ArticleBrain informatics2020
EEG-based classification of epilepsy and PNES: EEG microstate and functional brain network features.
Article in Brain informatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers.
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37 citing papers in PubMed.
- Seizure type classification algorithm based on multi-dimensional brain network feature selection.Fundamental research · 2026Article
- Attenuated total reflection Fourier transform infrared spectroscopy enables determination of the extent of precancer in cervical conization biopsy.Scientific reports · 2026Article
- State-Dependent CNN-GRU Reinforcement Framework for Robust EEG-Based Sleep Stage Classification.Biomimetics (Basel, Switzerland) · 2026Article
- EEG Microstates During Multisensory Stimulation: Assessing the Severity of Disorders of Consciousness and Distinguishing the Minimally Conscious State.Brain sciences · 2025Article
- Unraveling functional neurological disorder in pediatric populations: A systematic review of diagnosis, treatment, and outcomes.World journal of clinical pediatrics · 2025Article
- Altered EEG Microstate Dynamics in First Unprovoked Seizure and Newly Diagnosed Epilepsy Patients.European journal of neurology · 2025Article
- Advancing Alzheimer's disease detection: a novel convolutional neural network based framework leveraging EEG data and segment length analysis.Brain informatics · 2025Article
- Stable Yet Destabilised: Towards Understanding Brain Network Dynamics in Psychogenic Disorders.Journal of clinical medicine · 2025Article
- Improving diagnostic accuracy of routine EEG for epilepsy using deep learning.Brain communications · 2025Article
- Computer-assisted analysis of routine EEG to identify hidden biomarkers of epilepsy: A systematic review.Computational and structural biotechnology journal · 2024Review
- Resting state EEG microstate profiling and a machine-learning based classifier model in epilepsy.Cognitive neurodynamics · 2024Article
- Ictal-onset localization through effective connectivity analysis based on RNN-GC with intracranial EEG signals in patients with epilepsy.Brain informatics · 2024Article
- Beta-band power modulation in the human amygdala differentiates between go/no-go responses in an arm-reaching task.Journal of neural engineering · 2024Article
- A GRU-CNN model for auditory attention detection using microstate and recurrence quantification analysis.Scientific reports · 2024Article
- Disentanglement of Resting State Brain Networks for Localizing Epileptogenic Zone in Focal Epilepsy.Brain topography · 2024Article
- Intrinsic brain activity differences in perampanel-responsive and non-responsive drug-resistant epilepsy patients: an EEG microstate analysis.Therapeutic advances in neurological disorders · 2024Article
- Resting-state EEG microstate analysis of internet gaming disorder and alcohol use disorder.Dialogues in clinical neuroscience · 2024Article
- Article
- Differentiation of epilepsy and psychogenic nonepileptic events based on body fluid characteristics.Epilepsia open · 2023Observational
- Machine-learning for the prediction of one-year seizure recurrence based on routine electroencephalography.Scientific reports · 2023Article
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5 authors.
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Abstract
Epilepsy and psychogenic non-epileptic seizures (PNES) often show over-lap in symptoms, especially at an early disease stage. During a PNES, the electrical activity of the brain remains normal but in case of an epileptic seizure the brain will show epileptiform discharges on the electroencephalogram (EEG). In many cases an accurate diagnosis can only be achieved after a long-term video monitoring combined with EEG recording which is quite expensive and time-consuming. In this paper using short-term EEG data, the classification of epilepsy and PNES subjects is analyzed based on signal, functional network and EEG microstate features. Our results showed that the beta-band is the most useful EEG frequency sub-band as it performs best for classifying subjects. Also the results depicted that when the coverage feature of the EEG microstate analysis is calculated in beta-band, the classification shows fairly high accuracy and precision. Hence, the beta-band and the coverage are the most important features for classification of epilepsy and PNES patients.
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