Papers by Dr P Bhuvaneswari
Lie or deception analysis is a significant challenge for investigators especially in crime cases.... more Lie or deception analysis is a significant challenge for investigators especially in crime cases. Identifying liar from normal human behaviour has higher relevance with external behaviour and cognitive functionality of human brain. Methods such as polygraph, cognitive polygraph, facial electromyography, eye tracking, voice stress analysis and functional magnetic resonance imaging have been already developed for deception analysis. Even these methods has its own merits, all these methods faces a common issue of accuracy in deception detection ratio due to different kind of liars and learned criminals. Thinking is an internal stimulus having relationship with deception. Identifying thinking responses from brain is one of the measures used to detect deception. Electroencephalography is another modality to understand cognitive responses such as thinking from human brain and this method can be extended to detect liar from other people. Statistical features such as Power, Variance and Roo...
IETE Journal of Research, 2016
ABSTRACT Epilepsy is a critical brain disease which occurs primarily based on changes or connecti... more ABSTRACT Epilepsy is a critical brain disease which occurs primarily based on changes or connectivity issues between two neurons. Electroencephalography (EEG) is an efficient modality for capturing brain signals with respect to stimuli. Total variation (TV) is an energy based feature which helps to discriminate epilepsy from normal pattern. This paper proposes an efficient epileptic detection system based on TV model. Existing methods use single feature for classification, whereas, combination of multiple relevant features helps for better understanding of epilepsy. This paper also discusses combined multi feature model with TV for efficient detection and classification of EEG signal subbands (gamma, beta, alpha, theta, and delta) using support vector machine. Results show 100% delta band based classification accuracy for combination of features such as TV and sample entropy. This research also shows that combination of multi features such as, Shannon, spectral, TV, and linear predictor coefficient gives good classification accuracy for most of the subbands.
Indian Journal of Science and Technology, 2016
Background/Objectives: Neuropathy is a disorder which will be detected using Electromyography (EM... more Background/Objectives: Neuropathy is a disorder which will be detected using Electromyography (EMG) signals. A new transformation based wavelet decomposition method is proposed in this work to categorize normal EMG signals from abnormal neuropathy disorder signals. Methods/Statistical Analysis: Transformation technique is applied to convert the signals into frequency map. Wavelet decomposition method decomposes transformed signal into set of various levels of coefficients. Cepstral feature have been applied to extract meaningful properties and Minimum Redundancy Maximum Relevance (MRMR) method has been applied to reduce dimensionality of cepstral features. Findings: The KNN classifier is used to discriminate neuropathy disorder from healthy Electromyography signals. The results shows better classification accuracy using cepstral feature set. Entire signal has been subdivided into 20 and 40 sub segments for better features. Coefficients for five levels have been extracted where 40 sub segment features shows better classification accuracy than 20 sub segments. In some cases, 3rd and 5th level coefficients of 20 sub segments shows better classification. Application/Improvements: This study helps to detect abnormal EMG signal from normal patterns which helps radiologist for better prediction of various disorders based on EMG signals.
2014 IEEE International Conference on Computational Intelligence and Computing Research, 2014
Understanding cognitive responses of human brain is one of the significant research fields where ... more Understanding cognitive responses of human brain is one of the significant research fields where electroencephalography plays vital role in analyzing brain functionality with respect to brain signals. Electromyography is another modality to understand cognitive responses with respect to muscle activation. In this research work, a data set consists of healthy and myopathy has been considered from physionet data repository. Signal has been decomposed using wavelet transformation. Features such as Shannon, spectral and approximate entropy have been extracted from decomposed signal. Support vector machine has been used for classification. Result shows that first level and third level coefficient shows better classification accuracy than other components. Spectral entropy has good classification results than Shannon and approximate entropy.
Electroencephalography is a proficient modality to measure the signals from cerebral cortex. Thes... more Electroencephalography is a proficient modality to measure the signals from cerebral cortex. These signals are helpful to analyze many neurological and psychological disorders. During recording, signals are influenced with noise contamination which yields poor classification accuracy. Noise contamination may reflect in all neighboring electrodes which has higher significance in prediction of normal and abnormal group. Principal Component Analysis (PCA) is one of the nonlinear tool which works well for removing artifacts and noises in nonlinear EEG signals. This paper explains various influences of PCA in Electroencephalography signal analysis. This paper also addresses artifact signal and its removal method.
Emotion plays an important role in day to day activity in human life. Human psychological behavio... more Emotion plays an important role in day to day activity in human life. Human psychological behaviors tied up with emotional responses are associated with brain lobes. These responses will reflect in brain waves which are captured through Electroencephalography signals. Lie detection is a challenging issue in crime investigation. Learned liar can be identified with intelligent technology in the form of brain waves. Teeth biting is one of the emotional responses happened during investigation. In this experimental study, various features based on time domain (Shannon), frequency domain (Spectral), both time and frequency domain (Wavelet) and dynamic state space reconstruction (Fractal Dimension) are analyzed in feature extraction phase. For classification, intelligent machine learning techniques such as Support Vector Machine (SVM) and Neural Network (NN) are used to categorize teeth biting activity from normal behavior.
Electroencephalography is the commonly used technique for recording brainwave to diagnose brain d... more Electroencephalography is the commonly used technique for recording brainwave to diagnose brain disorders. The quality of recording helps to identify the pathology and cognitive behavior of the brain. EEG contain various types of artifacts which influence in recording. Hence it has been removed using Principal Component Analysis (PCA).
Procedia Computer Science, 2015
Epilepsy is a critical brain disorder which can be detected through the signals captured from the... more Epilepsy is a critical brain disorder which can be detected through the signals captured from the brain. Electroencephalography is an efficient method used to capture signals from the brain. K nearest neighbor is one of the simplest methods used for classi fying epilepsy patterns. Classification of the epilepsy signal from normal pattern will be primarily based on features extracted from brain signals. This paper discusses statistical based linear feature extraction methods such as Root Mean Square, Variance and Linear Prediction Coefficient. This paper also focuses influence of decision rules such as consensus and majority rule in the classification of epilepsy data set. Results show better classification with respect to increased k value.
International Journal of Computer Applications, 2013
Support Vector Machine (SVM) is one of the popular Machine Learning techniques for classifying th... more Support Vector Machine (SVM) is one of the popular Machine Learning techniques for classifying the Electroencephalography (EEG) signals based on the neuronal activity of the brain. EEG signals are represented into high dimensional feature space for analyzing the brain activity. Kernel functions are helpful for efficient implementation of non linear mapping. This paper gives an overview of classification techniques available in Support Vector Machine. This paper also focus role of SVM on EEG signal analysis.
Support Vector Machine (SVM) is one of the popular Machine Learning techniques for classifying th... more Support Vector Machine (SVM) is one of the popular Machine Learning techniques for classifying the Electroencephalography (EEG) signals based on the neuronal activity of the brain. EEG signals are represented into high dimensional feature space for analyzing the brain activity. Kernel functions are helpful for efficient implementation of non linear mapping. This paper gives an overview of classification techniques available in Support Vector Machine. This paper also focus role of SVM on EEG signal analysis.
Procedia Engineering, 2012
Uploads
Papers by Dr P Bhuvaneswari