advancement achieved by our proposed method for the real-world environment of memory-induced emotion recognition.

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 the effect of different smells in the indoor built environment (office) on human emotions using a mixed-method approach

Despite numerous studies highlighting the impact of smells on humans, there has been a notable lack of attention to ambient smell within the discipline of architecture. To address the gaps, a systematic methodology was designed to investigate the impact of the indoor-built environment's smell on human emotions. In this experiment, 14 adults (7 women and 7 men) are exposed to the smell of jasmine and rotten orange peel in two identical rooms. The EEG analysis revealed increased theta band power in frontal regions and increased alpha power in frontal and occipital areas when participants were exposed to the jasmine scent. Gamma activity in the frontal regions increases with exposure to the smell of rotten orange peel. Participants reported that the smell of jasmine was pleasant, leading to pleasant emotions, interest, enjoyment and a sleepy state. However, the smell of rotten orange peel led to unpleasant emotions, discomfort and annoyance for participants.

Emergence Electroencephalography in an Unresponsiveness Geriatric Patient in the Postanesthesia Care Unit: A Case Report.

Incomplete neurological awakening manifested as aberrant patterns of electroencephalography (EEG) at emergence may be responsible for an unresponsive patient in the postanesthesia care unit (PACU). We describe a case of an individual who remained unresponsive but awake in the PACU. Retrospective, intraoperative EEG analysis showed low alpha power and a sudden shift from deep delta to arousal preextubation. We explored parallels with diminished motivation disorders and anesthesia-induced sleep paralysis due to imbalances in anesthetic drug sensitivity between brain regions. Our findings highlight the relevance of end-anesthesia EEG patterns in diagnosing delayed awakening.

Fragrance and frequencies: EEG analysis of theta spectral edge frequency after olfactory stimulation by spearmint.

Fragrance and frequencies: EEG analysis of theta spectral edge frequency after olfactory stimulation by spearmint.

Beta-to-Theta Entropy Ratio of EEG in Aging, Frontotemporal Dementia, and Alzheimer's Dementia

Aging, frontotemporal dementia (FTD), and Alzheimer's dementia (AD) manifest electroencephalography (EEG) alterations, particularly in the beta-to-theta power ratio derived from linear power spectral density (PSD). Given the brain's nonlinear nature, the EEG nonlinear features could provide valuable physiological indicators of aging and cognitive impairment. Multiscale dispersion entropy (MDE) serves as a sensitive nonlinear metric for assessing the information content in EEGs across biologically relevant time scales. To compare the MDE-derived beta-to-theta entropy ratio with its PSD-based counterpart to detect differences between healthy young and elderly individuals and between different dementia subtypes. Scalp EEG recordings were obtained from two datasets: 1) Aging dataset: 133 healthy young and 65 healthy older adult individuals; and 2) Dementia dataset: 29 age-matched healthy controls (HC), 23 FTD, and 36 AD participants. The beta-to-theta ratios based on MDE vs. PSD were analyzed for both datasets. Finally, the relationships between cognitive performance and the beta-to-theta ratios were explored in HC, FTD, and AD. In the Aging dataset, older adults had significantly higher beta-to-theta entropy ratios than young individuals. In the Dementia dataset, this ratio outperformed the beta-to-theta PSD approach in distinguishing between HC, FTD, and AD. The AD participants had a significantly lower beta-to-theta entropy ratio than FTD, especially in the temporal region, unlike its corresponding PSD-based ratio. The beta-to-theta entropy ratio correlated significantly with cognitive performance. Our study introduces the beta-to-theta entropy ratio using nonlinear MDE for EEG analysis, highlighting its potential as a sensitive biomarker for aging and cognitive impairment.