{"id":62443,"date":"2024-12-30T11:52:05","date_gmt":"2024-12-30T11:52:05","guid":{"rendered":"https:\/\/biomedpharmajournal.org\/?p=62443"},"modified":"2025-03-07T07:00:06","modified_gmt":"2025-03-07T07:00:06","slug":"a-review-on-the-phenomenon-of-synchronization-in-eeg-signals-of-humans-and-its-application-in-detection-of-neurological-disorders","status":"publish","type":"post","link":"https:\/\/biomedpharmajournal.org\/staging\/vol17no4\/a-review-on-the-phenomenon-of-synchronization-in-eeg-signals-of-humans-and-its-application-in-detection-of-neurological-disorders\/","title":{"rendered":"A Review on the Phenomenon of Synchronization in EEG Signals of Humans and its Application in Detection of Neurological Disorders"},"content":{"rendered":"\n

Introduction<\/strong> <\/p>\n\n\n\n

The exploration of coupled systems began in the seventeenth\ncentury, initially focusing on the investigation of synchronization in\nnonlinear periodic systems. Subsequent studies on synchronization yielded\nvarious discoveries with crucial implications for the design of secure\ncommunication devices. The synchronized chaotic trajectories can be employed to\nencrypt messages and protect them from being deciphered. The notion of complete\nsynchronization of chaotic systems was later generalized, allowing for\nnon-identity among the coupled systems.<\/p>\n\n\n\n

In a later development, Rosenblum4<\/sup> examined a form of\nsynchronization between chaotic oscillators where the associated phases become\nlocked or synchronized while the amplitudes remain uncorrelated. They termed\nthis type of synchronization as “synchronization of phase.” Research\nhas not only demonstrated synchronization among chaotic oscillators such as electronic\ncircuits, lasers, and electrochemical oscillators but also observed\nsynchronization phenomena in biological systems.<\/p>\n\n\n\n

Examples encompass elements within the cardiorespiratory system,\nexpansive biological networks, and the electroencephalographic patterns of individuals\nwith Parkinson’s disease, all displaying synchronization characteristics.\nFigure 1 elucidates the categorization of neurological disorders that are\ngenerally considered by different researchers in their works. Understanding the\ncircumstances in which the coupling of chaotic systems occurs is crucial, as is\nidentifying the moments of coupling. Numerous studies are dedicated to\ninvestigating instances of phase synchronization (PS) and generalized\nsynchronization (GS). Several methodologies have been devised to date for the\nidentification of phase synchronization (PS) and generalized synchronization\n(GS). However, challenges arise when pinpointing the instances of coupling in\nsystems, primarily due to the extremely small-time intervals during which coupling\ntakes place or the specific signal values at which synchronization occurs.<\/p>\n\n\n\n\n\n
\"\"<\/td>\n\n

Figure 1: A Systematic Level Of Neurological\u00a0 Disorders<\/strong><\/p>\n<\/p>\n

Click here to view Figure<\/a><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

The initial concrete endeavour in this field began with the\nconcept put forth by Andreas Groth1<\/sup> in his paper titled\n“Visualization of coupling in time series by recurrence plots.”\nBefore this work, in the exploration of coupled systems, several non-graphical\nstrategies had been developed to identify instances of cooperation in time\nseries2,3,4<\/sup>.<\/p>\n\n\n\n

The methods and\ntechniques proposed in the aforementioned papers address a variety of needs.\nWhile direct methods based on correlations are inadequate for managing\nnonlinear conditions, many nonlinear methods require significantly long,\nstationary time series. In situations where stationarity is maintained only for\nbrief periods, cross recurrence plots (CRPs) have been introduced6,7<\/sup>.  <\/p>\n\n\n\n

The CRP strategy relies on calculating distances of\ntrajectories, which can be particularly challenging in real-time systems. An\noverarching challenge in analyzing multivariate data from real-time systems\nsuch as electroencephalograms (EEG) is that measurement conditions fluctuate\nover time. Among other factors, offsets and amplitude ranges can vary\ndifferently across channels 20,21<\/sup>. To tackle these challenges, we turn to an\ninnovative method that encodes the entire time series into an array of zeros\nand ones. This approach helps mitigate the impact of varying values in the time\nseries due to diverse external factors, as it discretizes the entire series\ninto a pattern of zeros and ones.<\/p>\n\n\n\n

This concept of order patterns was introduced by Bandt and Pompe8<\/sup>,\nwho proposed a straightforward model that quantifies time series values by\ncomparing them with neighbouring values. Subsequently, this method was utilized\nto detect epileptic seizures in patients. Building upon the notion of cross\nrecurrence plots (CRPs), a visualization tool was developed. <\/p>\n\n\n\n

The concept of recurrence\nhas been employed to detect relationships between interacting systems, leading\nto the introduction of synchronization probability. This approach incorporates\na multivariate analysis of aggregated synchronization. Furthermore, recurrence\nhas been used to quantify a weaker form of synchronization known as phase\nsynchronization. In this context, we expand these measures to identify the\ndirection of coupling. The proposed method is relatively straightforward to\ncalculate compared to more complex information-theoretic methods. Additionally,\nit is applicable to both weak and strong directional coupling, as well as to\nnonlinear systems.<\/p>\n\n\n\n

For assessing the direction of coupling, the methods utilized\nare entirely based on the mean conditional probability of recurrence or\ndirectionality, which is computed and based on shared information 24,25<\/sup>.<\/p>\n\n\n\n

In this study, several methods have been compared from various works that estimate the direction of the coupling. Most of these methods can be categorized into the following three groups: (I) Methods Based on a Functional Relationship between the Stages, (ii) State-Space Based Methods and (iii) Data Theory Based Methods.<\/p>\n\n\n\n

Synchronization Behaviour in EEG Signals<\/strong><\/p>\n\n\n\n

Synchronization in electroencephalographic (EEG) signals is crucial for interpreting data recorded from the human brain. The brain governs all activities of the body, and since each activity in the body is synchronized with others, this synchronization can be monitored by analyzing signals recorded from the brain. EEG (Electroencephalography) is highly effective for detecting neurological disorders due to several factors:<\/p>\n\n\n\n

Real-time Brain Activity Monitoring<\/strong><\/p>\n\n\n\n

EEG measures the brain\u2019s electrical signals produced by neuronal activity, allowing clinicians to observe brain function in real time. This capability is crucial for detecting abnormal patterns associated with conditions like epilepsy, seizures, and sleep disorders.<\/p>\n\n\n\n

Non-invasive Technique<\/strong><\/p>\n\n\n\n

As a non-invasive method, EEG doesn’t require surgical intervention or penetration into the body, making it safe for repeated use and less risky for patients.<\/p>\n\n\n\n

High Temporal Resolution<\/strong><\/p>\n\n\n\n

EEG excels in capturing rapid changes in brain activity due to its high temporal resolution. This ability to track short-lived electrical fluctuations is key for identifying transient events, such as epileptic discharges or specific sleep stages.<\/p>\n\n\n\n

Detection of Specific Abnormal Patterns<\/strong><\/p>\n\n\n\n

Various neurological conditions exhibit distinct EEG signatures. For example, epilepsy is often associated with characteristic abnormal discharges, while disorders like encephalopathy, sleep disorders, or brain trauma also show unique EEG patterns.<\/p>\n\n\n\n

Portable and Cost-efficient<\/strong><\/p>\n\n\n\n

Compared to other neuroimaging techniques like fMRI or PET scans, EEG is more affordable and portable, making it accessible for use in diverse clinical settings, including smaller hospitals or outpatient facilities.<\/p>\n\n\n\n

Assessment of Consciousness States<\/strong><\/p>\n\n\n\n

EEG is particularly valuable in evaluating brain activity in unconscious or comatose patients, aiding in the diagnosis of brain function levels in conditions like coma, vegetative states, or brain death.<\/p>\n\n\n\n

Broad Clinical Application<\/strong><\/p>\n\n\n\n

EEG is versatile, used in diagnosing a wide range of neurological conditions beyond epilepsy, including brain tumors, strokes, infections, and neurodegenerative diseases such as Alzheimer’s disease.<\/p>\n\n\n\n

These features make EEG an indispensable tool for\ndiagnosing and understanding various neurological disorders, thanks to its\nreal-time monitoring, accessibility, and ability to detect specific brain\nactivity abnormalities.<\/p>\n\n\n\n

The techniques utilized for brain mapping are contingent upon\neither bivariate measures (BM), which entail averaging across pairwise values,\nor on multivariate measures (MM), which directly assign a singular value to the\nsynchronization within a group.<\/p>\n\n\n\n

To contrast Multivariate Measures (MM) with Bivariate Measures\n(BM), nine distinct estimators were utilized on simulated multivariate time\nseries with known parameters and on actual EEG recordings. The investigation\nunveiled noteworthy correlations between BM and MM 34,35<\/sup>.<\/p>\n\n\n\n

Examining the performance of synchronization measures in\nsimulated scenarios featuring diverse coupling strengths, association probabilities,\nand parameter discrepancies, it was observed that certain measures, such as the\nS-estimator, S-Renyi, omega, and coherence, exhibit higher sensitivity to\ndirect dependencies. On the contrary, additional measures such as mutual\ninformation and phase locking parameters demonstrate reduced sensitivity to\nnonlinear effects.<\/p>\n\n\n\n

These attributes should be taken into account alongside the fact\nthat Multivariate Measures (MM) are computationally less demanding and,\nconsequently, more effective for large-scale time series analysis compared to\nBivariate Measures (BM) in evaluating synchronization within EEG signals 43<\/sup><\/p>\n\n\n\n

Dynamic behaviors and specific spatiotemporal patterns are\nobserved in oscillatory patterns within the alpha and beta bands (<35 Hz)\nduring a range of cognitive, sensory, and motor tasks, as depicted in the work\nof Neuper and Pfurtscheller11<\/sup>. The event-related desynchronization\n(ERD) seen in the alpha band and beta rhythms can be explained as a link\nbetween an activated cortical region and heightened excitability of neurons 12,13\n<\/sup>. <\/p>\n\n\n\n

Additionally,\nthe act of opening one’s eyes usually results in the suppression of alpha\nwaves, whereas alpha power tends to elevate during closed-eye states 50<\/sup>. This latter\nphenomenon is often associated with a decrease in the dynamic processing of\ndata, caused by interruptions in the flow of data from the visual system. The\ninitial discoveries of occipital and frontal alpha synchronization have\nproposed that sudden surges in alpha activity might signify a state of\n“hypofrontality,” where cognitive abilities linked to methodical\nreasoning and critical thinking could be temporarily impaired.<\/p>\n\n\n\n

 Fink27 <\/sup>conducted\nan additional investigation to ascertain whether alpha synchronization during\ninnovative ideation signifies elevated or deteriorated activity of EEG. This\nwas done by employing frontal magnetic resonance imaging method. <\/p>\n\n\n\n

For example, Jensen 19<\/sup> discovered that\nsynchronization in the alpha band (9-12 Hz) increases when individuals are\nrequired to retain information for brief durations. This heightened\nsynchronization in the alpha band can be studied to gain insights into\nmemory-related disorders such as dementia, where patients face challenges in\nmemory retention.<\/p>\n\n\n\n

Similarly, Klimesch12 <\/sup>suggested that alpha band desynchronization\noccurs when individuals engage in mentally demanding tasks.<\/p>\n\n\n\n

Furthermore, Sauseng21 <\/sup>observed synchronized alpha\nband frequencies in EEG signals recorded from frontal areas of brain when it is\ninvolved in any memory retention based task. <\/p>\n\n\n\n

Cooper18 <\/sup>examined the activity in the alpha band of\nEEG signals, particularly in tasks involving sensory processing of visual,\nauditory, and tactile stimuli, as well as tasks requiring mental visualization\nof these stimuli.<\/p>\n\n\n\n

Additionally, internally directed mental imagery tasks result in\nstronger alpha power compared to externally directed tasks. Moreover, alpha\npower increases with greater task demands and complexity.<\/p>\n\n\n\n

Moreover, frontal alpha synchronization is noted during tasks\nrequiring high levels of internal processing in the brain, but not during tasks\nwith low internal processing demands28 <\/sup>.<\/p>\n\n\n\n

Benedek22 <\/sup>investigated synchronized alpha band\nfrequencies in EEG signals when brain is involved in any creative task. They\nconcluded that there is high degree of synchronization in alpha band signals\nwhen brain is involved in different creative tasks. This view was supported by\nVon Stein and Sarnthein 24<\/sup>.<\/p>\n\n\n\n

Drawing from the aforementioned studies, numerous researchers in\nthe field suggest that neurological disorders can be explored and potentially\ndiagnosed by analyzing the synchronization patterns in brain signals across\nvarious frequency bands. Some of these works are summarized in Table 1.<\/p>\n\n\n\n

EEG Alpha Synchronization<\/strong><\/p>\n\n\n\n

Activity variations of various EEG bands have been observed to\ndetect and study cognitive activity and its various aspects. During periods of\nrest, the alpha band frequencies (8\u201312 Hz) become the predominant spectrum of\nEEG, marked by synchronized signals recorded from the brain, where as\nsubstantial deterioration in intensity and synchronism is observed when brain\nis involved with some task i.e. when it is not idle25 <\/sup>.<\/p>\n\n\n\n

ERD Based Synchronization<\/strong><\/p>\n\n\n\n

Investigations utilizing ERD\/ERS reveal a diverse pattern of\nalpha resynchronization observed across the broad alpha frequency band. This\napproach has been recognized through the substantial body of work conducted by\nKlimesch12<\/sup>. Their research revealed that lower alpha ERD is linked\nto general task demands such as attention processes (basic alertness,\nattentiveness, or arousal), while ERD in the upper range of the alpha band can\nindicate specific task requirements. Similarly, the upper alpha frequency band\nhas been identified as particularly responsive to demands associated with\ninsight. As outlined in Neuper and Pfurtscheller11<\/sup>, the\nEvent-Related Desynchronization (ERD) of EEG activity in the alpha band likely\nreflects increased excitability and firing of neurons in the underlying\ncortical areas, which can be associated with an enhanced transfer of\ninformation in thalamo-cortical circuits11<\/sup>.<\/p>\n\n\n\n

On\nthe other hand, Event-Related Synchronization (ERS) of alpha activity is\nbelieved to signify a reduced level of dynamic information processing in the\nunderlying neuronal networks, often referred to as ‘cortical idling’ 13<\/sup>.<\/p>\n\n\n\n

Nonetheless, recent developments in this field of\nstudy also propose that the synchronization phenomenon observed in alpha-based\nactivity is connected to the dynamic execution of cognitive tasks, potentially\ninvolving processes of cognitive control28<\/sup>. <\/p>\n\n\n\n

Data Acquisition of Synchronization Concepts<\/strong><\/p>\n\n\n\n

For the investigation of cortical activity, EEG signals are acquired\nusing an EEG amplifier at a sampling rate of 500 Hz. Gold electrodes (9.1 mm of\ndiameter) are placed on an electrode cap following the standard 10-20 system\nwith spaced positions. A single electrode is positioned on the forehead (Fpz),\nand an orientation electrode is located on the nose.<\/p>\n\n\n\n

The EEG signal is adjusted for ocular artifacts using an\nautomated regression-based method, supplemented by visual inspection to\nidentify any remaining artifacts stemming from eye movements and muscle\ntension. Typically, the calculation of power in various bands of the EEG signal\nemploys a standard Fast Fourier Transform (FFT) applied to time windows lasting\n1000 ms with 900 ms overlap. This process enables the extraction of features\nwithin the upper alpha frequency band (10.5\u201312.5 Hz). Additionally, for\ncomplementary analysis, power computation in the lower alpha band (8.5\u201310.5 Hz)\nis carried out.<\/p>\n\n\n\n

Based on the EEG outcomes and the task-related synchronization of frontal alpha activity, a substantial level of synchronization is observed during top-down processing. Conversely, tasks involving bottom-up processing demonstrate marked desynchronization 24,28,30<\/sup>.<\/p>\n\n\n\n

Detection of Neurological Disorders on the basis of Oscillations<\/strong><\/p>\n\n\n\n

Several investigations suggest that the rise in bilateral\nfrontal alpha activity observed during a standardized test for divergent\nthinking is connected with enhanced creativity. This discovery presents the\nprimary direct evidence for the functional significance of alpha oscillations\nin creative ideation.<\/p>\n\n\n\n

A notable consequence of oscillations in the alpha band of EEG\nsignals during imaginative thinking is cortical idling. Previous research has\nindicated that alpha band oscillations may indicate reduced mental activity, as\na decline in alpha power is commonly observed during brain activations in\ntasks. Therefore, the increase in alpha power in the frontal cortex is\nsuggested to represent a hypoactive state of this brain region, termed\n“hypofrontality,” which in turn may lead to enhanced creativity.<\/p>\n\n\n\n

However, current research suggests that creativity is an active\ncognitive process rather than an outcome of decreased activity in the frontal\ncortex. Several studies have shown a decrease in alpha power during various\nother demanding cognitive tasks16<\/sup>.<\/p>\n\n\n\n

More specifically, creative ideation involves internal thought\nprocesses combined with an inhibitory cognitive control mechanism 79,80<\/sup>. This\nmechanism acts to shield the internal process from potential disruption caused\nby incoming, attention-grabbing, but ultimately irrelevant stimuli 31,32<\/sup>.<\/p>\n\n\n\n

Hence, the amplified alpha activity triggered by frontal 10Hz-\ntranscranial alternating current stimulation (tACS) could boost the top-down\nmanagement of internal processes, thereby aiding in improved creative ideation 81<\/sup>.<\/p>\n\n\n\n

 A consolidated chart summarizing the\nsignificant recent works by various experts is presented in Table 1.<\/p>\n\n\n\n

Table 1: A systematic contribution chart of experts<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

S. No.<\/strong><\/p>\n<\/td>\n

\n

Experts<\/strong><\/p>\n<\/td>\n

\n

Year<\/strong><\/p>\n<\/td>\n

\n

Contributions<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

1.\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Mitchell D. Woodbright 33<\/sup><\/p>\n<\/td>\n

\n

2024<\/p>\n<\/td>\n

\n

They proposed a feature extraction method from the EEG signals to predict neurological disorders. Deep learning concepts have been utilized to acquire visualizations of the predictions.\u00a0<\/p>\n<\/td>\n<\/tr>\n

\n

2.\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Goel, S34<\/sup><\/p>\n<\/td>\n

\n

2024<\/p>\n<\/td>\n

\n

Transformation of recorded EEG signals into recurrence graphs has been the main the main focus here. The features have been extracted from the recurrence graphs for detection of disorders. Principal Component Analysis has been used for extracting the features, which has resulted in reduction of computational steps.<\/p>\n<\/td>\n<\/tr>\n

\n

3.\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Ali, L. 35<\/sup><\/p>\n<\/td>\n

\n

2023<\/p>\n<\/td>\n

\n

This paper has proposed a new and efficient feature extraction method with the help of deep neural network and has also compared its performance with the contemporary works.<\/p>\n<\/td>\n<\/tr>\n

\n

4.\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Singh, A. K 36<\/sup><\/p>\n<\/td>\n

\n

2023<\/p>\n<\/td>\n

\n

This works has facilitated the pipeline design for the analysis of signals recorded from brain. For this purpose, extensive use of artificial intelligence and machine learning has been advocated.<\/p>\n<\/td>\n<\/tr>\n

\n

5.\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Kidwai M.S37<\/sup><\/p>\n<\/td>\n

\n

2022<\/p>\n<\/td>\n

\n

Proposed an algorithm that is based on Order Recurrence Plots (ORPs) and Machine Learning, for \u00a0the detection of neurological disorders. Has also compared the performance of the proposed algorithm with the contemporary works and the performance of the proposed algorithm has been much better in terms of specifity, precision and other parameters.<\/p>\n<\/td>\n<\/tr>\n

\n

6.\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Lima 38<\/sup><\/p>\n<\/td>\n

\n

2022<\/p>\n<\/td>\n

\n

This study is focussed on reviewing various Machine Learning based\u00a0 signal conditioning techniques for acquired EEG signals and has compared and analyzed their performances.<\/p>\n<\/td>\n<\/tr>\n

\n

7.\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Xie, Q 39<\/sup><\/p>\n<\/td>\n

\n

2021<\/p>\n<\/td>\n

\n

They had utilized dynamic functional connectivity network for feature extraction from EEG signals for the efficient diagnosis of neurological disorders.<\/p>\n<\/td>\n<\/tr>\n

\n

8.\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Vandana, J.40<\/sup><\/p>\n<\/td>\n

\n

2021<\/p>\n<\/td>\n

\n

Provided an up-to-date comprehensive overview of the research focused on utilizing machine learning techniques to diagnose bruxism,epilepsy and dementia.<\/p>\n<\/td>\n<\/tr>\n

\n

9.\u00a0\u00a0\u00a0 <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Raghavendra, U 41<\/sup><\/p>\n<\/td>\n

\n

2020<\/p>\n<\/td>\n

\n

Offered a contemporary survey of research spanning the previous two decades on the automated detection of epilepsy and Bruxism by emphasizing on analysis of physiological signals and images.<\/p>\n<\/td>\n<\/tr>\n

\n

10. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Wanzeng Kong 42<\/sup><\/p>\n<\/td>\n

\n

2019<\/p>\n<\/td>\n

\n

Employed synchronization in the phase of recorded EEG data for the analysis of signals and for finding the reason of epileptic seizures in patients.<\/p>\n<\/td>\n<\/tr>\n

\n

11. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Miaolin Fan 43<\/sup>.<\/p>\n<\/td>\n

\n

2019<\/p>\n<\/td>\n

\n

Investigated the spatial-temporal synchronization patterns within the brains of epileptic individuals by utilizing spectral graph theoretic features extracted from scalp EEG data.<\/p>\n<\/td>\n<\/tr>\n

\n

12. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Anwesha Sengupta44<\/sup><\/p>\n<\/td>\n

\n

2018<\/p>\n<\/td>\n

\n

Has discussed a specific method to acquire data from EEG machine so that it can be analyzed effectively for detection of neurological disorders.<\/p>\n<\/td>\n<\/tr>\n

\n

13. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

L. Moumdjian 45<\/sup><\/p>\n<\/td>\n

\n

2018<\/p>\n<\/td>\n

\n

Observed what effect does the auditory stimulus has on the EEG signals that are already synchronized in the patients of neurological disorders.<\/p>\n<\/td>\n<\/tr>\n

\n

14. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Marila Rezende\u00a0Azevedo46<\/sup><\/p>\n<\/td>\n

\n

2018<\/p>\n<\/td>\n

\n

Employed the neuronal groups for analyzing the changes in EEG signals of a patient having sleep bruxism.<\/p>\n<\/td>\n<\/tr>\n

\n

15. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Alotaiby47<\/sup><\/p>\n<\/td>\n

\n

2018<\/p>\n<\/td>\n

\n

Outlined the signaling pathways associated with neurological disorders.<\/p>\n<\/td>\n<\/tr>\n

\n

16. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Li S. 49<\/sup><\/p>\n<\/td>\n

\n

2018<\/p>\n<\/td>\n

\n

Introduced the concept of network synchronization with periodic coupling<\/p>\n<\/td>\n<\/tr>\n

\n

17. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Notbohm 50<\/sup><\/p>\n<\/td>\n

\n

2016<\/p>\n<\/td>\n

\n

Studied the effect of light as a stimulus on the EEG signals.<\/p>\n<\/td>\n<\/tr>\n

\n

18. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Oleksandr Popovych51<\/sup><\/p>\n<\/td>\n

\n

2014<\/p>\n<\/td>\n

\n

Explored methods to counteract abnormal neuronal synchronization through invasive and non-invasive brain stimulation techniques.<\/p>\n<\/td>\n<\/tr>\n

\n

19. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Lialiana 52<\/sup><\/p>\n<\/td>\n

\n

2013<\/p>\n<\/td>\n

\n

Designed a brain-computer interface that depends on the elevated correlation levels among EEG signals.<\/p>\n<\/td>\n<\/tr>\n

\n

20. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Milan Br\u00e1zdil 53<\/sup><\/p>\n<\/td>\n

\n

2013<\/p>\n<\/td>\n

\n

Employed synchronization patterns to investigate cortical activity in response to stimuli.<\/p>\n<\/td>\n<\/tr>\n

\n

21. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Lai Y M54<\/sup><\/p>\n<\/td>\n

\n

2013<\/p>\n<\/td>\n

\n

Outlined the distinctions between clustering, de-synchronization, and synchronization in EEG signal states.<\/p>\n<\/td>\n<\/tr>\n

\n

22. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Akam 55<\/sup><\/p>\n<\/td>\n

\n

2012<\/p>\n<\/td>\n

\n

Described a method to study EEG signal states through the oscillatory dynamic techniques.<\/p>\n<\/td>\n<\/tr>\n

\n

23. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Lehnertz56<\/sup><\/p>\n<\/td>\n

\n

2011<\/p>\n<\/td>\n

\n

Provided fundamental terminology regarding neurophysiological signals.<\/p>\n<\/td>\n<\/tr>\n

\n

24. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Katharine Brigham57<\/sup><\/p>\n<\/td>\n

\n

2010<\/p>\n<\/td>\n

\n

Utilized synchronization in EEG signals to decode an individual’s thoughts.<\/p>\n<\/td>\n<\/tr>\n

\n

25. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Ermentrout 58<\/sup><\/p>\n<\/td>\n

\n

2010<\/p>\n<\/td>\n

\n

Has discussed about the Neuroscience empirically.<\/p>\n<\/td>\n<\/tr>\n

\n

26. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Schroeder59<\/sup><\/p>\n<\/td>\n

\n

2009<\/p>\n<\/td>\n

\n

Discussed how neuronal oscillations can be instrumental in detecting various disorders in humans.<\/p>\n<\/td>\n<\/tr>\n

\n

27. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Velazquez 60<\/sup><\/p>\n<\/td>\n

\n

2007<\/p>\n<\/td>\n

\n

Focused on activity in EEG signal states during epileptic seizure in patients.<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

Table 2 exclusively focuses on few significant works that have\nexamined one or more neurological disorders for their study and detection.<\/p>\n\n\n\n

Table 2: List of few main neurological disorders that have been studied along with the researchers\u2019 names.<\/strong><\/p>\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
\n

Sl. No.<\/strong><\/p>\n<\/td>\n

\n

Researchers<\/strong><\/p>\n<\/td>\n

\n

Year<\/strong><\/p>\n<\/td>\n

\n

Parkinson disease<\/strong><\/p>\n<\/td>\n

\n

Epilepsy<\/strong><\/p>\n<\/td>\n

\n

Bruxism<\/strong><\/p>\n<\/td>\n

\n

Hearing Loss<\/strong><\/p>\n<\/td>\n

\n

Schizophrenia<\/strong><\/p>\n<\/td>\n

\n

Stroke<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

1.<\/strong><\/p>\n<\/td>\n

\n

Woodbright, M. D 33<\/sup><\/p>\n<\/td>\n

\n

2024<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

2.<\/strong><\/p>\n<\/td>\n

\n

Goel, S.34<\/sup><\/p>\n<\/td>\n

\n

2024<\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

3.<\/strong><\/p>\n<\/td>\n

\n

Gulay61<\/sup><\/p>\n<\/td>\n

\n

2023<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

4.<\/strong><\/p>\n<\/td>\n

\n

Tawhid62<\/sup><\/p>\n<\/td>\n

\n

2023<\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

5.<\/strong><\/p>\n<\/td>\n

\n

Alalayah, K. M. 63<\/sup><\/p>\n<\/td>\n

\n

2023<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

6.<\/strong><\/p>\n<\/td>\n

\n

Mary, G. 64<\/sup><\/p>\n<\/td>\n

\n

2022<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

7.<\/strong><\/p>\n<\/td>\n

\n

Lima, A. A.65<\/sup><\/p>\n<\/td>\n

\n

2022<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

8.\u00a0<\/strong><\/p>\n<\/td>\n

\n

Saravanan, N. P.66<\/sup><\/p>\n<\/td>\n

\n

2021<\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

9.\u00a0<\/strong><\/p>\n<\/td>\n

\n

Boonyakitanont, P. 67<\/sup>.<\/p>\n<\/td>\n

\n

2020<\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

10. <\/strong>\u00a0<\/strong><\/p>\n<\/td>\n

\n

Raghavendra, U.41<\/sup><\/p>\n<\/td>\n

\n

2020<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

11.<\/strong><\/p>\n<\/td>\n

\n

Logroscino 68<\/sup><\/p>\n<\/td>\n

\n

2019<\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

12.<\/strong><\/p>\n<\/td>\n

\n

Yannick 69<\/sup><\/p>\n<\/td>\n

\n

2019<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

13.<\/strong><\/p>\n<\/td>\n

\n

Kidwai 70<\/sup><\/p>\n<\/td>\n

\n

2019<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

14.<\/strong><\/p>\n<\/td>\n

\n

Acharya 71<\/sup><\/p>\n<\/td>\n

\n

2018<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

15.<\/strong><\/p>\n<\/td>\n

\n

Kidwai 72<\/sup><\/p>\n<\/td>\n

\n

2017<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

16.<\/strong><\/p>\n<\/td>\n

\n

Uva73<\/sup><\/p>\n<\/td>\n

\n

2015<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

17.<\/strong><\/p>\n<\/td>\n

\n

Kumar, Y 74<\/sup><\/p>\n<\/td>\n

\n

2014<\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

18.<\/strong><\/p>\n<\/td>\n

\n

Jiruska, P 75<\/sup><\/p>\n<\/td>\n

\n

2013<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

19.<\/strong><\/p>\n<\/td>\n

\n

Kumar, S. P76<\/sup><\/p>\n<\/td>\n

\n

2010<\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

20.<\/strong><\/p>\n<\/td>\n

\n

Wirrell E 77<\/sup><\/p>\n<\/td>\n

\n

2008<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

21.<\/strong><\/p>\n<\/td>\n

\n

Loddenkemper T 78<\/sup><\/p>\n<\/td>\n

\n

2007<\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

22.<\/strong><\/p>\n<\/td>\n

\n

Wirrell E 79<\/sup><\/p>\n<\/td>\n

\n

2006<\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u221a<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n

\n

\u00a0<\/strong><\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n\n\n

The current state-of-the-art techniques for acquiring EEG signals and using them to detect neurological disorders involve significant advancements in both hardware and software, including improvements in signal acquisition, processing, machine learning, and brain-computer interfaces (BCIs). These developments have enhanced the precision, usability, and clinical effectiveness of EEG in diagnosing neurological conditions. The current state-of-the-art techniques for EEG signal acquisition and analysis have been significantly advanced through high-density EEG, portable systems, sophisticated signal processing, and the application of AI and machine learning. These innovations have greatly enhanced the accuracy, accessibility, and real-time capabilities of EEG in detecting and diagnosing neurological disorders, ranging from epilepsy and Parkinson\u2019s disease to Alzheimer\u2019s and autism spectrum disorders.<\/p>\n\n\n\n

Conclusion<\/strong><\/p>\n\n\n\n

This paper has discussed the presence of synchronization among\nbio-signals generated in the brain, and has highlighted its significance in the\nstudy and analysis of the brain through relevant and recent research findings. It is evident from the relevant literature that\nseveral authors have discovered that various brain activities can be examined\nby observing the synchronization patterns in EEG signals, with changes in these\npatterns observed when the brain responds to specific stimuli. Furthermore,\nexisting research by scientists and doctors suggests that the correlation\nbetween neuronal groups can also serve as a means to detect various\nneurological disorders in humans. The desynchronization patterns of EEG\nsignals can also be utilized to investigate cortical activity82,83<\/sup>. From the\nexisting literature, it is also apparent that there are numerous techniques\navailable for detecting various neurological disorders. However, there is a\nresearch gap in the development of a versatile and simple technique that can\ndetect seizure-based neurological disorders with minimal or no alteration to\nits approach84,85<\/sup>.<\/p>\n\n\n\n

The synchronization phenomenon in EEG signals has been widely\nemployed in the study of brain activities and for the detection of neurological\ndisorders. However, different parameters and approaches are utilized for\ndetecting various neurological disorders. Additionally, experts have proposed\nvarious theories to observe changes in EEG signal synchronization using\ngraphical methods. Yet, there has been limited work in developing a method that\nquantifies synchronization to facilitate brain study. Therefore, there is\npotential for developing a single-feature-based technique that can specifically\ndetect seizure-based neurological disorders. <\/p>\n\n\n\n

Acknowledgement<\/strong><\/p>\n\n\n\n

The authors acknowledge the contribution of Integral University, India and Dhofar University, Oman for providing them with the resources and the conducive environment to carry out their research and publish this paper.<\/p>\n\n\n\n

Funding Sources<\/strong><\/p>\n\n\n\n

The author(s) received no financial support for the research, authorship, and\/or publication of this article.<\/p>\n\n\n\n

Conflict of Interest<\/strong><\/p>\n\n\n\n

The author(s) do not have any conflict of interest.<\/p>\n\n\n\n

Data Availability Statement<\/strong><\/p>\n\n\n\n

This statement does not apply to this article.<\/p>\n\n\n\n

Ethics Statement<\/strong><\/p>\n\n\n\n

This research did not involve human participants, animal subjects, or any material that requires ethical approval.<\/p>\n\n\n\n

Informed Consent Statement<\/strong><\/p>\n\n\n\n

This study did not involve human participants, and therefore, informed consent was not required.<\/p>\n\n\n\n

Clinical Trial Registration<\/strong><\/p>\n\n\n\n

This\nresearch does not involve any clinical trials<\/p>\n\n\n\n

Author contributions<\/strong><\/p>\n\n\n\n

Mohd. Suhaib Kidwai: Conceptualization and writing the original draft.<\/p>\n\n\n\n

Mohd. Maroof Siddiqui: Arranging the literature review of the related works in chronological and tabular form, editing and proofreading.<\/p>\n\n\n\n

References<\/strong><\/p>\n\n\n\n

    \n
  1. Groth A. Visualization of coupling in time series by order recurrence plots. Phys Rev E. 2005;72(4):046220.
    CrossRef<\/a><\/li>\n\n\n\n
  2. Kantz H, Olbrich E. Scalar observations from a class of high-dimensional chaotic systems: limitations of the time delay embedding. Chaos. 1997;7(3):423-429.
     CrossRef <\/a><\/li>\n\n\n\n
  3. Brockwell PJ, Mitchell H. Linear prediction for a class of multivariate stable processes. Stochastic Models. 1998;14(1-2):297-310.
     CrossRef <\/a><\/li>\n\n\n\n
  4. Rosenblum M, Pikovsky A. Synchronization: from pendulum clocks to chaotic lasers and chemical oscillators. Contemp Phys. 2003;44(5):401-416.
    CrossRef <\/a><\/li>\n\n\n\n
  5. Schreiber T. Measuring information transfer. Phys Rev Lett. 2000;85(2):461.
     CrossRef <\/a><\/li>\n\n\n\n
  6. Zbilut JP, Giuliani A, Webber CL Jr. Recurrence quantification analysis and principal components in the detection of short complex signals. Phys Lett A. 1998;237(3):131-136.
    CrossRef <\/a><\/li>\n\n\n\n
  7. Marwan N, Kurths J. Nonlinear analysis of bivariate data with cross recurrence plots. Phys Lett A. 2002;302(5-6):299-307.
     CrossRef <\/a><\/li>\n\n\n\n
  8. Bandt C, Pompe B. Permutation entropy: a natural complexity measure for time series. Phys Rev Lett. 2002;88(17):174102.
     CrossRef <\/a><\/li>\n\n\n\n
  9. Groth A. Visualization of coupling in time series by order recurrence plots. Phys Rev E. 2005;72(4):046220.
    CrossRef <\/a><\/li>\n\n\n\n
  10. Schreiber T. Measuring information transfer. Phys Rev Lett. 2000;85(2):461.
    CrossRef <\/a><\/li>\n\n\n\n
  11. Neuper C, Pfurtscheller G. Event-related dynamics of cortical rhythms: frequency-specific features and functional correlates. Int J Psychophysiol. 2001;43(1):41-58.
     CrossRef <\/a><\/li>\n\n\n\n
  12. Klimesch W. EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis. Brain Res Rev. 1999;29(2-3):169-195.
     CrossRef <\/a><\/li>\n\n\n\n
  13. Pfurtscheller G, Da Silva FL. Event-related EEG\/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842-1857.
     CrossRef <\/a><\/li>\n\n\n\n
  14. Dietrich A. Functional neuroanatomy of altered states of consciousness: the transient hypofrontality hypothesis. Conscious Cogn. 2003;12(2):231-256.
     CrossRef <\/a><\/li>\n\n\n\n
  15. Fink A, Neubauer AC. EEG alpha oscillations during the performance of verbal creativity tasks: differential effects of sex and verbal intelligence. Int J Psychophysiol. 2006;62(1):46-53.
    CrossRef <\/a><\/li>\n\n\n\n
  16. Buzsaki G, Draguhn A. Neuronal oscillations in cortical networks. Science. 2004;304(5679):1926-1929.
    CrossRef <\/a><\/li>\n\n\n\n
  17. Ward LM. Synchronous neural oscillations and cognitive processes. Trends Cogn Sci. 2003;7(12):553-559.
     CrossRef <\/a><\/li>\n\n\n\n
  18. Cooper NR, Croft RJ, Dominey SJ, Burgess AP, Gruzelier JH. Paradox lost? Exploring the role of alpha oscillations during externally vs. internally directed attention and the implications for idling and inhibition hypotheses. Int J Psychophysiol. 2003;47(1):65-74.
     CrossRef <\/a><\/li>\n\n\n\n
  19. Jensen O, Gelfand J, Kounios J, Lisman JE. Oscillations in the alpha band (9\u201312 Hz) increase with memory load during retention in a short-term memory task. Cereb Cortex. 2002;12(8):877-882.
     CrossRef <\/a><\/li>\n\n\n\n
  20. Sauseng P, Klimesch W, Doppelmayr M, Pecherstorfer T, Freunberger R, Hanslmayr S. EEG alpha synchronization and functional coupling during top\u2010down processing in a working memory task. Hum Brain Mapp. 2005;26(2):148-155.
     CrossRef <\/a><\/li>\n\n\n\n
  21. Benedek M, Bergner S, K\u00f6nen T, Fink A, Neubauer AC. EEG alpha synchronization is related to top-down processing in convergent and divergent thinking. Neuropsychologia. 2011;49(12):3505-3511.
    CrossRef <\/a><\/li>\n\n\n\n
  22. Knyazev GG. Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neurosci Biobehav Rev. 2007;31(3):377-395.
     CrossRef <\/a><\/li>\n\n\n\n
  23. Von Stein A, Sarnthein J. Different frequencies for different scales of cortical integration: from local gamma to long-range alpha\/theta synchronization. Int J Psychophysiol. 2000;38(3):301-313.
    CrossRef <\/a><\/li>\n\n\n\n
  24. Neuper C, Klimesch W, eds. Event-Related Dynamics of Brain Oscillations. Vol. 159. Elsevier; 2006.<\/li>\n\n\n\n
  25. Pfurtscheller G, Da Silva FL. Event-related EEG\/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol. 1999;110(11):1842-1857.
     CrossRef <\/a><\/li>\n\n\n\n
  26. Fink A, Graif B, Neubauer AC. Brain correlates underlying creative thinking: EEG alpha activity in professional vs. novice dancers. NeuroImage. 2009;46(3):854-862.
     CrossRef <\/a><\/li>\n\n\n\n
  27. Klimesch W, Sauseng P, Hanslmayr S, Gruber W, Freunberger R. Event-related phase reorganization may explain evoked neural dynamics. Neurosci Biobehav Rev. 2007;31(7):1003-1016.
     CrossRef <\/a><\/li>\n\n\n\n
  28. Von Stein A, Sarnthein J. Different frequencies for different scales of cortical integration: from local gamma to long-range alpha\/theta synchronization. Int J Psychophysiol. 2000;38(3):301-313.
     CrossRef <\/a><\/li>\n\n\n\n
  29. Buschman TJ, Miller EK. Top-down versus bottom-up control of attention in the prefrontal and posterior parietal cortices. Science. 2007;315(5820):1860-1862.
     CrossRef <\/a><\/li>\n\n\n\n
  30. Benedek M, Franz F, Heene M, Neubauer AC. Differential effects of cognitive inhibition and intelligence on creativity. Pers Individ Differ. 2012;53(4):480-485.
     CrossRef <\/a><\/li>\n\n\n\n
  31. Fink A, Benedek M. EEG alpha power and creative ideation. Neurosci Biobehav Rev. 2014;44:111-123.
    CrossRef <\/a><\/li>\n\n\n\n
  32. Woodbright MD, Morshed A, Browne M, Ray B, Moore S. Towards Transparent AI for Neurological Disorders: A Feature Extraction and Relevance Analysis Framework. IEEE Access. 2024.
     CrossRef <\/a><\/li>\n\n\n\n
  33. Goel S, Agrawal R, Bharti RK. Automated detection of epileptic EEG signals using recurrence plots-based feature extraction with transfer learning. Soft Comput. 2024;28(3):2367-2383.
    CrossRef <\/a><\/li>\n\n\n\n
  34. Ali L, Chakraborty C, He Z. A novel sample and feature dependent ensemble approach for Parkinson\u2019s disease detection. Neural Comput Appl. 2023;35(22):15997-16010.
    CrossRef <\/a><\/li>\n\n\n\n
  35. Singh AK, Krishnan S. Trends in EEG signal feature extraction applications. Front Artif Intell. 2023;5:1072801.
     CrossRef <\/a><\/li>\n\n\n\n
  36. Kidwai MS, Siddiqui MM. Computer-based techniques for detecting the neurological disorders. In: Pervasive Healthcare: A Compendium of Critical Factors for Success. 2022:185-205.
     CrossRef <\/a><\/li>\n\n\n\n
  37. Lima AA, Mridha MF, Das SC. A comprehensive survey on the detection, classification, and challenges of neurological disorders. Biol (Basel). 2022;11(3):469.
     CrossRef <\/a><\/li>\n\n\n\n
  38. Xie Q, Zhang X, Rekik I. Constructing high-order functional connectivity network based on central moment features for diagnosis of autism spectrum disorder. Peer J. 2021;9.
     CrossRef <\/a><\/li>\n\n\n\n
  39. Vandana J, Nirali N. A review of EEG signal analysis for diagnosis of neurological disorders using machine learning. J Biomed Photonics Eng. 2021;7(4):40201.<\/li>\n\n\n\n
  40. Raghavendra U, Acharya UR, Adeli H. Artificial intelligence techniques for automated diagnosis of neurological disorders. Eur Neurol. 2020;82(1-3):41-64.
     CrossRef <\/a><\/li>\n\n\n\n
  41. Kong W, Wang L, Xu S. EEG fingerprints: phase synchronization of EEG signals as biomarker for subject identification. IEEE Access. 2019;7:121165-121173.
    CrossRef <\/a><\/li>\n\n\n\n
  42. Fan M, Chou CA, Yen SC, Lin Y. A network-based multimodal data fusion approach for characterizing dynamic multimodal physiological patterns. arXiv preprint arXiv:1901.00877. 2019.<\/li>\n\n\n\n
  43. Sengupta A. Alertness Assessment using Brain Signals [dissertation]. IIT Kharagpur; 2018. <\/li>\n\n\n\n
  44. Moumdjian L, Buhmann J, Willems I. Entrainment and synchronization to auditory stimuli during walking in healthy and neurological populations: a methodological systematic review. Front Hum Neurosci. 2018;12:263.
    CrossRef <\/a><\/li>\n\n\n\n
  45. Azevedo MR, Sena R, Freitas AM. Neuro-behavioral pattern of sleep bruxism in wakefulness. Res Biomed Eng. 2018;34:41-52.
     CrossRef <\/a><\/li>\n\n\n\n
  46. Alotaiby TN, Alshebeili SA, Alshawi T. EEG signal processing for epilepsy diagnosis: a comprehensive review. Neural Computer Appl. 2017;28(5):1043-1074.<\/li>\n\n\n\n
  47. Nguyen AT, Nguyen TT, Tran DS. An accurate and robust EEG-based emotion recognition method using dynamic graph convolutional neural networks. Comput Biol Med. 2017;87:127-136.<\/li>\n\n\n\n
  48. Li S, Cha SH, Tappert CC. Biometric distinctiveness of brain signals based on EEG. In: 2018 IEEE 9th International Conference on Biometrics Theory, Applications and Systems (BTAS). IEEE; 2018:1-6.
     CrossRef <\/a><\/li>\n\n\n\n
  49. Notbohm A, Kurths J, Herrmann CS. Modification of brain oscillations via rhythmic light stimulation provides evidence for entrainment but not for superposition of event-related responses. Front Hum Neurosci. 2016;10:10.
     CrossRef<\/a> <\/li>\n\n\n\n
  50. Popovych OV, Tass PA. Control of abnormal synchronization in neurological disorders. Front Neurol. 2014;5:100270.
     CrossRef <\/a><\/li>\n\n\n\n
  51. Liliana M, Adrian SM. The role of attention in the achievement of sport performance in judo. Procedia-Soc Behav Sci. 2013;84:1242-1249.
     CrossRef <\/a><\/li>\n\n\n\n
  52. Br\u00e1zdil M, Jane\u010dek J, Klime\u0161 P. On the time course of synchronization patterns of neuronal discharges in the human brain during cognitive tasks. PLoS One. 2013;8(5)
     CrossRef <\/a><\/li>\n\n\n\n
  53. Lai YM, Porter MA. Noise-induced synchronization, desynchronization, and clustering in globally coupled nonidentical oscillators. Phys Rev E. 2013;88(1):012905.
     CrossRef <\/a><\/li>\n\n\n\n
  54. Akam T, Oren I, Mantoan L, Ferenczi E, Kullmann DM. Oscillatory dynamics in the hippocampus support dentate gyrus\u2013CA3 coupling. Nat Neurosci. 2012;15(5):763-768.
     CrossRef <\/a><\/li>\n\n\n\n
  55. Lehnertz K. Assessing directed interactions from neurophysiological signals\u2014an overview. Physiol Meas. 2011;32(11):1715.
    CrossRef <\/a><\/li>\n\n\n\n
  56. Brigham K, Kumar BV. Subject identification from electroencephalogram (EEG) signals during imagined speech. In: 2010 Fourth IEEE International Conference on Biometrics: Theory, Applications and Systems (BTAS). IEEE; 2010:1-8.
     CrossRef <\/a><\/li>\n\n\n\n
  57. Ermentrout B, Terman DH. Mathematical Foundations of Neuroscience. Vol 35. Springer; 2010:331-367.
     CrossRef <\/a><\/li>\n\n\n\n
  58. Schroeder CE, Lakatos P. The gamma oscillation: master or slave? Brain Topogr. 2009;22:24-26.
     CrossRef <\/a><\/li>\n\n\n\n
  59. Velazquez JL, Huo JZ, Dominguez LG, Leshchenko Y, Snead OC III. Typical versus atypical absence seizures: network mechanisms of the spread of paroxysms. Epilepsia. 2007;48(8):1585-1593.<\/li>\n\n\n\n
  60. Gulay BK, Demirel N, Vahaplar A, Guducu C. A novel feature extraction method using chemosensory EEG for Parkinson’s disease classification. Biomed Signal Process Control. 2023;79:104147.
    CrossRef <\/a><\/li>\n\n\n\n
  61. Tawhid MNA, Siuly S, Wang K, Wang H. Automatic and efficient framework for identifying multiple neurological disorders from EEG signals. IEEE Trans Technol Soc. 2023;4(1):76-86.
     CrossRef <\/a><\/li>\n\n\n\n
  62. Alalayah KM, Senan EM, Atlam HF, Ahmed IA, Shatnawi HSA. Automatic and early detection of Parkinson\u2019s disease by analyzing acoustic signals using classification algorithms based on recursive feature elimination method. Diagnostics. 2023;13(11):1924.
     CrossRef <\/a><\/li>\n\n\n\n
  63. Mary G, Suganthi N. Detection of Parkinson’s Disease with Multiple Feature Extraction Models and Darknet CNN Classification. Comput Syst Sci Eng. 2022;43(1).
     CrossRef <\/a><\/li>\n\n\n\n
  64. de Almeida WF, de Moraes Lima CA, Peres SM. A systematic mapping of feature extraction and feature selection methods of electroencephalogram signals for neurological diseases diagnostic assistance. IEEE Lat Am Trans. 2021;19(5):735-745.
     CrossRef <\/a><\/li>\n\n\n\n
  65. Saravanan NP, Thamilselvan R, Loheswaran K. Prediction of neurological disorder using deep learning network. Oxid Commun. 2021;44(1).<\/li>\n\n\n\n
  66. Boonyakitanont P, Lek-Uthai A, Chomtho K, Songsiri J. A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. Biomed Signal Process Control. 2020;57:101702.
     CrossRef <\/a><\/li>\n\n\n\n
  67. Logroscino G, Piccininni M. Amyotrophic lateral sclerosis descriptive epidemiology: the origin of geographic difference. Neuroepidemiology. 2019;52(1-2):93-103.
    CrossRef <\/a><\/li>\n\n\n\n
  68. B\u00e9jot Y, Bailly H, Graber M. Impact of the ageing population on the burden of stroke: the Dijon stroke registry. Neuroepidemiology. 2019;52(1-2):78-85.
     CrossRef <\/a><\/li>\n\n\n\n
  69. Kidwai MS, Saeed SH. A novel approach for detection of neurological disorders through electrical potential developed in brain. Int J Electr Comput Eng. 2019;9(4):2751-2759.
     CrossRef <\/a><\/li>\n\n\n\n
  70. Acharya UR, Hagiwara Y, Adeli H. Automated seizure prediction. Epilepsy Behav. 2018;88:251-261.
     CrossRef <\/a><\/li>\n\n\n\n
  71. Kidwai MS, Saeed SH. A novel approach to study the effects of anesthesia on respiratory signals by using the EEG signals. Int J Electr Comput Eng. 2017;6(2):117-122.
     CrossRef <\/a><\/li>\n\n\n\n
  72. Uva L, Breschi GL, Gnatkovsky V, Taverna S, de Curtis M. Synchronous inhibitory potentials precede seizure-like events in acute models of focal limbic seizures. J Neurosci. 2015;35(7):3048-3055.
     CrossRef <\/a><\/li>\n\n\n\n
  73. Kumar Y, Dewal ML, Anand RS. Epileptic seizure detection using DWT-based fuzzy approximate entropy and support vector machine. Neurocomputing. 2014;133:271-279.
     CrossRef <\/a><\/li>\n\n\n\n
  74. Jiruska P, De Curtis M, Jefferys JG . Synchronization and desynchronization in epilepsy: controversies and hypotheses. J Physiol. 2013;591(4):787-797.
    CrossRef <\/a><\/li>\n\n\n\n
  75. Kumar SP, Sriraam N, Benakop PG, Jinaga BC. Entropies based detection of epileptic seizures with artificial neural network classifiers. Expert Syst Appl. 2010;37(4):3284-3291.
    CrossRef <\/a><\/li>\n\n\n\n
  76. Wirrell E, Sherman EM, Vanmastrigt R, Hamiwka L. Deterioration in cognitive function in children with benign epilepsy of childhood with central temporal spikes treated with sulthiame. J Child Neurol. 2008;23(1):14-21.
    CrossRef <\/a><\/li>\n\n\n\n
  77. Loddenkemper T, Holland KD, Stanford LD. Developmental outcome after epilepsy surgery in infancy. Pediatrics. 2007;119(5):930-935.
     CrossRef <\/a><\/li>\n\n\n\n
  78. Wirrell EC. Epilepsy\u2010related injuries. Epilepsia. 2006;47:79-86.
     CrossRef <\/a><\/li>\n\n\n\n
  79. Nour, M., Senturk, U., & Polat, K. (2024). A novel hybrid model in the diagnosis and classification of Alzheimer’s disease using EEG signals: Deep ensemble learning (DEL) approach.\u00a0Biomedical Signal Processing and Control<\/em>,\u00a089<\/em>, 105751.
    CrossRef <\/a><\/li>\n\n\n\n
  80. Siddiqui MM, Srivastava G, Saeed SH. Diagnosis of sleep disorders using EEG signal. Saarbr\u00fccken, Germany: LAP LAMBERT Academic Publishing; 2019.<\/li>\n\n\n\n
  81. Siddiqui MM, Jain R, Kidwai MS, Khan MZ. Recording of EEG signals and role in diagnosis of sleep disorder. Biomed Pharmacol J. 2022;15(3).
     CrossRef <\/a><\/li>\n\n\n\n
  82. Siddiqui MM. Digitalize the system to diagnosis of neurological disorder (sleep disorder). In: 2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE); February 2024. IEEE; 2024:1-6.
    CrossRef <\/a><\/li>\n\n\n\n
  83. Sharma, R., & Meena, H. K. (2024). Emerging Trends in EEG Signal Processing: A Systematic Review.\u00a0SN Computer Science<\/em>,\u00a05<\/em>(4), 1-14.
     CrossRef <\/a><\/li>\n\n\n\n
  84. Jui, S. J. J., Deo, R. C., Barua, P. D., Devi, A., Soar, J., & Acharya, U. R. (2023). Application of entropy for automated detection of neurological disorders with electroencephalogram signals: a review of the last decade (2012-2022).\u00a0IEEE Access<\/em>.
     CrossRef <\/a><\/li>\n\n\n\n
  85. Marsicano, G., Bertini, C., & Ronconi, L. (2024). Decoding cognition in neurodevelopmental, psychiatric and neurological conditions with multivariate pattern analysis of EEG data.\u00a0Neuroscience & Biobehavioral Reviews<\/em>, 105795.
    CrossRef <\/a><\/li>\n<\/ol>\n","protected":false},"excerpt":{"rendered":"

    Introduction The exploration of coupled systems began in the seventeenth […]<\/p>\n","protected":false},"author":15,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[119],"tags":[],"class_list":["post-62443","post","type-post","status-publish","format-standard","hentry","category-vol17no4"],"_links":{"self":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/62443","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/users\/15"}],"replies":[{"embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/comments?post=62443"}],"version-history":[{"count":5,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/62443\/revisions"}],"predecessor-version":[{"id":64640,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/posts\/62443\/revisions\/64640"}],"wp:attachment":[{"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/media?parent=62443"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/categories?post=62443"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/biomedpharmajournal.org\/staging\/wp-json\/wp\/v2\/tags?post=62443"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}