EEG Waves Research Articles

Combined LinkNet-MBi-LSTM for brain activity recognition with new Stockwell transform features.

Recognizing brain activity from EEG waves is an important field of study in biomedical engineering and neuroscience. Conventional approaches usually begin with signal processing techniques to extract features from the EEG data, and then machine learning algorithms are applied to classify the data. However, the spatial resolution of these EEG signals is low, which makes it difficult to pinpoint the exact location of the neural activity source in the brain. There are ongoing initiatives to use DL-based brain activity recognition algorithms to overcome these constraints. Therefore, this work presents a novel hybrid framework for brain activity detection using the enhanced Stockwell transform and an EEG signal that is called LinkNet and modified bidirectional-long short-term memory (LN-MBi-LSTM) model. This framework follows a methodical approach that includes stages for feature extraction, brain activity recognition and preprocessing. Firstly, the improved Weiner filtering (IWF) approach is used to preprocess the EEG input signal. The relevant features are then extracted using a feature extraction technique from the preprocessed EEG signal. To identify the brain activity, these recovered feature sets are subsequently processed separately using LinkNet and modified bidirectional-long short-term memory (MBi-LSTM). A thorough analysis that takes into account both simulation and experimental calculations is part of the validation process for the LN-MBi-LSTM model. Finally, this study demonstrates the therapeutic potential of the LN-MBi-LSTM framework by presenting a strong and verified model for brain activity recognition. With the highest precision of 0.997, the LinkNet-MBi-LSTM model distinguishes itself from other models and confirms its exceptional capacity to produce accurate positive predictions.

Comparative exploration on EEG signal filtering using window control methods

Electroencephalography (EEG) is used to monitor brain activity. The brain signals consist of different frequency band signals delta, theta, alpha, beta, and gamma waves. The signals are affected by external noise which reduces the quality of the EEG signal due to which it becomes difficult to do further processing of EEG signals like feature extraction or extraction of meaningful features from EEG signal. Therefore, it becomes important to filter the noise from the EEG signal before feature extraction or classification of the EEG signal. The research article presents an overview of different types of windowing filter techniques like Rectangular, Bartlett, Hamming, Hanning, and Kaiser windows applied for finite impulse response (FIR) behavior which is used for EEG signal processing for different brain waves processed in different frequency bands. The comparative analysis is carried out in terms of the response time of brain frequency bands for different windowing filter techniques using the MATLAB 2023 signal processing simulation tool. The novelty of the work lies in estimating minimum latency and appropriate filter selection for various typical EEG waves, since the EEG signals are pre-supposed in the hardware chip design, noise elimination is the first step in high-performance computing applications. The Bartlett window band stop has an optimal response time of 12.666 s for delta waves, a highpass filter with a response time of 16.187 s for theta waves, a bandpass with a response time of 13.122 s for alpha waves, a highpass filter with a response time of 17.866 s for beta waves, and a highpass filter with a response time of 13.797 s for gamma waves. The Barlett window FIR filter is well-suited for EEG applications.

Feasibility Assessment of Rapid Response EEG in the Identification of Nonconvulsive Seizures During Military Medical Air Transport.

Traumatic brain injury often requires neurologic care and specialized equipment, not often found downrange. Nonconvulsive seizures (NCSs) and nonconvulsive status epilepticus (NCSE) occur in up to 30% of patients with moderate or severe traumatic brain injury and is associated with a 39% morbidity and an 18% mortality. It remains difficult to identify at bedside because of the heterogeneous clinical manifestations. The primary diagnostic tool is an electroencephalogram (EEG) which is large, requires an external power source, and requires a specialized technician and neurologist to collect and interpret the data. Rapid response EEG (rr-EEG) is an FDA-approved device that is pocket sized and battery powered and uses a disposable 10-electrode headset. Prior studies have demonstrated the noninferiority of rr-EEG in the identification of NCSE and NCS as compared to conventional EEG in hospitals. An unanswered question is whether rr-EEG could be used in the identification of NCSE and NCS by medics. In conjunction with the Critical Care Air Transport (CCAT) team, a simulation was created and implemented on a CCAT training mission. The simulation team included a neurology resident, who oversaw the simulation, a pulmonary critical care fellow, an intensive care unit nurse, and a respiratory therapy. A survey was provided before and after the simulation. The team was expected to review the rr-EEG to make clinical decisions during ground transport, takeoff, and landing. The neurology resident monitored and recorded the team's ability to distinguish between NCS and a normal EEG. In between, the neurology resident monitored the quality of the EEG for potential interference and loss of quality. The CCAT team was able to efficiently set up the rr-EEG on a patient manikin, correctly identify visual EEG wave forms of a patient in NCS, and utilize the proprietary audio program of a simulated patient in NCS. The team reported that the device was easily set up in the environment, and the data were interpretable despite vibration, aircraft auditory and electrical noise, and the ergonomics of the aircraft medical section. This pilot study has validated a potentially revolutionary technology in medical transport. The rr-EEG technology is measurably user-friendly and will improve patient outcomes. This device and simulation can reduce time to an EEG by hours to days allowing for immediate treatment and intervention, which can significantly reduce morbidity and mortality.

Analysis of the therapeutic effect of pestle needle and EEG biofeedback and methylphenidate in the treatment of attention-deficit/hyperactivity disorder.

This research aimed to analyze the therapeutic effect of the pestle needle combined with electroencephalogram (EEG) biofeedback and methylphenidate in the treatment of attention-deficit/hyperactivity disorder (ADHD) in children. Seventy-eight children with ADHD were selected and randomized into a control group and an observation group (n = 39 each). The control group received EEG biofeedback and methylphenidate treatment, whereas the observation group received pestle needle therapy on this basis. Both groups received continuous treatment for 3 mo. The clinical efficacy, scores of Conners Parents Symptom Questionnaire (PSQ), Integrated Visual and Auditory Continuous Performance Test (IVA-CPT), and Pittsburgh Sleep Quality Index (PSQI), EEG θ/β changes in values, serum indicators such as adrenocorticotropic hormone (ACTH) and cortisol (CORT), and incidence of adverse reactions were compared in the two groups. The total effective rate of the observation group was 92.31% (36/39), which was higher than the control group's 69.23% (27/39) (P < 0.05). After treatment, reduced PSQ scores, PSQI scores, EEG θ/β values, and ACTH levels and elevated IVA-CPT and CORT levels were observed in both groups; the observation group had the best improvement effect after treatment (P < 0.05). Pestle needle combined with EEG biofeedback and methylphenidate in the treatment of ADHD children can elevate the IVA-CPT score, improve EEG waves and sleep quality, regulate serum indicators such as ACTH and CORT, reduce behavioral problem scores, and have high efficacy and safety.NEW & NOTEWORTHY The pestle needle combined with EEG biofeedback and methylphenidate in ADHD children can elevate the IVA-CPT score. The pestle needle combined with EEG biofeedback and methylphenidate in ADHD children can improve EEG waves. The pestle needle combined with EEG biofeedback and methylphenidate in ADHD children can improve sleep quality. The pestle needle combined with EEG biofeedback and methylphenidate in ADHD children can regulate serum indicators.

Detection of Alcoholic EEG signal using LASSO regression with metaheuristics algorithms based LSTM and enhanced artificial neural network classification algorithms

The world has a higher count of death rates as a result of Alcohol consumption. Identification is possible because Alcoholic EEG waves have a certain behavior that is totally different compared to the non-alcoholic individual. The available approaches take longer to provide the feedback because they analyze the data manually. For this reason, in the present paper we propose a novel approach applied to detect alcoholic EEG signals automatically by using deep learning methods. Our strategy has advantages as far as fast detection is concerned; hence people can help immediately when there is a need. The potential for a significant decrease in deaths from alcohol poisoning and improvement to public health is presented by this advancement. In order to create clusters and classify the alcoholic EEG signals, this research uses a cascaded process. To begin with, an initial clustering and feature extraction is done by LASSO regression. After that, a variety of meta-heuristics algorithms like Particle Swarm Optimization (PSO), Binary Coding Harmony Search (BCHS) as well as Binary Dragonfly Algorithm (BDA) are employed for feature minimization. When this method is used, normal and alcoholic EEG signals may be differentiated using non-linear features. PSO, BCHS, and BDA features allow for estimation of statistical parameters through t-test, Friedman statistic test, Mann-Whitney U test, and Z-Score with corresponding p-values for alcoholic EEG signals. Lastly, classification is done by the use of support vector machines (SVM) (including linear, polynomial, and Gaussian kernels), random forests, artificial neural networks (ANN), enhanced artificial neural networks (EANN), and LSTM models. Results showed that LASSO regression with BDA-based EANN proposed classifier have a classification accuracy of 99.59%, indicating that our method is highly accurate at classifying alcoholic EEG signals.

Low-Cost Classroom and Laboratory Exercises for Investigating Both Wave and Event-Related Electroencephalogram Potentials.

Electroencephalography (EEG) has given rise to a myriad of new discoveries over the last 90 years. EEG is a noninvasive technique that has revealed insights into the spatial and temporal processing of brain activity over many neuroscience disciplines, including sensory, motor, sleep, and memory formation. Most undergraduate students, however, lack laboratory access to EEG recording equipment or the skills to perform an experiment independently. Here, we provide easy-to-follow instructions to measure both wave and event-related EEG potentials using a portable, low-cost amplifier (Backyard Brains, Ann Arbor, MI) that connects to smartphones and PCs, independent of their operating system. Using open-source software (SpikeRecorder) and analysis tools (Python, Google Colaboratory), we demonstrate tractable and robust laboratory exercises for students to gain insights into the scientific method and discover multidisciplinary neuroscience research. We developed 2 laboratory exercises and ran them on participants within our research lab (N = 17, development group). In our first protocol, we analyzed power differences in the alpha band (8-13 Hz) when participants alternated between eyes open and eyes closed states (n = 137 transitions). We could robustly see an increase of over 50% in 59 (43%) of our sessions, suggesting this would make a reliable introductory experiment. Next, we describe an exercise that uses a SpikerBox to evoke an event-related potential (ERP) during an auditory oddball task. This experiment measures the average EEG potential elicited during an auditory presentation of either a highly predictable ("standard") or low-probability ("oddball") tone. Across all sessions in the development group (n=81), we found that 64% (n=52) showed a significant peak in the standard response window for P300 with an average peak latency of 442ms. Finally, we tested the auditory oddball task in a university classroom setting. In 66% of the sessions (n=30), a clear P300 was shown, and these signals were significantly above chance when compared to a Monte Carlo simulation. These laboratory exercises cover the two methods of analysis (frequency power and ERP), which are routinely used in neurology diagnostics, brain-machine interfaces, and neurofeedback therapy. Arming students with these methods and analysis techniques will enable them to investigate this laboratory exercise's variants or test their own hypotheses.

Delta waves as a sign of cortical plasticity after full-face transplantation

This study focused on detecting the reflections of healing and change in cortex activation in full-face transplantation and lesions patients on EEG activity. Face transplant patients have facial lesions before transplantation and, to identify pre-face transplant patients' brain activity in the absence of pre-transplant recordings, we used data obtained from pre-transplant facial lesion patients. Ten healthy, four facial lesion and three full-face transplant patients participated in this study. EEG data recorded for four different sensory stimuli (brush from the right face, right hand, left face, and left-hand regions) were analyzed using wavelet packet transform method. EEG waves were analyzed for standard bands. Our findings indicate significant change in the 2–4 Hz frequency range which may be a result of ongoing or previous cortical reorganization for face lesion and transplant patients. Alterations of the delta wave seen in patients with facial lesion and face transplant can also be explained by the intense central plasticity. Our findings show that the delta band differences might be used as a marker in the evaluation of post-transplant cortical plasticity in the future.

Brain wave modulation and EEG power changes during auditory beats stimulation

Auditory beats stimulation (ABS) has received increased attention for its potential to modulate neural oscillations through a phenomenon described as brain entrainment (i.e synchronization of brain’s electrocortical activity to external stimuli at a specific frequency). Recently, a new form of ABS has emerged, inspired by isochronic tones stimulation (ITd).This study investigated neural oscillatory responses induced by ITd in comparison with formerly well-established ABS protocols, such as gamma-binaural beats (BB) and white noise (WN).We recorded the electroencephalographic brain activity in 28 participants during 4 min of BB, ITd, and WN presentation.Data demonstrated that while both BB and WN enhanced oscillatory power on the EEG gamma band, consistently with the expected brain entrainment effect, ITd yielded greater changes in EEG power (p < 0.001). This was confirmed by time-based analysis, which showed a progressive increase in normalized EEG power within the ITd window compared to BB (p < 0.05). Findings also revealed that ITd elicited acute changes in the alpha band of EEG oscillations, through a progressive decrease in power over time, which was distinctly different from the pattern observed while listening BB and WN. Such dual alpha-gamma effects underline the promising and unique potential of ITd to modulate neural oscillations which selectively differ from BB and WN.This study contributes to the evolution of ABS research, highlighting the promise of ITd for cognitive enhancement and clinical applications.

A Creativity Monitoring Device (CMD) for New Insight into the Brain Mechanisms of Artistic, Scientific and Engineering Creative Acts

This paper introduces the basic design of a novel neuroscientific device named Creativity Monitoring Device, CMD, able to provide new insights into the brain mechanisms of artistic, scientific and engineering creative acts. The novelty of the device is threefold. First, it allows the artifact-free recording of association cortical EEG waves during such creative acts as painting artistic visions, composing music, running novel experiments in a laboratory or working on an engineering invention. Second, it allows the synchronous monitoring of the sounds and visual events in the environment of such actions along with the artist’s, scientist’s or engineer’s verbal and non-verbal interactions with this environment and his or her own notes on the engaged creative phase. Third, it allows the offline analysis of the synchronously collected EEG and audio-visual data with the new method of dynamic neurocombinatorics, which can reveal at 2 msec accuracy the relationships between the recorded objects of 5 sets: (1) the spatial occurrence codes of the recorded association cortical EEG waves; (2) the complexity codes of each of the recorded 0.2 - 200 Hz EEG waves; (3) the codes of the synchronously recorded environmental events including those of the subject’s verbal and non-verbal interactions with this environment; (4) the subject’s own dictated notes identifying the phase of the engaged creative act, and (5) the time-course of these continuously recorded objects within each of the indicated sets over the entire, at least 10-hour, creativity monitoring period. Thus, CMD studies should reveal the key electrical brain changes underlying the initiation, maintenance and termination of creative acts and should show the similarities and differences between artistic, scientific and engineering creativities.

Optimizing Ambiance: Intelligent RGB Lighting Control in Structures Using Fuzzy Logic

Managing red–green–blue (RGB) lighting conditions within structures may evoke emotions and positively influence behavior. Intelligent RGB lighting systems based on environmental data measurements can substantially enhance the perception of comfort. This study presents a challenge that requires a holistic and integrated approach to implement an automatic RGB artificial lighting control system that can be utilized in various structures and indoor environments. Initially, the challenge spans the identification of environmental variables directly impacting comfort up to the careful selection of suitable sensors. The result is the development of a sophisticated and autonomous system that can adjust RGB lighting in real time, creating environments that are both comfortable and energy-efficient. This automated system fosters the creation of appropriate atmospheres across different contexts. The identification and monitoring of environmental variables are achieved through a neuro-fuzzy control mechanism, where fuzzy rules and membership functions are defined based on late positive potential timings and the influence of artificial lighting on human emotions. The outcomes from this study are an interconnected system capable of performing both online and offline operations to enable the monitoring of environmental variables and the efficient management of artificial lighting based on these metrics. A pilot study, with reference to an EEG wave registry system, yielded significant results. These tests had a statistically relevant result with an average frequency of approximately 9.8 Hz, indicative of a state of comfort among people. Despite a 10% deviation margin, 87% of measurements during the test remained consistent. This research study contributes to human behavior by fostering a relaxing environment and enabling a reduction in energy consumption through the use of efficient lighting. Moreover, the environment intention enables the creation of stimuli in three emotional states: activation, relaxation, and neutral, allowing behavioral adaptation to an intention to occur automatically in fluctuating environmental conditions.

Comparative analysis of EEG in patients with schizophrenia receiving various atypical antipsychotics

Aim. To conduct a comprehensive analysis of EEG recordings of schizophrenia patients receiving atypical antipsychotics as monotherapy.Materials and methods. We examined 94 patients with schizophrenia aged 33 [28; 40] years with a disease duration of 10 [4; 15] years. The patients were divided into 5 groups depending on the antipsychotic drugs they took: 1) risperidone – 31 patients; 2) quetiapine – 20 patients; 3) aripiprazole – 11 patients; 4) olanzapine – 13 patients; 5) clozapine – 19 patients. EEG was recorded during wakefulness with closed eyes (background test), 3-minute hyperventilation, and rhythmic photostimulation in all patients. To describe and interpret the received recordings, the EEG classification according to J. Micoulaud – Franchi et al. was used.Results. EEG modifications (score &gt; 1A) were observed in 61.7% (n = 58) of patients. In the group of patients receiving risperidone, EEG modifications were found in 48.4% of cases, in patients taking quetiapine – in 70% of cases, aripiprazole – in 63.6% of cases, olanzapine – in 61.5% of cases, clozapine – in 73.7% of cases. The frequency of epileptiform patterns in patients receiving olanzapine was significantly higher than in those taking risperidone (p = 0.033) and clozapine (p = 0.032). Slowing in the EEG (score &gt; 1) was more often observed in patients taking clozapine – 63.2% (n = 12), olanzapine – 61.5% (n = 8), and quetiapine – 60% (n = 12). Slower EEG waves were less common in patients receiving aripiprazole – 45.5% (n = 5) and risperidone – 45.2% (n = 14). In the group of patients with EEG slowing (score &gt; 1), the dose of chlorpromazine equivalent was significantly greater compared to patients with normal EEG (p = 0.00046).Conclusion. The data obtained demonstrate changes in EEG parameters during monotherapy with atypical antipsychotics and indicate their dose-dependent effect on the bioelectrical activity of the brain.

MOTION-STIMULATED EEG RECOGNITION BY YOLO DEEP NETWORK

In order to elucidate the relationship between sportsman motion awareness and brain waves from a physiological perspective, research was conducted on the recognition of motion-stimulated brain waves. A motion-stimulated electroencephalogram (EEG) recognition method based on the You Only Look Once (YOLO) deep network is proposed to address the high complexity, high interference, strong randomness, and strong time-varying characteristics of EEG signals. Under the YOLO deep network framework, replacing the Efficient Layer Aggregation Networks-A (ELAN-A) module with the Convolution-Next (CvNX) module achieves the goal of lightweight processing of the YOLO network. Furthermore, the Simple Attention Mechanism (SimAM) attention mechanism is introduced, and a special loss function is set. During the experiment, the short-time Fourier transform was used to convert the EEG signals of the test subjects into time-frequency EEG images with rich features, which can be used as inputs for the YOLO network. The experimental results show that our method achieves recognition accuracy of over 90% and the highest of over 95% in the recognition of five types of motion-induced EEG waves. Compared to CNN and traditional YOLO methods, our method has 3–5% improved with recognition accuracy.

Impact of NMDA receptors block versus GABA-A receptors modulation on synaptic plasticity and brain electrical activity in metabolic syndrome

PurposeMetabolic syndrome (MetS) is a common disorder associated with disturbed neurotransmitter homeostasis. Memantine, an N-methyl-d-aspartate receptor (NMDAR) antagonist, was first used in Alzheimer's disease. Allopregnanolone (Allo), a potent positive allosteric modulator of the Gamma-Amino-Butyric Acid (GABA)-A receptors, decreases in neurodegenerative diseases. The study investigated the impact of Memantine versus Allo administration on the animal model of MetS to clarify whether the mechanism of abnormalities is related more to excitatory or inhibitory neurotransmitter dysfunction. Materials and methodsFifty-six male rats were allocated into 7 groups: 4 control groups, 1 MetS group, and 2 treated MetS groups. They underwent assessment of cognition-related behavior by open field and forced swimming tests, electroencephalogram (EEG) recording, serum markers confirming the establishment of MetS model and hippocampal Glial Fibrillary Acidic Protein (GFAP) and Brain-Derived Neurotrophic Factor (BDNF). ResultsAllo improved anxiety-like behavior and decreased grooming frequency compared to Memantine. Both drugs increased GFAP and BDNF expression, improving synaptic plasticity and cognition-related behaviors. The therapeutic effect of Allo was more beneficial regarding lipid profile and anxiety. We reported progressive slowing of EEG waves in the MetS group with Memantine and Allo treatment with increased relative theta and decreased relative delta rhythms. ConclusionsBoth Allo and Memantine boosted the outcome parameters in the animal model of MetS. Allo markedly improved the anxiety-like behavior in the form of significantly decreased grooming frequency compared to the Memantine-treated groups. Both drugs were associated with increased hippocampal GFAP and BDNF expression, indicating an improvement in synaptic plasticity and so, cognition-related behaviors.

EEG Frequency Correlates with α2-Receptor Density in Parkinson's Disease.

Increased theta and delta power and decreased alpha and beta power, measured with quantitative electroencephalography (EEG), have been demonstrated to have utility for predicting the development of dementia in patients with Parkinson's disease (PD). Noradrenaline modulates cortical activity and optimizes cognitive processes. We claim that the loss of noradrenaline may explain cognitive impairment and the pathological slowing of EEG waves. Here, we test the relationship between the number of noradrenergic α2 adrenoceptors and changes in the spectral EEG ratio in patients with PD. We included nineteen patients with PD and thirteen healthy control (HC) subjects in the study. We used positron emission tomography (PET) with [11C]yohimbine to quantify α2 adrenoceptor density. We used EEG power in the delta (δ, 1.5-3.9 Hz), theta (θ, 4-7.9 Hz), alpha (α, 8-12.9 Hz) and beta (β, 13-30 Hz) bands in regression analyses to test the relationships between α2 adrenoceptor density and EEG band power. PD patients had higher power in the theta and delta bands compared to the HC volunteers. Patients' theta band power was inversely correlated with α2 adrenoceptor density in the frontal cortex. In the HC subjects, age was correlated with, and occipital background rhythm frequency (BRF) was inversely correlated with, α2 adrenoceptor density in the frontal cortex, while occipital BRF was inversely correlated with α2 adrenoceptor density in the thalamus. The findings support the claim that the loss or dysfunction of noradrenergic neurotransmission may relate to the parallel processes of cognitive decline and EEG slowing.

A Newly Defined Electromagnetic Dural Armor Functioned as a Brain Protecting Cerebrosphere: A Preliminary Theoretical Analysis

Background: Electric and magnetic field-generating systems must be insulated in order to maintain their balance. It is certain that the brain, which has a very intense electric and magnetic field, is insulated by the dura mater and cerebrospinal fluid (CSF) that surround it. In this article, the electrophysical properties of these structures will be postulated in accordance with the laws of mathematics and physics.&#x0D; Material and Methods: In human samples, on the other hand, the morphological features of EEG waves were examined with parameters such as the number of scalp hairs and scalp thickness, conductivity, skull thickness, ratios between cranial and brain volumes, and the thickness of the subarachnoid space where CSF circulates, and ventricular volumes. Since this study is postulative, the data were not detailed by statistical evaluation.&#x0D; Results: With the geometric shapes of EEG waves; scalp thickness and number of hairs, skull thickness, depth of subarachnoid space, ventricular volumes, thickness of dura mater. EEG artifacts were excessive in pediatric cases with closed fontanelles or in adults with bone defects. There were statistically varying safety limits between 0.05

RECSAE: An interactive model of relevance cognitive load, spatial memory, ADHD and EEG for special educators and mental health professionals

The cognitive load (CL) increases as the brain is confronted with high-volume audiovisual and other multimodal information from the external environment. Spatial memory is a broad term for recording and recovery of this information. It has three dimensions, such as sensory memory, which is responsible for information capture and preprocessing, working memory, which is responsible for information processing and output generation, and long-term memory, which is responsible for registration/encoding and retrieval/decoding of what has been learned after the information processing. Cognitive training for long-term memory formation is a complex task that is conducted by analysing the "relevance" of the information. Relevance refers to the degree of meaningfulness of the input information based on logic, beliefs and perceptions. A high degree of relevance promotes the firing of the reentrant circuit for capturing similar information to create memory in the brain. An electroencephalogram (EEG) measures brain activities by using electrodes to deliver waveform signals through the scalp at a particular duration. This article proposes an interactive RECSAE model for the formation of relevant long-term memory through CL training and attempts to corroborate it with the EEG signals. The model is finally explained and correlated with Attention Deficit Hyperactive Disorder (ADHD) as a real-world use case. The proposed model is generic and would help psychologists, neurophysiologists, EEG researchers and special educators to evaluate CL training and memory dysfunctions by using EEG waves.

Corticomuscular Coupling Analysis in Stroke Rehabilitation Based on Variational Mode Decomposition-Transfer Entropy.

This study aims to explore alterations in corticomuscular and cortical coupling during the rehabilitation of stroke patients. We initiated the analysis by employing variational modal decomposition (VMD) on electromyography (EMG) data, followed by the application of VDM-transfer entropy (VMD-TE) to quantify the coupling strength between electroencephalogram (EEG) and EMG signals. Subsequently, we constructed the VMD-TE connection matrix and analyzed the clustering coefficient and small-world attributes within the cortico-muscular functional network (CMFN). Finally, a random forest algorithm was employed to extract features from the VMD-TE connection matrix across different rehabilitation periods. Beta waves in EEG were emerged as the key information carrier between the cortex and muscle, and the CMFN of patients with the beta frequency band has small-world characteristics. During rehabilitation, we observed a decrease in coupling between the initially affected motor cortex and muscle, accompanied by an increase in coupling between the frontal region and muscle. Our findings suggest potential neuro-remodeling in stroke patients after rehabilitation, with CFMN serving as a valuable metric for assessing cortico-muscular coupling.

Effects of CPAP treatment on electroencephalographic activity in patients with obstructive sleep apnea syndrome during deep sleep: Preliminary findings of a cross-sectional study.

Study objectives: To investigate whether electroencephalographic (EEG) activities during non-rapid eye movement sleep stage 3 (N3) in obstructive sleep apnea syndrome (OSAS) patients were changed with continuous positive airway pressure (CPAP) treatment.Methods: A cross-sectional study of EEG activity during N3 sleep was conducted in 15 patients with moderate to severe OSAS without and with CPAP treatment compared to 15 normal controls. The amplitude, and absolute and relative power of delta, theta, alpha and beta waves as well as the absolute power ratio of slow to fast EEG waves (i.e., absolute power of delta and theta waves/absolute power of alpha and beta waves) and the spectral power density of 0-30Hz EEG activities were analyzed.Results: CPAP significantly increased N3 sleep, the absolute and relative powers, amplitudes of delta and theta waves, and absolute power ratio of slow to fast EEG waves, but decreased relative alpha and beta powers during N3 sleep. However, there were no significant differences in those parameters between the OSAS patients with CPAP treatment and normal controls.Conclusions: CPAP prolongs N3 sleep and increases the power and amplitude of slow EEG waves during N3 sleep, which indicates an improvement in sleep quality and further provides evidence for recommendation of CPAP treatment for OSAS patients.

Detection of interictal epileptiform discharge in focal seizures

Introduction: Many factors underlying basic epileptic conditions determine the characteristics of epileptic seizures and the therapeutic outcome. Visual obvious abnormalities in resting baseline EEG are cardinal but incompletely understood like feature of seizure onset zone in focal epilepsy and interictal epileptiform discharge. Present study is an attempt to diagnose that evidence of epileptic discharge in temporal lobe epilepsy (TLE), would persist during interictal period in absence of abnormalities in baseline EEG, which could increase the impact of automatic analysis of EEG waves for clinical relevance.&#x0D; Material and Methods: By using 10-20 system, functional connectivity was estimated in the 20 channels of delta, theta, alpha, beta and gamma frequency bands of EEG, from 16 diagnosed focal epileptic seizure patients and 16 age and sex matched controls. To observe the dynamics of the healthy brain, differ from the brain of dynamically focal epileptic patients during interictal period treated with anti-epileptic drugs in the context of resting state during eye close session of EEG. Such differences can be observed by using absolute spectral power from BESS ((Brain Electro Scan Software) of the Axxonet System and statistically measure by applied unpaired student t -test.&#x0D; Result: The high significance results in slow frequency EEG waves (delta and theta) in power spectral analysis were observed that demonstrates the potential epileptic discharge occurring during interictal period without visible pathological activity for helping in the diagnosis and lateralization of TLE.&#x0D; Conclusion: The detailed spectral analysis of EEG waves offers novel insight into focal epileptic patients when visually EEG findings were normal. This linear analysis helpful in extracting information from EEG signals in diagnosing specific neuronal correlates for TLE. Present findings concluded that epileptic discharges occur at different topographical regions of brain during interictal period and power spectral analysis plays a new insight in diagnosis of focal discharge.

AI Oriented System to Identify Type of Epilepsy Seizure Using EEG Wave

For practical purposes, over the past three decades, several methods have been devised to mitigate various anomalies within corrupted EEG data. Nonetheless, there is still no universally acclaimed technique, rendering the field of study both intriguing and challenging. This research delves comprehensively into the identification and elimination of artifacts originating from ocular, muscular, and cardiac sources, offering a detailed analysis of their respective advantages and drawbacks. Furthermore, this investigation encompasses the methodologies employed for comparing real EEG data with simulated counterparts, serving to authenticate their efficacy. The primary focus of this work is twofold: firstly, to establish standardized criteria for the validation of recorded EEG signals in forthcoming studies, and secondly, to amalgamate diverse techniques through multiple processing stages, thereby facilitating the effective eradication of interference stemming from artifacts.