Volume 17 (2023)
Volume 16 (2022)
Volume 15 (2021)
Volume 14 (2020)
Volume 13 (2019)
Volume 12 (2018)
Volume 11 (2017)
Volume 10 (2016)
Volume 9 (2015)
Volume 8 (2014)
Volume 7 (2013)
Volume 6 (2012)
Volume 5 (2011)
Volume 4 (2010)
Volume 3 (2009)
Volume 2 (2008)
Volume 1 (2007)
Volume -2 (2005)
Volume -1 (2004)
Non-invasive deep electrical stimulation of the primary motor cortex of the rat by temporal interference method

Zohre Mojiri; Amir Akhavan; Ehsan Rouhani

Volume 16, Issue 3 , December 2022, , Pages 257-269

  Deep brain stimulation (DBS) is a technique to stimulate the deep areas of the brain which can be used in both invasive and non-invasive methods. In invasive DBS, the electrodes are surgically implanted inside the brain to achieve the desired depth of the stimulation. The invasive DBS approach suffers ...  Read More

Concealed Information Recognition with the Fusion of Physiological Communication Network of Facial Areas and Psychological Analysis

Amin Mohammadian; Akram Ghorbali; Maryam Asadolah Tooyserkani; Razieh kaveh; kian Shahi

Volume 16, Issue 1 , May 2022, , Pages 33-50

  The interview analyst’s need to detect deception is a topic that has provided the conditions for providing solutions to empower them. So that, the experts and interview analysts can be assisted by automatically monitoring the subject's unsalient, unknown, or counterintuitive activities during the ...  Read More

An Efficient Method for Parkinson’s Disease Detection using Handwriting Features

Elham Dehghanpur Deharab; Peyvand Ghaderyan

Volume 15, Issue 4 , March 2022, , Pages 279-287

  Parkinson's disease (PD) is one of the most common types of dementia associated with motor impairments and affected performance of motor skills such as writing. Brain imaging techniques are the common methods used to diagnose PD, which are expensive or invasive, and their accuracy depends on the experience ...  Read More

Sparse Coding Classification and Analytic EEG Signal Representation for Obsessive Compulsive Disorder Detection

Farzaneh Manzari; Peyvand Ghaderyan

Volume 15, Issue 4 , March 2022, , Pages 313-328

  Obsessive-Compulsive Disorder (OCD) is the fourth most common mental disorder and the tenth cause of disability worldwide. This disorder can lead to cognitive impariments in attention, memory, thinking, auditory processing of words and visual cognition. Previous studies have demonstrated that OCD is ...  Read More

Automatic Detection of Driver Fatigue from EEG Signals using Deep Neural Networks

Sobhan Sheykhivand; Zohreh Mousavi; Tohid Yousefi Rezaii

Volume 14, Issue 3 , October 2020, , Pages 179-193

  In recent years, driver fatigue has become one of the major causes of road accidents, and many studies have been conducted to analyze driver fatigue. EEG signals are considered the most reliable method for measuring driver fatigue because of the non-invasive nature. Manual interpretation of EEG signals ...  Read More

Automatic Identification of Epileptic Seizures from EEG Signals based on Dictionary Learning

Sobhan Sheykhivand; Zohreh Mousavi; Tohid Yousefi Rezaii

Volume 14, Issue 3 , October 2020, , Pages 209-220

  Using a smart method to automatically detect different stages of epilepsy in medical applications, to reduce the workload of physicians in analyzing epilepsy data by visual inspection is one of the major challenges in recent years. One of the problems of automatic identification of different stages of ...  Read More

Localizing Epileptic Focus through Simultaneous EEG-fMRI Recording and Automated Detection of IED from Inside-Scanner EEG

Elias Ebrahimzadeh; Hamid Soltanian-Zadeh; Babak Nadjar Araabi; Seyed Sohrab Hashemi Fesharaki; Jafar Mehvari Habibabadi

Volume 13, Issue 2 , August 2019, , Pages 135-145

  Since electroencephalography (EEG) signal contains temporal information and fMRI carries spatial information, we can reasonably expect that a combination of the two contributes greatly to precise localization of   epileptic focuses.  With that in mind, we have first extracted spike patterns ...  Read More

Fusion Analysis of Brain Functional and Structural Connectivity to Discriminate Schizophrenia in Network Level

Farzaneh Keyvanfard; Abbas Nasiraei Moghaddam

Volume 13, Issue 2 , August 2019, , Pages 147-158

  Brain as the most complex organ in the human body has been investigated from various aspects. The greatest origin of this complexity is due to the fact that, despite the fixed architecture of brain structure (physical connections), the functional connectivity is in a constantly changing state, resulting ...  Read More

Esophageal Epithelium Modeling based on Globally Coupled Maps with the approach of Precancerous Lesions Diagnosis

Zahra Sadat Hosseini; Seyed Mohammad Reza Hashemi Golpayegani

Volume 13, Issue 1 , April 2019, , Pages 69-84

  The esophageal carcinoma is the eight most predominate malignancy in the world and the sixth deadliest cancer. 80% of esophageal cancers occur in squamous cells. In Iran, this type of cancer is more prevalent in Golestan province. Before the onset of this type of cancer, histological precursor lesions ...  Read More

Spiral Waves Formation in a Network of Neuron Model with Monotonically Differentiable Magnetic Flux

Fatemeh Parastesh; Sajad Jafari; Hamed Azarnoush

Volume 13, Issue 1 , April 2019, , Pages 85-93

  Spiral wave is a particular spatiotemporal pattern, observed in a wide range of complex systems such as neuronal network. Appearance of these waves is related to the network structure as well as the dynamics of its blocks. In this paper, we propose a new modified Hindmarsh-Rose neuron model. The proposed ...  Read More

Optimization of Continuous Wavelet Coefficients for Neural Spike Sorting

Amir Soleymankhani; Vahid Shalchyan

Volume 12, Issue 2 , September 2018, , Pages 85-96

  The extracellular recording from the brain's single neurons is known as a popular method in neuroscience and neuro-rehabilitation engineering. These recordings include the activity of all neurons around the electrode, for better use of which, spike sorting methods should be utilized to obtain the activity ...  Read More

Automatic Stage Scoring of Single-Channel Sleep EEG using Discrete Wavelet Transform and a Hybrid Model of Simulated Annealing Algorithm and Neural Network

Sobhan Sheykhivand; Tohid Yousefi Rezaii; Zohreh Mousavi; Saeed Meshgini

Volume 11, Issue 4 , February 2018, , Pages 313-325

  Using an intelligent method to automatically detect sleep patterns in medical applications is one of the most important challenges in recent years to reduce the workload of physicians in analyzing sleep data through visual inspection. In this paper, a single-channel EEG-based algorithm is used to automatically ...  Read More

Investigating the Effect of Behavior Modulation in Purkinje Cells Spike Train on the Spectrum of Deep Cerebellar Nuclei Output

Samira Abbasi

Volume 11, Issue 2 , June 2017, , Pages 127-135

  Neural function depends on the received synapses and the intrinsic properties of the neuron. However, synaptic integration and intrinsic responses can largely depend on the synaptic inputs. In this respect, deep cerebellar nuclei (DCN) neurons which receive inhibitory synapses from Purkinje cells (PCs) ...  Read More

Analysis of the Role of Parameters in the Chaotic Behavior of a Cancerous System and its Biological Interpretation

Seyed Hojat Sabzpoushan; Tina Ghodsi Asnaashari; Fateme Pourhasanzade

Volume 11, Issue 1 , May 2017, , Pages 41-49

  Cancer is one of the most important causes of mortality in human society; therefore, scientists are always looking for new ways to cope with the disease. Understanding more about the dynamics of cancerous tumors in body can help researches. Therefore, making simple models for tumor growth is important. ...  Read More

Correlation Method with Pre-Tuning Using CCA for SSVEP-Based Brain-Computer Interface System

Marzieh Alirezaei Alavijeh; Ali Maleki

Volume 10, Issue 2 , August 2016, , Pages 187-196

  Brain-computer interface system based on Steady-state visual evoked potentials is taken into consideration due to advantages such as simplicity of installation and use of the system, enough accurate and acceptable Information transfer rate. In addition to these benefits, short processing time is also ...  Read More

Blood Glucose Regulation using Fractional Order Sliding Mode Control for Type 1 Diabetes Patients

Hamid Heydari Nejad; Hadi Delavari

Volume 9, Issue 4 , February 2015, , Pages 327-339

  The patients with Type 1 diabetes need strict blood glucose level control because the body’s production and use of insulin are impaired and hence this increases the blood glucose level. In this paper, a fractional order sliding mode control and an adaptive fractional order sliding mode control ...  Read More

A Smartphone-based Fall Detection System using Accelerometer and Microphone

Saeid Shakeri

Volume 9, Issue 4 , February 2015, , Pages 399-410

  Falls are one of the main reasons to injury, especially in the elderly people. These injuries can be reduced by quick and accurate response or reaction, but this is not possible often in elderly people because they usually live alone and after injury caused the falling, cannot call for help. This paper ...  Read More