Meta-cognitive Radial Basis Function Network Research Articles

Investigation of deep convolutional neural network for classification of motor imagery fNIRS signals for BCI applications

Brain-Computer Interface (BCI) is a promising technology that enables people affected by neuromuscular disorders to control external devices like a wheelchair or prosthesis with the help of their brain signals. Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging technique that describes the brain hemodynamics. Recently, it has been investigated for the development of BCI application. In the current study, fNIRS signals that report changes in hemoglobin concentrations acquired during imagination of executing four motor tasks namely, right-fist clenching, left-fist clenching, right- and left-foot tapping and rest have been explored. Besides employing conventional machine learning algorithms used in BCI field such as Support Vector Machine (SVM), Multilayer Perceptron (MLP) neural network, and Projection-based learning in a Meta-cognitive Radial Basis Function Network (PBL-McRBFN), a deep learning approach namely, Convolutional Neural Network (CNN) has been explored to classify the motor imagery fNIRS signals. Also, the selection of the best input image representation of the one-dimensional fNIRS signal to be used for CNN architecture is investigated. In comparison to conventional machine learning algorithms, the performance of CNN was superior with respect to the classification of four-class motor imagery fNIRS signals, with an average classification accuracy value of 72.35 ± 4.4%. The results show that the classification of fNIRS signals is possible using deep learning approach for the development of BCI application specifically using spectrogram representation of the fNIRS signal.

Computer Aided Detection of Imaging Biomarkers for Alzheimers Disease

In this paper, we present a novel approach for the computer aided detection of imaging biomarkers responsible for Alzheimer's disease (AD) from magnetic resonance imaging (MRI) using meta-cognitive radial basis function network (McRBFN) classifier. The McRBFN classifier uses voxel-based morphometric features extracted from MRI and employs a sequential projection-based learning (PBL) algorithm for classification. We propose a recursive feature elimination approach (called PBL-McRBFN-RFE) to identify the most relevant and meaningful imaging biomarkers for AD detection. The study has been conducted using the well-known open access series of imaging studies dataset. The brain regions identified by the PBL-McRBFNRFE feature selection approach include hippocampus, parahippocampal gyrus, superior temporal gyrus, insula, precentral gyrus and extra nuclear, which have also been reported as critical regions in the medical literature. Further, we also conducted a study based on the age to identify the brain regions responsible for the onset of AD.

A Survey on Study of Various Machine Learning Methods for Classification

This article comprises a review of various sequential algorithms. The review represents the working nature of the learning methods. It also includes the methods such as Minimal Resource Allocation Network (MRAN), Extreme Learning Machine (ELM), Self-regulated Resource Allocation Network (SRAN) and Meta-Cognitive Neural Network (MCNN) for real –valued neural network. Projection Based Learning with MetaCognitive Radial Basis Function Network (PBL-McRBFN) for complex valued neural network. Finally about Meta-Cognitive Fuzzy Inference System (MCFIS) using the Neuro - Fuzzy inference system for learning. The previously said SRAN works on the basis of self – regulatory mechanism in order to reduce the huge loss error and to maximize the class – wise significance. The methods such as MCNN, PBL-McRBFN and MCFIS execute on the human learning strategies such as what – to-learn, when –to-learn and how –to – learn. This review helps to select the learning methods suitable for the data that is to be classified.

Accurate detection of autism spectrum disorder from structural MRI using extended metacognitive radial basis function network

In this paper, we present an accurate detection of Autism Spectrum Disorder (ASD) from structural MRI using an Extended Metacognitive Radial Basis Function Neural Classifier (EMcRBFN). An automatic whole brain Voxel Based Morphometry (VBM) approach is used to identify gray matter composition in the brain from structural Magnetic Resonance Imaging (MRI) and an improved q-Gaussian classifier and its metacognitive learning algorithm has been proposed to approximate the functional relationship between the high dimensional VBM features and the true class labels. Recent genetic studies indicate that ASD manifests in different ways between males and females and also between adolescents and adults. Accordingly, the proposed EMcRBFN classifier has been evaluated using the publicly available Autism Brain Imaging Data Exchange dataset with a comprehensive study on both males and females and also between adolescents and adults in both categories. EMcRBFN classifier performance is compared with currently existing results for ASD classification in the literature and also with well known standard classifiers. The results clearly indicate that the performance of the EMcRBFN classifier is better than that of the other classifiers considered in this study. Further, the comprehensive study also indicates that the following subregions in the brain viz., premotor cortex and supplementary motor cortex are affected for adult-females while the somatosensory cortex subregion is affected for adolescent-females with ASD. Similar results indicate that the precentral gyrus, motor cortex, medial frontal gyrus and the paracentral lobule areas are affected for adolescent males while the superior frontal gyrus and the frontal eye fields areas are affected for adult males with ASD.

Subject independent human action recognition using spatio-depth information and meta-cognitive RBF network

In this paper, we present a machine learning approach for subject independent human action recognition using depth camera, emphasizing the importance of depth in recognition of actions. The proposed approach uses the flow information of all 3 dimensions to classify an action. In our approach, we have obtained the 2-D optical flow and used it along with the depth image to obtain the depth flow (Z motion vectors). The obtained flow captures the dynamics of the actions in space–time. Feature vectors are obtained by averaging the 3-D motion over a grid laid over the silhouette in a hierarchical fashion. These hierarchical fine to coarse windows capture the motion dynamics of the object at various scales. The extracted features are used to train a Meta-cognitive Radial Basis Function Network (McRBFN) that uses a Projection Based Learning (PBL) algorithm, referred to as PBL-McRBFN, henceforth. PBL-McRBFN begins with zero hidden neurons and builds the network based on the best human learning strategy, namely, self-regulated learning in a meta-cognitive environment. When a sample is used for learning, PBL-McRBFN uses the sample overlapping conditions, and a projection based learning algorithm to estimate the parameters of the network. The performance of PBL-McRBFN is compared to that of a Support Vector Machine (SVM) and Extreme Learning Machine (ELM) classifiers with representation of every person and action in the training and testing datasets. Performance study shows that PBL-McRBFN outperforms these classifiers in recognizing actions in 3-D. Further, a subject-independent study is conducted by leave-one-subject-out strategy and its generalization performance is tested. It is observed from the subject-independent study that McRBFN is capable of generalizing actions accurately. The performance of the proposed approach is benchmarked with Video Analytics Lab (VAL) dataset and Berkeley Multi-modal Human Action Database (MHAD).

A novel PBL-McRBFN-RFE approach for identification of critical brain regions responsible for Parkinson’s disease

In this paper, we present a novel approach for the identification of critical brain regions responsible for Parkinson’s disease (PD) based on magnetic resonance images (MRI) using meta-cognitive radial basis function network (McRBFN) classifier with Recursive Feature Elimination (RFE). The McRBFN classifier uses voxel based morphometric (VBM) features extracted from MRI and employs a projection based learning (PBL) algorithm. The meta-cognitive learning present in PBL-McRBFN helps in selecting proper samples to learn based on its current knowledge and evolve the architecture automatically. Since, the classifier developed using PBL-McRBFN is efficient, we propose recursive feature elimination approach (called PBL-McRBFN-RFE) to identify most relevant brain regions responsible for PD prediction.The study has been conducted using the Parkinson’s Progression Markers Initiative (PPMI) data set. First, we conducted the study on PD prediction using the PBL-McRBFN classifier on the PPMI data set. We have also compared the results of the PBL-McRBFN classifier with the support vector machine (SVM) classifier. The study results clearly show that the PBL-McRBFN classifier produces better generalization performance on PD prediction. Finally, we use RFE approach with PBL-McRBFN to identify the brain regions responsible for PD. The PBL-McRBFN-RFE selected features indicate that the loss of gray matter in the superior temporal gyrus region may be responsible for the onset of PD, and is consistent with the earlier findings from medical research studies.

Meta-cognitive RBF Network and its Projection Based Learning algorithm for classification problems

‘Meta-cognitive Radial Basis Function Network’ (McRBFN) and its ‘Projection Based Learning’ (PBL) algorithm for classification problems in sequential framework is proposed in this paper and is referred to as PBL-McRBFN. McRBFN is inspired by human meta-cognitive learning principles. McRBFN has two components, namely the cognitive component and the meta-cognitive component. The cognitive component is a single hidden layer radial basis function network with evolving architecture. In the cognitive component, the PBL algorithm computes the optimal output weights with least computational effort by finding analytical minima of the nonlinear energy function. The meta-cognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. In addition, sample overlapping conditions are considered for proper initialization of new hidden neurons, thus minimizes the misclassification. The interaction of cognitive component and meta-cognitive component address the what-to-learn, when-to-learn and how-to-learn human learning principles efficiently. The performance of the PBL-McRBFN is evaluated using a set of benchmark classification problems from UCI machine learning repository and two practical problems, viz., the acoustic emission signal classification and the mammogram for cancer classification. The statistical performance evaluation on these problems has proven the superior performance of PBL-McRBFN classifier over results reported in the literature.

Parkinson’s disease prediction using gene expression – A projection based learning meta-cognitive neural classifier approach

In this paper, we propose a gene expression based approach for the prediction of Parkinson’s disease (PD) using ‘projection based learning for meta-cognitive radial basis function network (PBL-McRBFN)’. McRBFN is inspired by human meta-cognitive learning principles. McRBFN has two components, a cognitive component and a meta-cognitive component. The cognitive component is a radial basis function network with evolving architecture. In the cognitive component, the PBL algorithm computes the optimal output weights with least computational effort. The meta-cognitive component controls the learning process in the cognitive component by choosing the best learning strategy for the current sample and adapts the learning strategies by implementing self-regulation. The interaction of cognitive component and meta-cognitive component address the what-to-learn, when-to-learn and how-to-learn of human learning principles efficiently.PBL-McRBFN classifier is used to predict PD using micro-array gene expression data obtained from ParkDB database. The performance of PBL-McRBFN classifier has been evaluated using Independent Component Analysis (ICA) reduced features sets from the complete genes and selected genes with two different significance levels. Further, the performance of PBL-McRBFN classifier is statistically compared with existing classifiers using one-way repeated ANOVA test. Further, it is also used in PD prediction using the standard vocal and gait PD data sets. In all these data sets, the performance of PBL-McRBFN is compared against existing results in the literature. Performance results clearly highlight the superior performance of our proposed approach.