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Murugappan Murugappan

Bio: Murugappan Murugappan is an academic researcher from Universiti Malaysia Perlis. The author has contributed to research in topics: Emotion classification & Wavelet. The author has an hindex of 30, co-authored 109 publications receiving 3492 citations.


Papers
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Journal ArticleDOI
07 Dec 2012-Sensors
TL;DR: It is concluded that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsy level of a driver.
Abstract: In recent years, driver drowsiness has been one of the major causes of road accidents and can lead to severe physical injuries, deaths and significant economic losses. Statistics indicate the need of a reliable driver drowsiness detection system which could alert the driver before a mishap happens. Researchers have attempted to determine driver drowsiness using the following measures: (1) vehicle-based measures; (2) behavioral measures and (3) physiological measures. A detailed review on these measures will provide insight on the present systems, issues associated with them and the enhancements that need to be done to make a robust system. In this paper, we review these three measures as to the sensors used and discuss the advantages and limitations of each. The various ways through which drowsiness has been experimentally manipulated is also discussed. We conclude that by designing a hybrid drowsiness detection system that combines non-intusive physiological measures with other measures one would accurately determine the drowsiness level of a driver. A number of road accidents might then be avoided if an alert is sent to a driver that is deemed drowsy.

583 citations

Journal ArticleDOI
TL;DR: The average classification rate and subsets of emotions classification rate of two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA), are presented for justifying the performance of the emotion recognition system.
Abstract: In this paper, we summarize the human emotion recognition using different set of electroencephalogram (EEG) channels using discrete wavelet transform. An audio-visual induction based protocol has been designed with more dynamic emotional content for inducing discrete emotions (disgust, happy, surprise, fear and neutral). EEG signals are collected using 64 electrodes from 20 subjects and are placed over the entire scalp using International 10-10 system. The raw EEG signals are preprocessed using Surface Laplacian (SL) filtering method and decomposed into three different frequency bands (alpha, beta and gamma) using Discrete Wavelet Transform (DWT). We have used “db4” wavelet function for deriving a set of conventional and modified energy based features from the EEG signals for classifying emotions. Two simple pattern classification methods, K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA) methods are used and their performances are compared for emotional states classification. The experimental results indicate that, one of the proposed features (ALREE) gives the maximum average classification rate of 83.26% using KNN and 75.21% using LDA compared to those of conventional features. Finally, we present the average classification rate and subsets of emotions classification rate of these two different classifiers for justifying the performance of our emotion recognition system.

408 citations

Journal ArticleDOI
TL;DR: An automated detection system for Parkinson’s disease employing the convolutional neural network (CNN) employing the thirteen-layer CNN architecture which can overcome the need for the conventional feature representation stages is proposed.
Abstract: An automated detection system for Parkinson’s disease (PD) employing the convolutional neural network (CNN) is proposed in this study. PD is characterized by the gradual degradation of motor function in the brain. Since it is related to the brain abnormality, electroencephalogram (EEG) signals are usually considered for the early diagnosis. In this work, we have used the EEG signals of twenty PD and twenty normal subjects in this study. A thirteen-layer CNN architecture which can overcome the need for the conventional feature representation stages is implemented. The developed model has achieved a promising performance of 88.25% accuracy, 84.71% sensitivity, and 91.77% specificity. The developed classification model is ready to be used on large population before installation of clinical usage.

317 citations

Proceedings ArticleDOI
04 Mar 2011
TL;DR: An insight on the current state of research and its challenges on emotion recognition using physiological signals, so that research can be advanced to obtain better recognition.
Abstract: Recent research in the field of Human Computer Interaction aims at recognizing the user's emotional state in order to provide a smooth interface between humans and computers. This would make life easier and can be used in vast applications involving areas such as education, medicine etc. Human emotions can be recognized by several approaches such as gesture, facial images, physiological signals and neuro imaging methods. Most of the researchers have developed user dependent emotion recognition system and achieved maximum classification rate. Very few researchers have tried to develop a user independent system and obtained lower classification rate. Efficient emotion stimulus method, larger data samples and intelligent signal processing techniques are essential for improving the classification rate of the user independent system. In this paper, we present a review on emotion recognition using physiological signals. The various theories on emotion, emotion recognition methodology and the current advancements in emotion research are discussed in subsequent topics. This would provide an insight on the current state of research and its challenges on emotion recognition using physiological signals, so that research can be advanced to obtain better recognition.

267 citations

Proceedings Article
06 Apr 2008
TL;DR: Results confirm the possibility of using wavelet transform based feature extraction for assessing the human emotions from EEG signal, and of selecting a minimal number of channels for emotion recognition experiment.
Abstract: The Electroencephalogram (EEG) is one of the useful biosignals detect the human emotions. This paper discusses on a research conducted to determine the changes in the electrical activity of the human brain related to distinct emotions. We designed a competent acquisition protocol for acquiring the EEG signals under audio-visual induction environment. The EEG data has been collected from 6 healthy subjects with in an age group of 21-27 using 63 biosensors. From the subjective analysis on each emotion, three emotions have been identified with higher agreement. After preprocessing the signals, discrete wavelet transform is employed to extract the EEG parameters. The feature vectors derived from the above feature extraction method on 63 biosensors form an input matrix for emotion classification. In this work, we have used Fuzzy C-Means (FCM) and Fuzzy k-Means (FKM) clustering methods for classifying the emotions. We have also analyzed the performance of FCM and FKM on reduced number of 24 biosensors model. Finally, we compared the performance of clustering the discrete emotions using FCM and FKM on both 64 biosensors and 24 biosensors. Results confirm the possibility of using wavelet transform based feature extraction for assessing the human emotions from EEG signal, and of selecting a minimal number of channels for emotion recognition experiment.

135 citations


Cited by
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Journal ArticleDOI
01 Jun 1959

3,442 citations

Journal ArticleDOI
TL;DR: This work reviews feature extraction methods for emotion recognition from EEG based on 33 studies, and results suggest preference to locations over parietal and centro-parietal lobes.
Abstract: Emotion recognition from EEG signals allows the direct assessment of the “inner” state of a user, which is considered an important factor in human-machine-interaction. Many methods for feature extraction have been studied and the selection of both appropriate features and electrode locations is usually based on neuro-scientific findings. Their suitability for emotion recognition, however, has been tested using a small amount of distinct feature sets and on different, usually small data sets. A major limitation is that no systematic comparison of features exists. Therefore, we review feature extraction methods for emotion recognition from EEG based on 33 studies. An experiment is conducted comparing these features using machine learning techniques for feature selection on a self recorded data set. Results are presented with respect to performance of different feature selection methods, usage of selected feature types, and selection of electrode locations. Features selected by multivariate methods slightly outperform univariate methods. Advanced feature extraction techniques are found to have advantages over commonly used spectral power bands. Results also suggest preference to locations over parietal and centro-parietal lobes.

743 citations

Book ChapterDOI
01 Jan 2010

691 citations

Journal ArticleDOI
TL;DR: A survey of the neurophysiological research performed from 2009 to 2016 is presented, providing a comprehensive overview of the existing works in emotion recognition using EEG signals, and a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results.
Abstract: Emotions have an important role in daily life, not only in human interaction, but also in decision-making processes, and in the perception of the world around us. Due to the recent interest shown by the research community in establishing emotional interactions between humans and computers, the identification of the emotional state of the former became a need. This can be achieved through multiple measures, such as subjective self-reports, autonomic and neurophysiological measurements. In the last years, Electroencephalography (EEG) received considerable attention from researchers, since it can provide a simple, cheap, portable, and ease-to-use solution for identifying emotions. In this paper, we present a survey of the neurophysiological research performed from 2009 to 2016, providing a comprehensive overview of the existing works in emotion recognition using EEG signals. We focus our analysis in the main aspects involved in the recognition process (e.g., subjects, features extracted, classifiers), and compare the works per them. From this analysis, we propose a set of good practice recommendations that researchers must follow to achieve reproducible, replicable, well-validated and high-quality results. We intend this survey to be useful for the research community working on emotion recognition through EEG signals, and in particular for those entering this field of research, since it offers a structured starting point.

640 citations

Journal ArticleDOI
TL;DR: The experimental results show that the designed networks achieve excellent performance on the task of recognizing speech emotion, especially the 2D CNN LSTM network outperforms the traditional approaches, Deep Belief Network (DBN) and CNN on the selected databases.

599 citations