Frontiers in Biomedical Technologies
Volume:7 Issue: 3, Summer 2020

The core mission of clinical MRI in Human Brain Mapping (HBM) is formed in a cycle of research, education and practice. Learning the effective diagnostic and treatment planning procedures occurs not in the classrooms but through engagement in active research. Clinical MRI research for HBM initiates with strategic and necessary demands of clinicians, e.g. neurologists, neurosurgeons, psychologists, psychiatrists, etc. who need specific clinical MRI acquisition and quantification techniques for better, faster and more accurate diagnostic and follow-up procedures. Neuro-radiologists are responsible for all aspects of a research MRI examination, including assessment of patient’s clinical symptoms, assigning the imaging protocol, reviewing the acquired images for their quality and interpretations, and finally, preparing the reports. MR physicists with their unique scientific qualifications and perception of clinical requirements play a critical role in optimization of the existing protocols, establishment of research investigations and development of effective techniques (including pulse sequences, analysis and quantification software, etc.) for clinical application of MRI in HBM, when responsibility of a clinical scientist is minimal when the research methodology development starts while the physicist starts with the maximum responsibility to develop the methodology, and vice versa when the methodology development progresses from early to the end stages closer to the clinical practice.

Sleep is one of the necessities of the body, such as eating, drinking, etc., that affects different aspects of human life. Sleep monitoring and sleep stage classification play an important role in the diagnosis of sleep-related diseases and neurological disorders. Empirically, classification of sleep stages is a time-consuming, tedious, and complex task, which heavily depends on the experience of the experts. As a result, there is a crucial need for an automatic efficient sleep staging system.

This study develops a 13-layer 1D Convolutional Neural Network (CNN) using single-channel Electroencephalogram (EEG) signal for extracting features automatically and classifying the sleep stages. To overcome the negative effect of an imbalance dataset, we have used the Synthetic Minority Oversampling Technique (SMOTE). In our study, the single-channel EEG signal is given to a 1D CNN, without any feature extraction/selection processes. This deep network can self-learn the discriminative features from the EEG signal.

Applying the proposed method to sleep-EDF dataset resulted in overall accuracy, sensitivity, specificity, and Precision of 94.09%, 74.73%, 96.43%, and 71.02%, respectively, for classifying five sleep stages. Using single-channel EEG and providing a network with fewer trainable parameters than most of the available deep learning-based methods are the main advantages of the proposed method.

In this study, a 13-layer 1D CNN model was proposed for sleep stage classification. This model has an end-to-end complete architecture and does not require any separate feature extraction/selection and classification stages. Having a low number of network parameters and layers while still having high classification accuracy, is the main advantage of the proposed method over most of the previous deep learning-based approaches.

Brain-Computer Interface (BCI) systems are able to understand and execute commands through processing brain signals. It has numerous applications in the field of biomedical engineering such as rehabilitation, biometric and entertainment. A BCI system consists of four major parts: signal acquisition, signal pre-processing, feature extraction and classification. Steady State Visually Evoked Potentials (SSVEP) is one of the most common paradigms in BCI systems, which is generally a response to visual stimuli with the frequency between 5 to 60 Hz.

In this study, we suggest a Convolutional Neural Network (CNN) based model for the classification of EEG signal during SSVEP task. For the evaluation, the model was tested with different channels and electrodes.

Results show that channels number 138 and 139 have the great potential to appropriately classify EEG signal.

Using the suggested model and the mentioned channels, the accuracy of 73.74% could be achieved in this study.

Graph theory is a widely used and reliable tool to quantify brain connectivity. Brain functional connectivity is modeled as graph edges employing correlation coefficients. The correlation coefficients can be used as the weight that shows the power of connectivity between two nodes or can be binarized to show the existence of a connection regardless of its strength. To binarize the brain graph two approaches, namely fixed threshold and fixed density are often used.

This paper aims to investigate the difference between weighted or binarized graphs in brain functional connectivity analysis. To achieve this goal, the brain connectivity matrices are generated employing the functional Magnetic Resonance Imaging (fMRI) data of Alzheimer's Disease (AD). After preprocessing the data, weighted and binarized connectivity matrices are constructed using a fixed threshold and fixed density techniques. Graph global features are extracted and a non-parametric statistical test is performed to analyze the performance of the methods.

Results show that all three methods are powerful in distinguishing the healthy group from AD subjects. The P-Values of the weighted graph is close to the fixed threshold method.

Also, it is worthwhile mentioning that the fixed threshold method is robust in changing the threshold while the fixed density method is very sensitive. On the other hand, graph global measures such as clustering coefficient and transitivity, regardless of the method, show significant differences between the control and AD groups. Furthermore, the P-Values of modularity measure are very varied according to the method and the selected threshold.

One of the essential problems in deep-learning face recognition research is the use of self-made and less counted data sets, which forces the researcher to work on duplicate and provided data sets. In this research, we try to resolve this problem and get to high accuracy.

In the current study, the goal is to identify individual facial expressions in the image or sequence of images that include identifying ten facial expressions. Considering the increasing use of deep learning in recent years, in this study, using the convolution networks and, most importantly, using the concept of transfer learning, led us to use pre-trained networks to train our networks.

One way to improve accuracy in working with less counted data and deep-learning is to use pre-trained using pre-trained networks. Due to the small number of data sets, we used the techniques for data augmentation and eventually tripled the data size. These techniques include: rotating 10 degrees to the left and right and eventually turning to elastic transmation. We also applied deep Res-Net's network to public data sets existing for face expression by data augmentation.

We saw a seven percent increase in accuracy compared to the highest accuracy in previous work on the considering dataset.

Irreversible electroporation is a physical process which is used for killing the cancer cells. The process that leads to cell death in this method is a unique process. Thermal damage does not exist in this process. However, the temperature of the tissue also increases during the electroporation. In this study, we aim to investigate the effect of conductivity changes on tissue temperature increase during the irreversible electroporation process.

To perform simulations and solve equations, COMSOL MultiPhysics has been used. Standard electroporation pulse sequence (8 pulses with different electric field intensities) was used as a pulse sequence in the simulation.

During the electroporation process, the electrical conductivity and the temperature of the tissue were increased. Changes in the tissue temperature in the simulation with variable electrical conductivity are more than in the simulation with constant electrical conductivity during the electroporation process. This difference for pulses with more vigorous electric field intensity and points closer to the electrodes has been achieved more.

To more accurately estimate and calculate the temperature and thermal damage inside the tissue during the irreversible electroporation process, it is suggested to consider the effect of conductivity changes during this process.

There are many methods for advertisements of products and neuromarketing is new area in this field. In neuromarketing, we use neuroscience information for reveal Consumer behavior by extraction brain activity. Functional magnetic resonance imaging (fMRI), Magnetoencephalography (MEG), and electroencephalography (EEG) are high efficacy tools   for investigation the brain activity in neuromarketing. EEG signal is a high temporal resolution and cheap method for to examine the brain activity.

In this study, 32 subjects (16 males and 16 females) who aging between 20-35 years participated. We proposed neuromarketing method exploit EEG system for predicting consumer preferences while they view E-commerce products. We apply some important preprocessing steps for noise and artifacts elimination of the EEG signal. In next step feature extraction methods apply on the EEG data such as discrete wavelet transform (DWT) and statistical features. The goal of this study is classification of analyzed EEG signal to likes and dislikes using supervised algorithms. We use Support Vector Machine (SVM), Artificial Neural Network (ANN) and Random Forest (RF) for data classification. The mentioned methods used for whole and lobe brain data.

The results show high efficacy for SVM algorithms than other methods. Accuracy, sensitivity, specificity and precision parameters used for evaluation of the model performance. The results shows high performance of SVM algorithms for classification of the data with accuracy more than 87% and 84% for whole and parietal lobe data.

We design a tool with EEG signals for extraction brain activity of consumers using neuromarketing methods. We investigate the effects of advertising on brain activity of consumers by EEG signals measures.

This review paper aimed to examine radiation safety issues related to relatives as well as caregivers of patients with thyroid diseases treated with radioiodine (I-131). During I-131 therapy for thyroid disorders such as hyperthyroidism, patients receiving I-131 doses (200-800 MBq) emit radioactive radiations which pose a prospective risk to other people. Critical groups are patients’ visitors and families, especially children. Following the updated international guidelines, the doses received by members of the public as a proportion of the therapy of a patient have been decreased. The public annual dose limits are 1 mSv, although higher doses are permitted for adults in the patient’s family, provided that the maximum 5 mSv is not surpassed for 5 years. Without compliance with the current recommendations, extended hospitalizations for patients are essential. Family members should therefore limit close interactions with an individual for some duration following thyroid therapy with I-131.

A Photoacoustic Imaging (PAI) as a non-invasive hybrid imaging modality has the potential to be used in a wide range of pre-clinical and clinical applications. There are different optical excitation sources that affect the performance of PAI systems. Our goal is proving the capability of the Light-Emitting Diode (LED) based PAI system for imaging of objects in different depths.

In this study the Full Width of Half Maximum (FWHM) and Contrast to Noise Ratio (CNR) of LED-based PAI system is evaluated using agar, and Poly-Vinyl Alcohol Cryogel (PVA-C) phantoms

The results show that axial and lateral FWHM of the photoacoustic image in agar phantom 1%, are 0.59 and 1.16 mm, respectively. It is capable of distinguishing objects about 250 µm. Furthermore, one of the main improvements of photoacoustic images is achieved by proposed LED-based system that is a 26% higher CNR versus the ultrasound images.

Therefore, the provided technical characteristics in this study have made designed LED-based PAI system as a suitable tool for preclinical and clinical imaging.