M. Murugappan, Dr.
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This page provides access to research works by Dr. M. Murugappan, currently a Senior Lecturer of School of Mechatronic Engineering, Universiti Malaysia Perlis (UniMAP).
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Skills and Expertise: Brain Computer Interface, Neuro-Fuzzy, LabVIEW programming, Cognition Disorders, Nonverbal Communication, Cognitive Science, Neuromarketing, Machine Learning, Behavioural Science, Pattern Recognition, Digital Signal Processing, Biomedical Signal Processing, Time-Frequency Analysis, BCI, Wavelet, Signal Analysis, Brain Signal Processing, Wavelet Analysis, Image Processing, Computer Vision, Biosignal Processing, Medical and Biomedical Image Processing
Recent Submissions
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Inter-hemispheric EEG coherence analysis in Parkinson's disease: Assessing brain activity during emotion processing
(Springer-Verlag Wien, 2014-06)Parkinson's disease (PD) is not only characterized by its prominent motor symptoms but also associated with disturbances in cognitive and emotional functioning. The objective of the present study was to investigate the ... -
Machine learning approach for sudden cardiac arrest prediction based on optimal heart rate variability features
(American Scientific Publishers, 2014-08)Sudden Cardiac Arrest (SCA) is a devastating heart abnormality which leads to millions of casualty per year. Thus, early detection or prediction of SCA could save the human lives in greater scale. This present work is aimed ... -
A software prototype based emotional impairments detection in neurological disorders patients using wireless EEG signals
(Universiti Malaysia Perlis (UniMAP)School of Mechatronic Engineering, 2014-05)Social communication and the ability to respond emotional signals are essential for meaningful Interpersonal Iinteractions -
PNN based driver drowsiness level classification using EEG
(JATIT & LLS. All rights reserved, 2013-06)In this work, we classify the driver drowsiness level (awake, drowsy, high drowsy and sleep stage1) based on different wavelets and probabilistic neural network classifier using wireless EEG signals. Deriving the amplitude ... -
Classification of driver drowsiness level using wireless EEG
(Przegląd Elektrotechniczny, 2013)In this work, wireless Electroencephalogram (EEG) signals are used to classify the driver drowsiness levels (neutral, drowsy, high drowsy and sleep stage1) based on Discrete Wavelet Packet Transform (DWPT). Two statistical ... -
Hospital nurse following robot: hardware development and sensor integration
(Inderscience Enterprises Ltd., 2014)Hospital nurse regularly bring her instrument to the patient using cart. They need to push or pull the cart to the patient bed and bring it back many times in a day. This can be tiresome for nurses because they need to ... -
Frequency band analysis of electrocardiogram (ECG) signals for human emotional state classification using discrete wavelet transform (DWT)
(Society of Physical Therapy Science, 2013-06)[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions ... -
Lifting scheme for human emotion recognition using EEG
(IEEE Conference Publications, 2008-08)In recent years, the need and importance of automatically recognizing emotions from EEG signals has grown with increasing role of brain computer interface applications. The detection of fine grained changes in functional ... -
Electromyogram signal based human emotion classification using KNN and LDA
(IEEE Conference Publications, 2011-06)In this paper, we presents Electromyogram (EMG) signal based human emotion classification using K Nearest Neighbor (KNN) and Linear Discriminant Analysis (LDA). Five most dominating emotions such as: happy, disgust, fear, ... -
ECG signals based mental stress assessment using wavelet transform
(IEEE Conference Publications, 2011-11)This paper describes the mental stress assessment using Electrocardiography (ECG) signal. Stress reflects the changes in heart rates under stressful situation. In this work, Heart Rate Variability (HRV) from ECG signal is ... -
A study on mental arithmetic task based human stress level classification using discrete wavelet transform
(IEEE Conference Publications, 2012-10)Several studies examined human stress identification using Mental Arithmetic Task (MAT). The identification and prediction of stress levels using existing data processing methodologies are incompetent to predict the stress ... -
Emotion recognition from electrocardiogram signals using Hilbert Huang Transform
(IEEE Conference Publications, 2012-10)Equipping robots and computers with emotional intelligence is becoming important in Human-Computer Interaction (HCI). Bio-signal based methods are found to be reliable and accurate than conventional methods as they directly ... -
Subtractive fuzzy classifier based driver drowsiness levels classification using EEG
(IEEE Conference Publications, 2013-04)Driver drowsiness is one of the major causes for several road accidents over the world. In this study, Electroencephalogram (EEG) signals were acquired using 14 electrodes from 50 subjects. All the electrodes are placed ... -
Emotion detection from QRS complex of ECG signals using hurst exponent for different age groups
(IEEE Conference Publications, 2013-09)Emotion recognition using physiological signals is one of the key research areas in Human Computer Interaction (HCI). In this work, we identify the six basic emotional states (Happiness, sadness, fear, surprise, disgust ... -
Classification of emotional States from electrocardiogram signals: a non-linear approach based on hurst
(BioMed Central, 2013-05)Background: Identifying the emotional state is helpful in applications involving patients with autism and other intellectual disabilities; computer-based training, human computer interaction etc. Electrocardiogram (ECG) ... -
Subtractive fuzzy classifier based driver distraction levels classification using EEG
(Society of Physical Therapy Science, 2013)[Purpose] In earlier studies of driver distraction, researchers classified distraction into two levels (not distracted, and distracted). This study classified four levels of distraction (neutral, low, medium, high). [Subjects ... -
Development of EEG data acquisition device by using single board computer
(Inderscience Enterprises Ltd., 2013)Electroencephalogram (EEG) plays a vital role in several medical diagnosis (brain tumour detection, Alzheimer disease, epilepsy, etc.), engineering applications (emotion detection, drowsiness detection, stress assessment, ... -
Descriptive analysis of skin temperature variability of sympathetic nervous system activity in stress
(Society of Physical Therapy Science, 2012-12)[Purpose] Stress is a common factor of several diseases. Stress can be reduced through appropriate stress management and relaxation methods. In this study, variation in skin temperature (ST) was investigated as a primary ... -
Human emotional stress assessment through Heart Rate Detection in a customized protocol experiment
(IEEE Conference Publications, 2012-09)Continuous existence of negative emotions (disgust, anger, fear and sad) over a longer period of time induces emotional stress. This emotional stress can be analyzed through physiological signal characteristics such as ... -
Frequency band analysis of electrocardiogram (ECG) signals for human emotional state classification using discrete wavelet transform (DWT)
(Society of Physical Therapy Science, 2013-06)[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions ...
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