Full Research Paper
Brain Computer Interface / BCI / Neural Control Int. / NCI / Mind Machine Int. / MMI / Direct Neural Int. / DNI / Brain Machine Int. / BMI
Marzie Alirezaei Alavijeh; Ali Maleki
Volume 16, Issue 1 , May 2022, Pages 1-9
Abstract
Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such ...
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Nowadays, brain-computer interface system based on steady-state visual evoked potentials is increased due to advantages such as accepted accuracy and minimal need for user training. Despite these benefits, the unwanted noise that affects SSVEP is one of the issues that can reduce the efficiency of such systems. This paper uses the EMD algorithm in the initial phase and CCA or LASSO for the recognition of the stimulation frequency. In the first step, the EMD algorithm is applied so that non-stationary SSVEP signal breaks into oscillating functions and meaningful information are extracted. Among the IMFs obtained from the EMD method, only IMFs whose amplitude of the frequency spectrum in the frequency ranges corresponding to the excitation is higher were selected. With this selection, noisy signals and unprofitable information can be omitted. In the proposed method, two CCA and LASSO diagnostic methods were performed on the sum of selected signals to identify the frequency of stimulation. The simulation results show the recognition accuracy of 81.76% and 82.26% for the proposed method EMD-CCA and EMD-LASSO, respectively. While detection accuracy is 78.10% and 78.72% for conventional methods of CCA and LASSO.
Full Research Paper
Cardiovascular Biomechanics
Hadi Taghizadeh; Faezeh Amini
Volume 16, Issue 1 , May 2022, Pages 11-21
Abstract
Atherosclerosis, a common cardiovascular disease, is among the leading causes of death. Many of the heart attacks results from ruptured atherosclerotic lesion and emboli formation. Then, the susceptibility of the lesion is a key factor in preventing negative outcomes of the rupture. Mechanisms of plaque ...
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Atherosclerosis, a common cardiovascular disease, is among the leading causes of death. Many of the heart attacks results from ruptured atherosclerotic lesion and emboli formation. Then, the susceptibility of the lesion is a key factor in preventing negative outcomes of the rupture. Mechanisms of plaque rupture are under debate. However, a general agreement on the bold contribution of hemodynamic factors including the blood pressure is established. In the current study, biomechanical impacts of plaque calcification procedure and the changed thickness of fibrous cap were investigated. To do so, a cross-section of the constricted coronary artery is reconstructed from the histological images and extruded in the axial direction of the artery to produce the three dimensional configuration of the coronary model. Holzapfel strain energy density function is utilized for mechanical description of the arterial tissue and the fibrous cap which enables us to adopt collagen fiber orientation into the mechanical model. Furthermore, since the constricted vessel configuration is asymmetrical, instead of simplified cylindrical coordinates for collagen orientation, a discrete coordinate system is assigned to every element and respective circumferential, axial and radial directions were assigned. With calcification, plaque is more stable and produces monotonic stress patterns in its vicinity. Also, the fibrous cap thickness plays an important role as a barrier to inhibit stress concentration from soft lipid core and disturb the mechanical loads to the neighboring regions. These two parameters, provide useful insight on mechanical load distribution around an atherosclerotic lesion and the pathway of arterial tissue toward a new homeostasis.
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Medical Physics / Biomedical Physics / Medical Biophysics / Biophysics
Hossein Mirzaei; ,Ghazale Geraily,; Fatemeh Seyyedrezaei; Ali Kazemian
Volume 16, Issue 1 , May 2022, Pages 23-32
Abstract
In radiotherapy treatments, there are some situations where the shape of the target volume is complicated, so multiple radiation fields are used to cover the whole tumoral tissue. Therefore, adjusting adjacent fields and minimizing radiation dose to healthy tissues is an important goal in radiotherapy. ...
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In radiotherapy treatments, there are some situations where the shape of the target volume is complicated, so multiple radiation fields are used to cover the whole tumoral tissue. Therefore, adjusting adjacent fields and minimizing radiation dose to healthy tissues is an important goal in radiotherapy. The aim of this study is to introduce a general mathematical solution for matching adjacent radiation fields and also to evaluate this solution in therapeutic techniques such as Craniospinal irradiation and breast with a supraclavicular full-field and half-field irradiation. This method considers a right-handed system with its center located in the isocenter and two hypothetical fields named field number one and field number two. Then, the angles of collimators, couch, gantry, and the jaw aperture for both hypothetical fields are calculated to match between their side plates using the presented methods. Comparison of the measurements with the treatment planning system shows that the Craniospinal radiotherapy technique has an error only in measuring the collimator angle; this error is 0.198%. The maximum errors were obtained in two supraclavicular techniques with full- and half-field in calculating the size of the jaw aperture of the supraclavicular field, which were 21.05% and 18.6%, respectively. In conclusion, according to the results, this method can improve the field alignment in all cases where adjacent treatment fields are used, with the acceptable error rate and without changing the field size, to achieve the same dose distribution.
Full Research Paper
Bioelectrics
Amin Mohammadian; Akram Ghorbali; Maryam Asadolah Tooyserkani; Razieh kaveh; kian Shahi
Volume 16, Issue 1 , May 2022, Pages 33-50
Abstract
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 ...
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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 interview. The aim of this study was to combine quantitative and qualitative information to help improve the detection of deception. For this purpose, in addition to using the capacity of verbal and non-verbal analysis methods, thermal imaging technology and new methods of spatiotemporal analysis of the thermal patterns have been used to detect concealed information in individuals. Then, based on the study design, the database consisting of 48 truth-tellers and liars who participated in a mock scenario was collected. Then, two qualitative methods of verbal and non-verbal information analysis, including standard criteria-based content analysis (CBCA) and behavioral analysis interview (BAI) scoring, were used to identify liars and truth-tellers. In order to complete the obtained results based on these two methods, using effective connectivity analysis method, physiological network analysis of communication between different areas of the face was performed in thermal images of individuals. As a result of combining quantitative and qualitative information, the final accuracy of individuals' diagnosis increased from an average of 73.61% to 79.17%. The investigation of the agreement analysis between methods by kappa coefficient and analysis of confusion matrix information indicated the existence of complementary information in various quantitative and qualitative methods to identify concealed information in individuals.
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Neural Engineering / Neuroengineering / Brain Engineering
Mohammad Reza Nazari; Mohammad Reza Daliri; Ali Motie Nasrabadi
Volume 16, Issue 1 , May 2022, Pages 51-62
Abstract
Visual attention as a cognitive factor plays a significant role in the processing of higher-order mental information that happens in the brain and affects brain activity in various areas of the visual cortex. Among the various recording systems, local field potentials, due to their stability, robustness, ...
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Visual attention as a cognitive factor plays a significant role in the processing of higher-order mental information that happens in the brain and affects brain activity in various areas of the visual cortex. Among the various recording systems, local field potentials, due to their stability, robustness, and frequency content have received interest in brain structure and cognitive processing research, as well as brain-computer interface (BCI) systems. Hence, the extraction and interpretation of information from local field potential (LFP) signals during visual attention has been considered to control cognitive systems. Cross-frequency coupling (CFC) as one of the information encoding strategies in the brain plays a functional role in perception, working memory, and visual attention tasks. However, the role of CFC as informative features for spatial attention decoding has not been adequately investigated. This paper aims to examine spatial attention decoding using LFP signals recorded from the monkey middle temporal area (MT). For this purpose, phase-phase and phase-amplitude coupling features and machine learning algorithms have been employed. The results show that the highest decoding performance was achieved by applying selected optimal features and the support vector machine classifier (90.36%). Moreover, among the selected features, gamma-delta, gamma-alpha, and beta-delta coupling contain the most cognitive information and the most effective features to improve the decoding performance of spatial attention in the visual system. Generally, the results suggest that cross-frequency coupling of LFP signals contains significant information in spatial attention tasks, and can be used as a suitable alternative to the time-frequency features of brain signals in cognitive BCI systems.
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Biomedical Imaging / Medical Imaging
Elham Mohammadi; Abbas Nasiraei Moghaddam
Volume 16, Issue 1 , May 2022, Pages 63-74
Abstract
Real-time MRI using highly undersampled radial acquisition can be used for dynamic assessments of the heart. The main challenges, however, are the presence of severe undersampling artifacts in the periphery of the images. In this study, to improve the visual quality of the final real-time images, a new ...
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Real-time MRI using highly undersampled radial acquisition can be used for dynamic assessments of the heart. The main challenges, however, are the presence of severe undersampling artifacts in the periphery of the images. In this study, to improve the visual quality of the final real-time images, a new method for the acquisition of successive frames based on a radial trajectory with the turned arrangement is presented. Accordingly, by combining the information obtained from successive frames, it is possible to reconstruct images with high and low spatial resolution. In the proposed method, specifically due to the use of the Polar Fourier Transform reconstruction method, reconstructed images with two different resolutions can be combined to reduce the visual effects of undersampling artifacts. In this paper, the proposed method has been used especially for the real-time radially tagged images to increase the efficiency and accuracy of measuring left ventricular rotation motion. According to the simulation results, the structural similarity measure is improved from 0.6 to 0.8. Real-time imaging with a time resolution of 46 ms of healthy individuals also shows that while the temporal resolution of the rotational information is well preserved, the visual quality of images is improved.
Full Research Paper
Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing
Mohammad Davood Khalili; Vahid Abootalebi; Hamid Saeedi-Sourck
Volume 16, Issue 1 , May 2022, Pages 75-94
Abstract
The human brain is one of the most complex and heterogeneous networks, and brain signals contain a lot of information, so researchers in this field are always looking for proper solutions to select meaningful features and reduce the dimension of this information appropriately to lead to better classification. ...
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The human brain is one of the most complex and heterogeneous networks, and brain signals contain a lot of information, so researchers in this field are always looking for proper solutions to select meaningful features and reduce the dimension of this information appropriately to lead to better classification. Two of the new tools for brain signal processing are Graph Signal Processing (GSP) and Meta-heuristic and Evolutionary methods. In this paper, a geometric structure and a mixed structure are considered for the brain graph and the weights of the edges in the mixed structure are calculated by a combination of two measures: geometric distance and correlation. To reduce the graph dimension, the weighted degree metric and a combination of the Kron reduction method and Graph Fourier Transform (KG) are used to properly preserve the information of all vertices of the graph into the selected vertices. Feature extraction is performed by Ledoit-Wolf shrinkage estimation and Tangent Space Mapping (TSM) method. For dimension reduction of extracted features, Principal Component Analysis (PCA) method and feature selection based on Differential Evolution (DE) are used. The selected features are given to several well-known machine learning classifiers. To evaluate the performance of the proposed method, dataset IVa from BCI Competition III has been used. The results show that the average classification accuracy of the proposed KG-PCA method with SVM-RBF and DT classifiers, in the structural graph and the functional-structural graph, is higher than the TSM-GFT method expressed in previous studies, and the DT classifier has achieved an average accuracy of 91.15±1.17. Also, according to the obtained results, the performance of the proposed KG-DE method has been better compared to KG-PCA and in the best case, the average accuracy of the SVM-RBF classifier is equal to 95.50±1.27.
