Browsing Paulraj Murugesa Pandiyan, Assoc. Prof. Dr. by Author "Abdul Hamid, Adom"

Now showing items 1-10 of 10

    • Active stereo vision based system for estimation of Mobile Robot Orientation using composition matrix 

      Paulraj, Murugesapandian; Fadzilah, Hashim; R. Badlishah, Ahmad; Hema, Chengalvarayan Radhakrishnamurthy; Abdul Hamid, Adom (Universiti Malaysia Perlis, 2009-10-11)
      The computation of a mobile robot position and orientation is a common task in the area of computing vision and image processing. For a successful application, it is important that the position and orientation of a mobile ...

    • Brain machine interface based wheelchair control with minimal subject training 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abdul Hamid, Adom; Ramachandran, Nagarajan (Universiti Malaysia Perlis, 2009-10-11)
      Wheelchair control using a Brain Machine Interface based on motor imagery requires adequate subject training. In this paper we propose a new algorithm for a brain machine interface design which is implemented in real-time ...

    • Brain machine interface: A comparison between fuzzy and neural classifiers 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Ramachandran, Nagarajan; Sazali, Yaacob; Abdul Hamid, Adom (ICIC International, 2009)
      Patients with neurodegenerative disease loose all motor movements including impairment of speech, leaving the patients totally locked-in. One possible option for rehabilitation of such patients is through a brain machine ...

    • Brain machine interface: motor imagery recognition with different signal length representations 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abdul Hamid, Adom; Ramachandran, Nagarajan (Institute of Electrical and Electronics Engineering (IEEE), 2009-03-06)
      This work investigates how signal representations affect the performance of a motor imagery recognition system, specifically we investigate on recognition accuracy and computational time of a brain machine interface designed ...

    • EEG classification using radial basis PSO neural network for brain machine interfaces 

      Paulraj, Murugesapandian; Hema, Chengalvarayan Radhakrishnamurthy; Ramachandran, Nagarajan; Sazali, Yaacob; Abdul Hamid, Adom (Institute of Electrical and Electronics Engineering (IEEE), 2007-12)
      Brain Machine Interfaces use the cognitive abilities of patients with neuromuscular disorders to restore communication and motor functions. At present, only EEG and related methods, which have relatively short time constants, ...

    • EEG signal classification using Particle Swarm Optimization (PSO) neural network for brain machine interfaces 

      Paulraj, Murugesapandian; Hema, Chengalvarayan Radhakrishnamurthy; Ramachandran, Nagarajan; Sazali, Yaacob; Abdul Hamid, Adom (Association for the Advancement of Modelling & Simulation Techniques in Enterprises (A.M.S.E.), 2008)
      The brain uses the neuromuscular channels to communicate and control its external environment, however many disorders can disrupt these channels. Amyotrophic lateral sclerosis is one such disorder which impairs the neural ...

    • Functional link PSO neural network based classification of EEG mental task signals 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abdul Hamid, Adom; Nagarajan, Ramachandran (Institute of Electrical and Electronics Engineering (IEEE), 2008-08-26)
      Classification of EEG mental task signals is a technique in the design of Brain machine interface [BMI]. A BMI can provide a digital channel for communication in the absence of the biological channels and are used to ...

    • Fuzzy based classification of EEG mental tasks for a brain machine interface 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Ramachandran, Nagarajan; Sazali, Yaacob; Abdul Hamid, Adom (Institute of Electrical and Electronics Engineers (IEEE), 2007-11-28)
      Patients with neurodegenerative diseases loose all motor movements including impairment of speech, leaving the patients totally locked-in. One possible option for rehabilitation of such patients is using a brain machine ...

    • Neuro-Fuzzy based motor imagery classification for a four class brain machine interface 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abdul Hamid, Adom; Ramachandran, Nagarajan (Universiti Malaysia Perlis, 2009-10-11)
      Brain Machine Interface (BMI) provides a digital link between the brain and a device such as a computer, robot or wheelchair. This paper presents a BMI design using Neuro-Fuzzy classifiers for controlling a wheelchair using ...

    • Single trial motor imagery classification for a four state brain machine interface 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abdul Hamid, Adom; Ramachandran, Nagarajan (Institute of Electrical and Electronics Engineering (IEEE), 2009-03-06)
      Motor imagery is the mental simulation of a motor act which can be used to design brain machine interfaces [BMI]. A BMI is a digital communication system, which connects the human brain directly to an external device ...