EEG efficient classification of imagined hand movement using RBF kernel SVM

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km2 area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently.

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Optimizing low-frequency common spatial pattern features for multi-class classification of hand movement directions

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) ◽

10.1109/embc.2013.6610117 ◽

2013 ◽

Cited By ~ 3

Author(s):

Andrew Keong Ng ◽

Kai Keng Ang ◽

Keng Peng Tee ◽

Cuntai Guan

Keyword(s):

Spatial Pattern ◽

Hand Movement ◽

Low Frequency ◽

Common Spatial Pattern ◽

Multi Class Classification

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Hilbert transform and rbf-kernel based support vector machine synergy for automatic classification of eeg signals

International Journal of Latest Trends in Engineering and Technology ◽

10.21172/1.93.04 ◽

2018 ◽

Vol 9 (3) ◽

Keyword(s):

Support Vector Machine ◽

Hilbert Transform ◽

Automatic Classification ◽

Support Vector ◽

Eeg Signals ◽

Rbf Kernel

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GA_SVM

Handbook of Research on Soft Computing and Nature-Inspired Algorithms - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2128-0.ch012 ◽

2017 ◽

pp. 359-397 ◽

Cited By ~ 3

Author(s):

Dilip Kumar Choubey ◽

Sanchita Paul

Keyword(s):

Diagnostic System ◽

Computation Time ◽

Modern Society ◽

Attribute Selection ◽

Polynomial Kernel ◽

Sigmoid Function ◽

Linear Kernel ◽

Rbf Kernel ◽

Life Threatening ◽

Kernel Svm

The modern society is prone to many life-threatening diseases which if diagnosis early can be easily controlled. The implementation of a disease diagnostic system has gained popularity over the years. The main aim of this research is to provide a better diagnosis of diabetes. There are already several existing methods, which have been implemented for the diagnosis of diabetes. In this manuscript, firstly, Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel, Linear Kernel SVM used for the classification of PIDD. Secondly GA used as an Attribute selection method and then used Polynomial Kernel, RBF Kernel, Sigmoid Function Kernel, Linear Kernel SVM on that selected attributes of PIDD for classification. So, here compared the results with and without GA in PIDD, and Linear Kernel proved better among all of the noted above classification methods. It directly seems in the paper that GA is removing insignificant features, reducing the cost and computation time and improving the accuracy, ROC of classification. The proposed method can be also used for other kinds of medical diseases.

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