Articles

type: Conference
Title Date
Investigating Effective Connectivity within the Default Mode Network in Autism Spectrum Disorder: Insights from Dynamic Causal Modeling of Resting-State fMRI
كنترل تشنج در مدل صرع ساز با استفاده از كنترل كننده سطح ديناميكي
Automatic classification of migraine and tensiontype headaches using machine learning methods
A Cloud-based IoT-enabled framework for BCI applications
بررسي ميزان تغييرات ارتباطات عملكردي شبكه مغزي در طول زمان به منظور تشخيص بيماري اوتيسم از روي تصاوير اف ام آرآي
type: Journal
Title DOI Date
Investigation of Electrical Signals in the Brain of People with Autism Using Effective Connectivity Network #
Detection of autism spectrum disorder using graph representation learning algorithms and deep neural network, based on fMRI signals 10.3389/fnsys.2022.904770
Detection of factors affecting kidney function using machine learning methods 10.1038/s41598-022-26160-8
Investigation of fetal ECG signal using textile-based electrodes #
Diagnosis of Autism Disorder Based on Deep Network Trained by Augmented EEG Signals #
تركيب فيلتر ذره ترتيبي و شكل دهنده پرتو براي مكانيابي منابع اخلالگر مغزي #
Design and Implementation of a Spiking Neural Network with Integrate-and-Fire Neuron Model for Pattern Recognition 10.1142/S0129065720500732
Physiological constraints of visual pathway lead to more efficient coding of information in retina #
پيشگويي برخط و تك كاناله وقوع حمله صرعي با ارائه الگوي توليد صرع بر روي سيگنالهاي الكتروانسفالوگرام عمقي با استفاده از فيلتر كالمن توسعه يافته #
Hippocampal effective synchronization values are not pre-seizure indicator without considering the state of the onset channels #
Proposing a two-level stochastic model for epileptic seizure genesis #
A Model-Based Method for Computation of Correlation Dimension Lyapunov Exponents and Synchronization from Depth-EEG Signals #
Analysis of the Behavior of a Seizure Neural Mass Model Using Describing Functions #
A brief survey of computational models of normal and epileptic EEG signals: a guideline to model-based seizure prediction #