Computer-Aided Tinnitus Detection based on Brain Network Analysis of EEG Functional Connectivity

Mohagheghian, Fahimeh; Makkiabadi, Bahador; Jalilvand, Hamid; Khajehpoor, Hassan; Samadzadehaghdam, Nasser; Eqlimi, Ehsan; Deevband, Mohammad Reza

doi:10.31661/jbpe.v0i0.937

Cited by 20 publications

(12 citation statements)

References 41 publications

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“…Then, with a moving window and a peak-to-peak threshold all parts, which exceeded ±100 μv were removed. The preprocessed data, containing the least amount of artifacts, was segmented into 5-second trials (24 trials, totally 120 sec) which were in the range of other resting-state EEG studies [21, 28–30].…”

Section: Methodsmentioning

confidence: 99%

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Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

et al. 2019

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This study aimed to examine the effects of chronic methamphetamine use on the topological organization of whole-brain functional connectivity network (FCN) by reconstruction of neural-activity time series at resting-state. The EEG of 36 individuals with methamphetamine use disorder (IWMUD) and 24 normal controls (NCs) were recorded, pre-processed and source-reconstructed using standardized low-resolution tomography (sLORETA). The brain FCNs of participants were constructed and between-group differences in network topological properties were investigated using graph theoretical analysis. IWMUD showed decreased characteristic path length, increased clustering coefficient and small-world index at delta and gamma frequency bands compared to NCs. Moreover, abnormal changes in inter-regional connectivity and network hubs were observed in all the frequency bands. The results suggest that the IWMUD and NCs have distinct FCNs at all the frequency bands, particularly at the delta and gamma bands, in which deviated small-world brain topology was found in IWMUD.

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Section: Methodsmentioning

confidence: 99%

“…Previous studies reported that the small-world topology abnormally alters in many disease e.g. major depressive disorder[17], Parkinson's disease [18], Alzheimer’s disease [19], schizophrenia and tinnitus [20, 21], opioids (heroin) and methamphetamine abuse [13, 22], Cirrhosis [23], cognitive disorders related to aging [24, 25].…”

Section: Introductionmentioning

confidence: 99%

Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

et al. 2019

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“…BP, ECG, and EEG along with many other objective physiological, or psychological, function tests can be undertaken in the field, in ecologically valid settings ( 91 , 92 ). Although there is no single physiological objective measure of tinnitus itself, years of lab based search has identified many related markers of tinnitus related activity, for example neural networks associated with tinnitus ( 93 ), measures of emotion, and stress ( 94 ). New miniaturized wearable technology is now available to make longitudinal measures of physiological function ( 95 – 104 ) that can be related to behavioral indices of tinnitus.…”

Section: Resultsmentioning

confidence: 99%

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

Searchfield

Sanders

Doborjeh

et al. 2021

Front. Digit. Health

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Background: Digital processing has enabled the development of several generations of technology for tinnitus therapy. The first digital generation was comprised of digital Hearing Aids (HAs) and personal digital music players implementing already established sound-based therapies, as well as text based information on the internet. In the second generation Smart-phone applications (apps) alone or in conjunction with HAs resulted in more therapy options for users to select from. The 3rd generation of digital tinnitus technologies began with the emergence of many novel, largely neurophysiologically-inspired, treatment theories that drove development of processing; enabled through HAs, apps, the internet and stand-alone devices. We are now of the cusp of a 4th generation that will incorporate physiological sensors, multiple transducers and AI to personalize therapies.Aim: To review technologies that will enable the next generations of digital therapies for tinnitus.Methods: A “state-of-the-art” review was undertaken to answer the question: what digital technology could be applied to tinnitus therapy in the next 10 years? Google Scholar and PubMed were searched for the 10-year period 2011–2021. The search strategy used the following key words: “tinnitus” and [“HA,” “personalized therapy,” “AI” (and “methods” or “applications”), “Virtual reality,” “Games,” “Sensors” and “Transducers”], and “Hearables.” Snowballing was used to expand the search from the identified papers. The results of the review were cataloged and organized into themes.Results: This paper identified digital technologies and research on the development of smart therapies for tinnitus. AI methods that could have tinnitus applications are identified and discussed. The potential of personalized treatments and the benefits of being able to gather data in ecologically valid settings are outlined.Conclusions: There is a huge scope for the application of digital technology to tinnitus therapy, but the uncertain mechanisms underpinning tinnitus present a challenge and many posited therapeutic approaches may not be successful. Personalized AI modeling based on biometric measures obtained through various sensor types, and assessments of individual psychology and lifestyles should result in the development of smart therapy platforms for tinnitus.

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“…The auditory network with tinnitus has a comparatively different level of segregation and integration in most rhythm bands (Mohagheghian et al 2019). The connectivity in the superior frontal cortex has various degrees of reduction for all frequency bands during the development stage of tinnitus (Lan et al 2020).…”

Section: Discussion and Future Challengesmentioning

confidence: 99%

A survey of brain network analysis by electroencephalographic signals

Luo

et al. 2021

Cogn Neurodyn

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Brain network analysis is one efficient tool in exploring human brain diseases and can differentiate the alterations from comparative networks. The alterations account for time, mental states, tasks, individuals, and so forth. Furthermore, the changes determine the segregation and integration of functional networks that lead to network reorganization (or reconfiguration) to extend the neuroplasticity of the brain. Exploring related brain networks should be of interest that may provide roadmaps for brain research and clinical diagnosis. Recent electroencephalogram (EEG) studies have revealed the secrets of the brain networks and diseases (or disorders) within and between subjects and have provided instructive and promising suggestions and methods. This review summarized the corresponding algorithms that had been used to construct functional or effective networks on the scalp and cerebral cortex. We reviewed EEG network analysis that unveils more cognitive functions and neural disorders of the human and then explored the relationship between brain science and artificial intelligence which may fuel each other to accelerate their advances, and also discussed some innovations and future challenges in the end.

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Computer-Aided Tinnitus Detection based on Brain Network Analysis of EEG Functional Connectivity

Cited by 20 publications

References 41 publications

Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG

A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics

A survey of brain network analysis by electroencephalographic signals

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