fa بازسازی تصاویر تصویربرداری تشدید مغناطیسی کارکردی با استفاده از سیگنال‌های الکتروآنسفالوگرام با روش شبکه‌های کانولوشنی همبند متراکم خود رمزنگار مدل سازی شناختی، پردازش سیگنال و تصویربرداری مغز پژوهشي اصیل Research <div style="text-align: justify;"><span style="line-height:2;"><span style="font-size:11pt"><span style="direction:rtl"><span style="unicode-bidi:embed"><span style="font-family:&quot;Calibri&quot;,sans-serif"><b><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Titr&quot;"><span style="color:black">مقدمه:</span></span></span></b><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"><span style="color:black"> داده‌های استفاده شده در این مدل یادگیر از ثبت هم‌زمان تصاویر </span></span></span><span lang="AR-SA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;">تصویربرداری تشدید مغناطیسی </span></span><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"><span style="color:black">و سیگنال‌های الکتروآنسفالوگرام در حین انجام تکلیف شناختی نقاط تصادفی متحرک برای سنجش میزان اطمینان در تصمیم‌گیری ادراکی تشکیل شده است با یادگیری مدل می‌توان از داده‌های سیگنال‌های الکتروآنسفالوگرام در آینده به طور مستقل استفاده کرد. </span></span></span><span lang="AR-SA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;">هدف این پژوهش بازسازی تصاویر تصویربرداری تشدید مغناطیسی کارکردی با استفاده از سیگنال‌های الکتروآنسفالوگرام است این پژوهش کاربردی است که بر روی </span></span><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"><span style="color:black">داده‌های</span></span></span><span lang="AR-SA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"> ثبت شده هم‌زمان صورت پذیرفته است.</span></span><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"><span style="color:black"></span></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="direction:rtl"><span style="unicode-bidi:embed"><span style="font-family:&quot;Calibri&quot;,sans-serif"><b><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Titr&quot;"><span style="color:black">روش کار:</span></span></span></b><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"><span style="color:black"> داده‌های </span></span></span><span lang="AR-SA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;">الکتروآنسفالوگرام </span></span><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"><span style="color:black">به‌ عنوان ورودی مدل و داده‌های تصاویر </span></span></span><span lang="AR-SA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;">تصویربرداری تشدید مغناطیسی </span></span><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"><span style="color:black">به ‌عنوان خروجی مدل در نظر گرفته شده و مدل یاد می‌گیرد که چطور از داده‌هایی با قالب ورودی، داده‌هایی از جنس قالب خروجی تولید نماید. قبل از ورود داده‌ها به مدل داده‌های ورودی برای بالا رفتن دقت مدل با حذف آرتیفکت‌ها با روش </span></span></span><span dir="LTR" style="font-family:&quot;Times New Roman&quot;,serif"><span style="color:black">fastICA</span></span> <span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"><span style="color:black">و تبدیل ‌شدن به ماتریس گرامیان پیش‌پردازش می‌شود.</span></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="direction:rtl"><span style="unicode-bidi:embed"><span style="font-family:&quot;Calibri&quot;,sans-serif"><b><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Titr&quot;"><span style="color:black">یافته‌ها:</span></span></span></b><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"><span style="color:black"> مدل نسبت به سایر روش‌ها برتری‌های مناسبی را در زمان آموزش و دقت مدل نشان داده است و مدل عمیق یادگیر کانولوشنی پیشنهادی با دقت مطلوبی موفق به شبیه‌سازی تصاویر تصویربرداری تشدید مغناطیسی از روی سیگنال‌های الکتروآنسفالوگرام گردید.</span></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="direction:rtl"><span style="unicode-bidi:embed"><span style="font-family:&quot;Calibri&quot;,sans-serif"><b><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Titr&quot;"><span style="color:black">نتیجه&shy;‌گیری:</span></span></span></b><span lang="FA" style="font-size:12.0pt"><span style="font-family:&quot;B Nazanin&quot;"><span style="color:black"> با استفاده از مدل عمیق یادگیر کانولوشنی پیشنهادی می‌توان به ارتباط بین فضای ساختاری و فضای رفتاری مغز&nbsp; پی برد و آن را جهت مطالعه هر بخش، پیاده‌&shy;سازی نمود.</span></span></span></span></span></span></span></span><br> &nbsp;</div> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><b><span new="" roman="" style="font-family:" times="">Introduction</span></b><span style="font-family:"Times New Roman",serif"></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">Deep Neural Network (DNN) models are one of the most dominant unsupervised feature extraction methods that have been widely studied recently. Convolutional Neural Networks (CNN) combine learned features with input data and use 2D convolutional layers, and this architecture is usually well suited for processing 2D data such as images. Convolutional neural networks work by extracting features directly from images and do not require feature extraction by an observer. The corresponding properties are not predefined. Convolutional neural networks learn to recognize different features of an image using tens or hundreds of hidden layers. Each hidden layer increases the complexity of the learned image features. The initially hidden layers can learn how to detect edges, and the later layers &nbsp;discover how to identify more complex shapes, particularly the shape of the object we are trying to detect. </span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">Generally, CNNs in each layer recognize more detailed features from an image that conclude, analyze, and then decide.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><b><span new="" roman="" style="font-family:" times="">Method</span></b><span style="font-size:12.0pt"><span style="line-height:150%"><span new="" roman="" style="font-family:" times="">s</span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">The basis of this research is based on DenseNet deep learning architecture.&nbsp;DenseNet architecture consists of several dense transmission blocks placed between two adjacent dense blocks. Each layer uses all the previous feature maps as input. This new model provides elevatedaccuracy with a reasonable number of network parameters for object detection tasks. </span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">With changes in settings, these algorithms achieved high accurate results in several datasets used for this purpose. Sets of features are collected in a flat layer and reconstructed layer by layer as a latent space of an Autoencoder network with UpSampling layers.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">In a joint project with the support of the American Science Foundation, Stanford University, and several other scientific research centers, a website for the free sharing of brain and cognitive science data under the openfMRI was launched in 2011 and &nbsp;then &nbsp;renamed openNeuro. Accordingly, these data were related to confidence in perceptual decisions, which were simultaneously recorded in EEG and fMRI.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">The task used during data recording was the Random Moving Dots (RDM) test, in which healthy volunteers were asked to judge the direction in which dots were moving across the screen.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">This study used&nbsp; the independent component analysis (ICA) methodto clean the existing EEG signals in the Python programming language using the fastICA algorithm. The self-sufficient analysiscomponents are called the separation of independent sources mixed by an unknown combination system. Besides, the the separation should be done only based on the observation of the combined signals, i.e. , both the combination system and the primary signals are unknown.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">The Gramian Angular Sum/Difference Fields method and Markov Transfer Fields were used to reduce the size of the input without losing the basic data and to convert the EEG signal into images.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><b><span new="" roman="" style="font-family:" times="">Results </span></b><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">The test data in this deep neural network are tensorial, consisting of MTF images with a size of 64&times;64&times;3 and an fMRI image matrix with a size of 70&times;70&times;32. The total available data are 16962, and the number of test data is 11873, which for this network, the ratio of test data to test data is 70-30. GPUs from Google&#39;s Collaboratory service were used</span> <span new="" roman="" style="font-family:" times="">to process these number calculations.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">The total number of parameters is 1593175, of which 1541275 parameters, are trained in the network. &nbsp;The average accuracy of the model was 85.86% during 1000 iterations <span style="color:red">(Figure 1)</span>.</span></span></span></span> <table align="right" class="TableGrid12" dir="rtl" style="width:701px; margin-left:-42px; border-collapse:collapse; border:none" width="701"> <tbody> <tr> <td style="width:701px; padding:0in 7px 0in 7px; height:239px" valign="top"><span style="font-size:11pt"><span 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src="file:///C:/Users/Mokhlesi/AppData/Local/Temp/msohtmlclip1/01/clip_image005.png" style="position:absolute; left:761; top:574; width:6286; height:4430" > </v:rect></v:group></v:rect></v:rect></v:rect></v:group></v:shape></v:shape></v:group><span dir="RTL" lang="FA" style="font-size:12.0pt"><span style="line-height:150%"><span b="" nazanin="" style="font-family:"><span style="color:black"></span></span></span></span></span></span></span></span></span></span></td> </tr> <tr> <td style="width:701px; padding:0in 7px 0in 7px; height:17px" valign="top"><span style="font-size:11pt"><span style="line-height:150%"><span style="unicode-bidi:embed"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times=""><span style="color:black">Figure 1. Model implementation steps</span></span><span b="" dir="RTL" nazanin="" style="font-family:"><span style="color:black"></span></span></span></span></span></span></td> </tr> </tbody> </table> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><b><span new="" roman="" style="font-family:" times=""><span style="color:black">Conclusion</span></span></b><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">The above method using fastICA, MTF, and DenseNet deep learning algorithm and combining it with an autoencoder network is new for reconstructing brain images. According to the obtained results, it can be concluded that the deep learning algorithm performs better than other superficial methods in many applications. As mentioned earlier, the deep learning algorithm is a new method yet has much potential for improvement and development. The method used in this research is a novel method in image reconstruction and there is a need to improve it. Developing methods&nbsp;can help improve the algorithm performance in future work &nbsp;&nbsp;to increase the accuracy model and a more appropriate weighting of the used neural network. Additionally, with newer deep structures that increase in number daily, their efficiency in image reconstruction using EEG signals can be evaluated and checked.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:normal"><span calibri="" style="font-family:"><b><span style="font-size:14.0pt"><span new="" roman="" style="font-family:" times="">Ethical considerations</span></span></b></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><b><span new="" roman="" style="font-family:" times="">Compliance with ethical guidelines</span></b></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">There were no ethical considerations involved in the research related to this article.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><b><span new="" roman="" style="font-family:" times="">Authors&rsquo; contributions</span></b></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">The design of the research implementation stages and the writing of the article&nbsp;were done by Mahdi Arjmand. All authors performed the research literature and background. Saeed Stayeshi, Manuchehr Kelarestaghi, and Javad Hatami completed the design and actively participated in advising and supervising the implementation of the research and writing stages.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><b><span new="" roman="" style="font-family:" times="">Funding</span></b><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">This research is not under the financial support of any institution or organization.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><b><span new="" roman="" style="font-family:" times="">Acknowledgments</span></b><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">The authors are grateful to all the dignitaries who helped us in conducting and consensus on the current research.</span><span style="font-size:12.0pt"><span style="line-height:150%"><span style="font-family:"Times New Roman",serif"></span></span></span></span></span></span><br> <span style="font-size:11pt"><span style="line-height:150%"><span calibri="" style="font-family:"><b><span new="" roman="" style="font-family:" times="">Conflict of interest</span></b></span></span></span><br> <span style="font-size:11pt"><span style="line-height:107%"><span calibri="" style="font-family:"><span new="" roman="" style="font-family:" times="">The authors declare no conflicts of interest.</span><span style="font-size:14.0pt"><span style="line-height:107%"></span></span></span></span></span><br> &nbsp; یادگیری عمیق, میدان انتقال مارکوف, DenseNet, fastICA, شبکه‌های خود رمزنگار Deep learning, Markov transfer field, DenseNet, fastICA, Autoencoder networks 105 115 http://icssjournal.ir/browse.php?a_code=A-10-1519-1&slc_lang=fa&sid=1 Mahdi Arjmand مهدی ارجمند 100319475328460014059 100319475328460014059 No PhD Candidate of Cognitive Modeling, Department of Cognitive Modeling, Institute for Cognitive Science Studies, Tehran, Iran دانشجوی دکتری مدل‌سازی شناختی، گروه مدل‌سازی شناختی، موسسه آموزش عالی علوم شناختی، تهران، ایران Saeed Setayeshi سعید ستایشی setayesh@aut.ac.ir 100319475328460014060 100319475328460014060 Yes Associate Professor, Department of Nuclear Engineering, Faculty of Physics and Energy Engineering, Amirkabir University of Technology, Tehran, Iran دانشیار گروه مهندسی هسته‌ای، دانشکده فیزیک و مهندسی انرژی، دانشگاه صنعتی امیرکبیر، تهران، ایران Manouchehr Kelarestaghi منوچهر کلارستاقی 100319475328460014061 100319475328460014061 No Assistant Professor, Department of Electrical and Computer Engineering, Technical and Engineering Faculty, Khwarazmi University, Tehran, Iran استادیار گروه مهندسی برق و کامپیوتر، دانشکده فنی و مهندسی، دانشگاه خوارزمی، تهران، ایران Javad Hatami جواد حاتمی 100319475328460014062 100319475328460014062 No Associate Professor of Psychology, University of Tehran and Higher Education Institute of Cognitive Sciences, Tehran, Iran دانشیار روان‌شناسی، دانشگاه تهران و موسسه آموزش عالی علوم شناختی، تهران، ایران