Browsing M. Murugappan, Dr. by Title

Now showing items 54-67 of 67

    • On the analysis of EEG power, frequency and asymmetry in Parkinson's disease during emotion processing 

      Yuvaraj, Rajamanickam; Murugappan, M., Dr.; Norlinah, Mohamed Ibrahim; Mohd Iqbal, Omar@Ye Htut, Assoc. Prof. Dr.; Sundaraj, Kenneth, Prof. Dr.; Khairiyah, Mohamad; Palaniappan, Ramaswamy; Mesquita, Edgar; Satiyan, Marimuthu (BioMed Central Ltd., 2014-03)
      Objective: While Parkinson's disease (PD) has traditionally been described as a movement disorder, there is growing evidence of disruption in emotion information processing associated with the disease. The aim of this study ...

    • A physiological signal based approach on stress assessment through emotion, physical, and cognitive stimuli: A theoretical frame work 

      Murugappan, M., Dr.; Karthikeyan, P.; Bong, Siao Zheng; Ku Nor Syamimi, Ku Ismail; Sazali, Yaacob, Prof. Dr. (Universiti Malaysia Perlis (UniMAP)School of Mechatronic Engineering, 2012-02-27)
      Stress is one of an unsteady state of human like uncontrolled machineries in industries. In order analysis effects of human stress, several studies have reported to identify the physiological and psychological variation ...

    • Physiological signals based human emotion recognition: A review 

      S., Jerritta; M., Murugappan; Ramachandran, Nagarajan, Prof. Dr.; Wan Khairunizam, Wan Ahmad, Dr. (Institute of Electrical and Electronics Engineers (IEEE), 2011-03-04)
      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 ...

    • PNN based driver drowsiness level classification using EEG 

      Mousa Kadhim, Wali; Murugappan, Muthusamy, Dr.; R. Badlishah, Ahmad, Prof. Dr. (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 ...

    • Review of emotion recognition in stroke patients 

      Yuvaraj, Rajamanickam; Murugappan, M; Mohamed Ibrahim, Norlinah; Sundaraj, Kenneth, Prof. Dr.; Mohamad, Khairiyah (Karger AG, Basel., 2013)
      Objective: Patients suffering from stroke have a diminished ability to recognize emotions. This paper presents a review of neuropsychological studies that investigated the basic emotion processing deficits involved in ...

    • A review on stress inducement stimuli for assessing human stress using physiological signals 

      P., Karthikeyan; Murugappan, M., Dr.; Sazali, Yaacob, Prof. Dr. (Institute of Electrical and Electronics Engineers (IEEE), 2011-03-04)
      Assessing human stress in real-time is more difficult and challenging today. The present review deals about the measurement of stress in laboratory environment using different stress inducement stimuli by the help of ...

    • A software prototype based emotional impairments detection in neurological disorders patients using wireless EEG signals 

      Murugappan, Muthusamy, Dr.; Yuvaraj, Rajamanickam; Norlinah, Mohamed Ibrahim, Dr.; Kenneth, Sundaraj, Dr.; Mohd Iqbal, Omar, Dr.; Khairiyah, Mohamad (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

    • A study on mental arithmetic task based human stress level classification using discrete wavelet transform 

      Karthikeyan, Palanisamy; Murugappan, M., Dr.; Sazali, Yaacob, Prof. Dr. (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 ...

    • Subtractive fuzzy classifier based driver distraction levels classification using EEG 

      Wali, Mousa Kadhim; Murugappan, M., Dr.; R. Badlishah, Ahmad, Prof. Dr. (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 ...

    • Subtractive fuzzy classifier based driver drowsiness levels classification using EEG 

      Murugappan, M., Dr.; Wali, Mousa Kadhim; R. Badlishah, Ahmad, Prof. Dr.; Murugappan, Subbulakshmi (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 ...

    • Sudden cardiac death prediction using ECG signal derivative (heart rate variability): a review 

      Murukesan, Loganathan; Murugappan, M.; Mohammad Iqbal, Omar@Ye Htut, Assoc. Prof. Dr. (IEEE Conference Publications, 2013)
      Sudden cardiac death (SCD) prediction using Electrocardiogram (ECG) signal is a popular area of research because of the seriousness of the matter. There are tons of papers published in this research which available online. ...

    • Time-frequency analysis of EEG signals for human emotion detection 

      Murugappan, Muthusamy, Dr.; M.Rizon; R., Nagarajan; Sazali, Yaacob, Prof. Dr.; Hazry, Desa, Assoc. Prof. Dr.; Zunaidi, Ibrahim (Springer Berlin Heidelberg, 2008)
      This paper proposes an emotion recognition system from EEG (Electroencephalogram) signals. The main objective of this work is to compare the efficacy of classifying human emotions using two discrete wavelet transform (DWT) ...

    • Wavelet packet transform based driver distraction level classification using EEG 

      Wali, Mousa Kadhim; Murugappan, M., Dr.; R. Badlishah, Ahmad, Prof. Dr. (Mathematical Problems in Engineering, 2013)
      We classify the driver distraction level (neutral, low, medium, and high) based on different wavelets and classifiers using wireless electroencephalogram (EEG) signals. 50 subjects were used for data collection using 14 ...

    • Wireless EEG signals based neuromarketing system using Fast Fourier Transform (FFT) 

      Murugappan, M., Dr.; Murugappan, Subbulakshmi; Balaganapathy; Gerard, Celestin (IEEE Conference Publications, 2014-03)
      his work aims to identify the most preferred brand on automotive in Malaysia through wireless EEG signals. A group of four major vehicle brand advertisements such as Toyota, Audi, Proton and Suzuki is considered on this ...