Multi-classification of action intention understanding brain signals based on thresholding graph metric

2021 ◽  
pp. 1-8
Author(s):  
Qian Cai ◽  
Xingliang Xiong ◽  
Weiqiang Gong ◽  
Haixian Wang
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Binary Classification ◽  
Extraction Procedure ◽  
Graph Metrics ◽  
Brain Signals ◽  
Intention Understanding ◽  
Multi Classification ◽  
Action Intention

BACKGROUND: Classification of action intention understanding is extremely important for human computer interaction. Many studies on the action intention understanding classification mainly focus on binary classification, while the classification accuracy is often unsatisfactory, not to mention multi-classification. METHOD: To complete the multi-classification task of action intention understanding brain signals effectively, we propose a novel feature extraction procedure based on thresholding graph metrics. RESULTS: Both the alpha frequency band and full-band obtained considerable classification accuracies. Compared with other methods, the novel method has better classification accuracy. CONCLUSIONS: Brain activity of action intention understanding is closely related to the alpha band. The new feature extraction procedure is an effective method for the multi-classification of action intention understanding brain signals.

scholarly journals Feature Extraction and Classification of Citrus Juice by Using an Enhanced L-KSVD on Data Obtained from Electronic Nose

Sensors ◽  
2019 ◽  
Vol 19 (4) ◽  
pp. 916 ◽  
Author(s):  
Wen Cao ◽  
Chunmei Liu ◽  
Pengfei Jia
Keyword(s):  
Feature Extraction ◽  
Kernel Function ◽  
Electronic Nose ◽  
Classification Accuracy ◽  
Extraction Methods ◽  
Object Function ◽  
Optimal Value ◽  
Processed Products

Aroma plays a significant role in the quality of citrus fruits and processed products. The detection and analysis of citrus volatiles can be measured by an electronic nose (E-nose); in this paper, an E-nose is employed to classify the juice which is stored for different days. Feature extraction and classification are two important requirements for an E-nose. During the training process, a classifier can optimize its own parameters to achieve a better classification accuracy but cannot decide its input data which is treated by feature extraction methods, so the classification result is not always ideal. Label consistent KSVD (L-KSVD) is a novel technique which can extract the feature and classify the data at the same time, and such an operation can improve the classification accuracy. We propose an enhanced L-KSVD called E-LCKSVD for E-nose in this paper. During E-LCKSVD, we introduce a kernel function to the traditional L-KSVD and present a new initialization technique of its dictionary; finally, the weighted coefficients of different parts of its object function is studied, and enhanced quantum-behaved particle swarm optimization (EQPSO) is employed to optimize these coefficients. During the experimental section, we firstly find the classification accuracy of KSVD, and L-KSVD is improved with the help of the kernel function; this can prove that their ability of dealing nonlinear data is improved. Then, we compare the results of different dictionary initialization techniques and prove our proposed method is better. Finally, we find the optimal value of the weighted coefficients of the object function of E-LCKSVD that can make E-nose reach a better performance.

Feature Extraction and Classification of Images Based on Corner Invariant Moments

2013 ◽  
Vol 475-476 ◽  
pp. 374-378
Author(s):  
Xue Ming Zhai ◽  
Dong Ya Zhang ◽  
Yu Jia Zhai ◽  
Ruo Chen Li ◽  
De Wen Wang
Keyword(s):  
Feature Extraction ◽  
Classification Accuracy ◽  
Image Feature ◽  
Support Vector ◽  
System Management ◽  
Image Feature Extraction ◽  
Invariant Moments ◽  
Harris Corner ◽  
Classification Of Images

Image feature extraction and classification is increasingly important in all sectors of the images system management. Aiming at the problems that applying Hu invariant moments to extract image feature computes large and too dimensions, this paper presented Harris corner invariant moments algorithm. This algorithm only calculates corner coordinates, so can reduce the corner matching dimensions. Combined with the SVM (Support Vector Machine) classification method, we conducted a classification for a large number of images, and the result shows that using this algorithm to extract invariant moments and classifying can achieve better classification accuracy.

scholarly journals Action Intention Understanding EEG Signal Classification Based on Improved Discriminative Spatial Patterns

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xingliang Xiong ◽  
Hua Yu ◽  
Haixian Wang ◽  
Jiuchuan Jiang
Keyword(s):  
Feature Extraction ◽  
Spatial Patterns ◽  
Extraction Method ◽  
Signal Classification ◽  
Eeg Signal ◽  
Feature Extraction Method ◽  
Intention Understanding ◽  
Eeg Signal Classification ◽  
New Feature ◽  
Action Intention

Objective. Action intention understanding EEG signal classification is indispensable for investigating human-computer interactions and intention understanding mechanisms. Numerous investigations on classification tasks extract classification features by using graph theory metrics; however, the classification results are usually not good. Method. To effectively implement the task of action intention understanding EEG signal classification, we proposed a new feature extraction method by improving discriminative spatial patterns. Results. The whole frequency band and fusion band achieved satisfactory classification accuracies. Compared with other authors’ methods for action intention understanding EEG signal classification, the new method performs more satisfactorily in some aspects. Conclusions. The new feature extraction method not only effectively avoids complex values when solving the generalized eigenvalue problem but also perfectly realizes appreciable classification accuracies. Fusing the classification features of different frequency bands is a useful strategy for the classification task.

Disease Classification Using ECG Signals Based on R-Peak Analysis With ABC and ANN

2019 ◽  
Vol 8 (2) ◽  
pp. 67-86 ◽  
Author(s):  
Suman Lata ◽  
Rakesh Kumar
Keyword(s):  
Feature Extraction ◽  
Comparative Analysis ◽  
Classification Accuracy ◽  
Disease Classification ◽  
Cardiac Diseases ◽  
Ecg Signals ◽  
Processing Steps ◽  
Ann Classification ◽  
Selection Of

ECG feature extraction has an important role in identifying a number of cardiac diseases. Lots of work has been done in this field but the most important challenges faced in previous work are the selection of proper R-peaks and R-R intervals due to the lack of appropriate pre-processing steps like decomposition, smoothing, filtering, etc., and the optimization of the features for proper classification. In this article, DWT-based pre-processing and ABC is used for optimization of features which helps to achieve better classification accuracy. It is utilized for initial diagnosis of abnormalities. The signals are taken from MIT-BIH arrhythmia database for the analysis. The aim of the research is to classification of six diseases; Normal, Atrial, Paced, PVC, LBBB, RBBB with an ABC optimization algorithm and an ANN classification algorithm on the basis of the extracted features. Various parameters, like, FAR, FRR, and accuracy are measured for the execution. Comparative analysis is shown of the proposed and the existing work to depict the effectiveness of the work.

Classification of Brain Signals of Event Related Potentials using Different Methods of Feature Extraction

2017 ◽  
Vol 8 (4) ◽  
pp. 680-686
Author(s):  
Ishfaque Ahmed ◽  
Muhammad Jahangir ◽  
Syed Tanveer Iqbal ◽  
Muhammad Azhar ◽  
Imran Siddiqui
Keyword(s):  
Feature Extraction ◽  
Event Related Potentials ◽  
Brain Signals ◽  
Related Potentials

scholarly journals Modified GAN Augmentation Algorithms for the MRI-Classification of Myocardial Scar Tissue in Ischemic Cardiomyopathy

2021 ◽  
Vol 8 ◽  
Author(s):  
Umesh C. Sharma ◽  
Kanhao Zhao ◽  
Kyle Mentkowski ◽  
Swati D. Sonkawade ◽  
Badri Karthikeyan ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Binary Classification ◽  
Coronary Revascularization ◽  
Scar Tissue ◽  
Myocardial Scar ◽  
Automated Classification ◽  
Multicenter Studies ◽  
Generative Adversarial Network ◽  
Therapeutic Decisions

Contrast-enhanced cardiac magnetic resonance imaging (MRI) is routinely used to determine myocardial scar burden and make therapeutic decisions for coronary revascularization. Currently, there are no optimized deep-learning algorithms for the automated classification of scarred vs. normal myocardium. We report a modified Generative Adversarial Network (GAN) augmentation method to improve the binary classification of myocardial scar using both pre-clinical and clinical approaches. For the initial training of the MobileNetV2 platform, we used the images generated from a high-field (9.4T) cardiac MRI of a mouse model of acute myocardial infarction (MI). Once the system showed 100% accuracy for the classification of acute MI in mice, we tested the translational significance of this approach in 91 patients with an ischemic myocardial scar, and 31 control subjects without evidence of myocardial scarring. To obtain a comparable augmentation dataset, we rotated scar images 8-times and control images 72-times, generating a total of 6,684 scar images and 7,451 control images. In humans, the use of Progressive Growing GAN (PGGAN)-based augmentation showed 93% classification accuracy, which is far superior to conventional automated modules. The use of other attention modules in our CNN further improved the classification accuracy by up to 5%. These data are of high translational significance and warrant larger multicenter studies in the future to validate the clinical implications.

scholarly journals Classification of Power Quality Disturbance Based on Multiscale Singular Spectral Analysis and Multi Resolution Wavelet Transforms

2019 ◽  
Vol 8 (4) ◽  
pp. 6654-6659
Keyword(s):  
Feature Extraction ◽  
Spectral Analysis ◽  
Power Quality ◽  
Classification Accuracy ◽  
Wavelet Transforms ◽  
Back Propagation ◽  
Renewable Energy Resources ◽  
Statistical Parameters ◽  
Singular Spectral Analysis

In real power system, Power quality disturbances (PQDs) have become major challenge due to the introduction of renewable energy resources and embedded power systems. In this research, two novel feature extraction methods multi resolution analysis wavelet transform (MRA-WT) and Multiscale singular spectral analysis (MSSA) have been analysed with convolution neural network classifier for the classification of PQDs. Statistical parameters are also applied for the optimal feature selection. MSSA is time-series tool and MRA-WT are applied for feature extraction and 1-dimensional CNN (1-DCNN) is used to classify the single and multiple PQDs. The architecture is built with forward propagation and back propagation is utilized to tune the data. Finally, the results of two selected feature extraction techniques are compared with classification accuracy. The simulation based results explained that MSSA with 1-DCNN has significantly higher classification accuracy under different noisy conditions.

Classification of Mammogram Abnormalities Using Legendre Moments

2020 ◽  
pp. 2150010
Author(s):  
Rodrigo Dalvit C. Silva ◽  
Thomas R. Jenkyn
Keyword(s):  
Image Analysis ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Nearest Neighbors ◽  
Accuracy Rate ◽  
Feature Extractor ◽  
Analysis Society ◽  
Mammogram Image ◽  
Legendre Moments

In this paper, the issue of classifying mammogram abnormalities using images from an mammogram image analysis society (MIAS) database is discussed. We compare a feature extractor based on Legendre moments (LMs) with six other feature extractors. To determine the best feature extractor, the performance of each was compared in terms of classification accuracy rate and extraction time using a [Formula: see text]-nearest neighbors ([Formula: see text]-NN) classifier. This study shows that feature extraction using LMs performed best with an accuracy rate over 84% and requiring relatively little time for feature extraction, on average only 1[Formula: see text]s.

HALF-WAVE SEGMENT FEATURE EXTRACTION OF EEG SIGNALS OF PATIENTS WITH DEPRESSION AND PERFORMANCE EVALUATION OF NEURAL NETWORK CLASSIFIERS

2017 ◽  
Vol 17 (01) ◽  
pp. 1750006 ◽  
Author(s):  
SUBHA D. PUTHANKATTIL ◽  
PAUL K. JOSEPH
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Time Domain ◽  
Classification Accuracy ◽  
Discrete Wavelet ◽  
Eeg Signals ◽  
Wavelet Entropy ◽  
Wavelet Energy ◽  
Significant Difference

A detailed understanding of key signal characteristics has enabled the use of artificial neural networks (ANN) for feature detection and classification of EEG signals in clinical research. The present study is performed to classify EEG signals of normal and depression patients with wavelet parameters as key input features. The characteristics of depression cannot be made out by visual inspection of EEG records unlike epilepsy which is well characterized by sudden recurrent and transient waveforms. In this study, a comparison is made between the performance of feedforward neural network (FFNN) and probabilistic neural network (PNN) while classifying the EEG signals of normal and depression patients. Classification capabilities of both the methods are validated with the EEG recordings from 30 normal controls and 30 depression patients. One-way ANOVA provided a statistical significant difference between the two classes of EEG signals recorded. Preprocessing for feature extraction is done using discrete wavelet transform (DWT). The time domain and relative wavelet energy (RWE) features calculated from the sub-bands are given as a set of input to the neural network. Another set of feature used independently for training the network is the wavelet entropy (WE). The FFNN achieved a classification accuracy of 100% and PNN gave an accuracy of 58.75% with time domain and wavelet energy as the input features. With wavelet entropy as the input feature, FFNN further showed 98.75% classification accuracy while PNN gave an accuracy of only 46.5%. The results indicate that FFNN with the given input features is more suitable for the classification of EEG signals with mood changing depressive disorders.

scholarly journals Classification of Aggressive Behaviors Based on sEMG Feature Extraction and Machine Learning Algorithm

2020 ◽  
Vol 4 (Supplement_1) ◽  
pp. 656-656
Author(s):  
Youngjun Kim ◽  
Uchechukuwu David ◽  
Yeonsik Noh
Keyword(s):  
Machine Learning ◽  
Feature Extraction ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Machine Learning Algorithm ◽  
Intrinsic Mode Functions ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Learning Machine

Abstract New surface electromyography (sEMG) feature extraction approach combined with Empirical Mode Decomposition (EMD) and Dispersion Entropy (DisEn) is proposed for classifying aggressive and normal behaviors from sEMG data. In this study, we used the sEMG physical action dataset from the UC Irvine Machine Learning repository. The raw sEMG was decomposed with EMD to obtain a set of Intrinsic Mode Functions (IMF). The IMF, which includes the most discriminant feature for each action, was selected based on the analysis by Hibert Transform (HT) in the time-frequency domain. Next, the DisEn of the selected IMF was calculated as a corresponding feature. Finally, the DisEn value was tested using five different classifiers, such as LDA, Quadratic DA, k-NN, SVM, and Extreme Learning Machine (ELM) for the classification task. Among these ML algorithms, we achieved classification accuracy, sensitivity, and specificity with ELM as 98.44%, 100%, and 96.72%, respectively.

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