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![Figure-2. Example of abstraction in deep learning. Extracted from [18].](profile/Mohd-Suhaimi-Nur-Farahana/publication/288229443/figure/fig1/AS:629987266801664@1527212077037/Figure-2-Example-of-abstraction-in-deep-learning-Extracted-from-18_Q320.jpg)
Brain as main server for entire human body is a complex composition. It is a challenging task to read and interpret the brain. Functional magnetic resonance imaging (fMRI) has become one of the means to do the task. fMRI is a noninvasive technique to measure brain activity of a human subject according to various stimuli. However, the fMRI datasets...
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Citations
... [195] [195] [196] [197] fNIRS [38], [198], [199], [71], [200] [198] [201] fMRI [202,203] [204], [63], [205], [206], [117], [207], [208,194] [209] [210], [211], [210], [212,213] [ 214,215], [216,203] [217] ...
... (2) Representative models. A wide range of publications demonstrated the effectiveness of representative models in recognition of fMRI data [213]. Hu et al. [217] used demonstrated that deep learning outperforms other machine learning methods in the diagnosis of neurological disorders such as Alzheimer's disease. ...
Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide a comprehensive survey of the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.
... Comparisons were here established for the classification of ADHD, memory encoding, memory encoding, memory decoding and brain parcellation. It also seems that the Z_axis view images were utilized [11]. In 2017, Meszlényi et al. proposed a Connectome-Convolutional Neural Network (CCNN). ...
The scan of functional Magnetic Resonance Imaging (fMRI) can provide three views for brain activities. These views are basically the X_axis (sagittal Plane), Y_axis (coronal plane) and Z_axis (axial plane). To the best of the obtained knowledge, studying brain activities for all of these views has not been considered before together with Deep Learning (DL) techniques. In this paper, various DL models named the X_axis Classification Model (XCM), Y_axis Classification Model (YCM) and Z_axis Classification Model (ZCM) are proposed. Each of these models is able to classify between the vision, movement and forward brain activities. Extensive experiments are performed for examining their parameters. The designed models have the capability to automatically detect the important features without any human supervision. In addition, they can provide intelligent decisions or classifications. Furthermore, effective combination method is suggested based on the Genetic Algorithm (GA) and Genetic Weighted Summation (GWS) rule, where high performances of outcomes can be achieved. After extensive experiments, the accuracies of 91.67%, 89.88% and 91.67% have been obtained for the XCM, YCM and ZCM, respectively. In addition, the accuracy has been raised to 97.22% by applying the suggested fusion method. .
... Furthermore, the Haxby data consists of six subjects with 1452-time step each. Since data is huge, researchers usually employ tedious preprocessing stepsfor SPM and general linear model approach such as slice timing correction, realignment, normalization, smoothing, denoising, and skull stripping [11], [12]. However, minimal preprocessing is opted for this research to reduce the processing time. ...
Machine learning has opened up the opportunity for understanding how the brain works. In this paper, functional magnetic resonance imaging (fMRI) data are analyzed with reduced dimension. We have carried out a performance comparison of random projection (RP) and principal component analysis (PCA) with different number of components of fMRI data. In addition to that, six different types of machine learning algorithm have been used. In particular, the Haxby dataset is chosen for our experiment. The dataset comprises 9 classes for object recognition. 10-fold cross validation step has been employed. We have discovered that RP outperforms PCA when the former is paired with logistic regression, Gaussian Naive Bayes and linear support vector machine. The best pair for this study was found to be PCA and k-nearest neighbors. Nevertheless, each algorithm was found to have its own strengths for fMRI classification approach.
This study explores the patterns of activation in brain regions toward classifying decision making voxels from among four major Brodmann areas (BAs) upon stimulus of visual tasks. Toward this goal, a well-known clustering analysis has been performed on real-time data of the human brain obtained using functional magnetic resonance imaging (fMRI). The functional connectivity among various brain regions was detected by leveraging a distance correlation graph. Graphical methods have been employed to visualize the clusters elicited in the process. The analysis of the results sheds new light on how four significantly activated BAs of the brain exhibit effective connectively to perform a visual task in the context of decision making.
