ArticleJournal of biomedical physics & engineering2019
Computer-Aided Tinnitus Detection based on Brain Network Analysis of EEG Functional Connectivity.
Article in Journal of biomedical physics & engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 25 citations in OpenAlex.
- Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Deep learning-based electroencephalic diagnosis of tinnitus symptom.Frontiers in human neuroscience · 2023Article
- Effects of Transcranial Direct Current Stimulation on Attentional Bias to Methamphetamine Cues and Its Association With EEG-Derived Functional Brain Network Topology.The international journal of neuropsychopharmacology · 2022Article
- A survey of brain network analysis by electroencephalographic signals.Cognitive neurodynamics · 2022Article
- Brain-Computer Interface: Advancement and Challenges.Sensors (Basel, Switzerland) · 2021Review
- A State-of-Art Review of Digital Technologies for the Next Generation of Tinnitus Therapeutics.Frontiers in digital health · 2021Review
- Computer-aided classifying and characterizing of methamphetamine use disorder using resting-state EEG.Cognitive neurodynamics · 2019Article
- Disrupted resting-state brain functional network in methamphetamine abusers: A brain source space study by EEG.PloS one · 2019Article
Corrections and comments
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Authors and funding
7 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundTinnitus known as a central nervous system disorder is correlated with specific oscillatory activities within auditory and non-auditory brain areas. Several studies in the past few years have revealed that in the most tinnitus cases, the response pattern of neurons in auditory system is changed due to auditory deafferentation, which leads to variation and disruption of the brain networks.
objectiveIn this paper, we introduce an approach to automatically distinguish tinnitus individuals from healthy controls based on whole-brain functional connectivity and network analysis. MATERIAL AND
methodsThe functional connectivity analysis was applied to the resting state electroencephalographic (EEG) data of both groups using Weighted Phase Lag Index (WPLI) for various frequency bands in 2-44 Hz frequency range. In this case- control study, the classification was performed on graph theoretical measures using support vector machine (SVM) as a robust classification method.
resultsExperimental results showed promising classification performance with a high accuracy, sensitivity, and specificity in all frequency bands, specifically in the beta2 frequency band.
conclusionThe current study provides substantial evidence that tinnitus network can be successfully detected by consistent measures of the brain networks based on EEG functional connectivity.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.