Browsing by Author "hema@unimap.edu.my"

Now showing items 1-17 of 17

    • Asynchronous brain machine interface-based control of a wheelchair 

      Hema, Chengalvarayan Radhakrishnamurthy; Murugesan Pandiyan, Paulraj, Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Abdul Hamid, Adom, Dr.; Ramachandran, Nagarajan, Prof. Dr. (Springerlink, 2011)
      A brain machine interface (BMI) design for controlling the navigation of a power wheelchair is proposed. Real-time experiments with four able bodied subjects are carried out using the BMI-controlled wheelchair. The BMI is ...

    • Bimodal Rehabilitation for the Voice and Movement Impaired using Brain Signals 

      Hema, C. R.; Paulraj, M. P., Assoc. Prof. Dr.; Sazali, Yaacob, Prof.; Abdul Hamid, Adom, Assoc. Prof. Dr.; Nagarajan, Prof.; Leong, Shi Wei; Erdy Sulino, Mohd Muslim Tan; Ahmad Zulfadli, Musa; Farid, Affendi (Universiti Malaysia Perlis, 2009-01-07)
      The Bimodal Rehabilitation for the Voice and Movement Impaired using Brain Signals is a device which can help the paralyzed patients to communicate through a digital voice and also to drive a robot chair within their homes ...

    • A biometric Authentication System Using Brain Signatures for Individuals 

      Adzizul Adzlan, Osman (Universiti Malaysia Perlis (UniMAP)Jabatan Hal Ehwal Pelajar & Alumni (HEPA), 2010-04-02)
      Biometric identification is simply, the technique of verifying a person by a physical charateristic (physiological) or personal trait (behavior). Biometric offer automated methods of identity verification or identification ...

    • 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: Analysis of segmented EEG signal classification using short-time PCA and recurrent neural networks 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesa Pandiyan, Assoc. Prof.; Nagarajan, Ramachandran, Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Abdul Hamid, Adom, Prof. Madya (University of Basrah, 2008)
      Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients ...

    • Brain machine interface: classification of mental tasks using short-time PCA and recurrent neural networks 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abd Hamid, Adom; Ramachandran, Nagarajan (Institute of Electrical and Electronics Engineering (IEEE), 2007-11-25)
      Brain machine interface provides a communication channel between the human brain and an external device. Brain interfaces are studied to provide rehabilitation to patients with neurodegenerative diseases; such patients ...

    • Brain Power Word Communcation System 

      Leong, Shi Wei; Erdy Sulino, Mohd Muslim Tan (Universiti Malaysia PerlisJabatan Hal Ehwal Pelajar & Alumni, 2010-04-02)
      The system can be custom designed for each individual based on their environment. The system can run on any PC platform with simple user graphical interface. The system can improve the quality of life of the patient. The ...

    • Brain signatures: a modality for biometric authentication 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Kaur, Harkirenjit (Institute of Electrical and Electronics Engineering (IEEE), 2008-12-01)
      In this paper we investigate the use of brain signatures as a possible biometric authentication technique. Research on brain EEG signals has shown that individuals exhibit unique brain patterns for similar tasks. In this ...

    • Designing a hybrid sensor system for a housekeeping robot 

      Hema, Chengalvarayan Radhakrishnamurthy; Sim, Kwoh Fung; Poo, Tarn Shi (Institute of Electrical and Electronics Engineering (IEEE), 2009-03-06)
      Housekeeping robots are service robots specially designed to perform housekeeping tasks such as cleaning and vacuuming, our research focuses on the design of a housekeeping robot to pick up waste objects in a home or office ...

    • 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 ...

    • Housekeeping robot: From concept to design 

      Hema, Chengalvarayan Radhakrishnamurthy; Lam, Chee Kiang; Lot, V.T.S. (Institute of Electrical and Electronics Engineering (IEEE), 2009-03-06)
      Service robots are emerging from the laboratory as commercial products. Floor cleaning, material transporting in radioactive and other hostile environments and security robots are some of the facets of a service robot. ...

    • Intelligent Sensor for Reversing Vehicles 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesa Pandiyan, Assoc. Prof.; Nagarajan, Ramachandran, Prof.; Sazali, Yaacob, Prof. (Kementerian Pengajian Tinggi Malaysia (KPTM), 2007-08-10)
      The Intelligent Sensor for Reversing Vehicles comprises of a stereo camera (placed on the rear bumper of the car), a processing unit, a display unit and speaker (placed near the dashboard of the car). The cameras are ...

    • 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 ...

    • Recognition of motor imagery of hand movements for a BMI using PCA features 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Sazali, Yaacob; Abd Hamid, Adom; Ramachandran, Nagarajan (Institute of Electrical and Electronics Engineering (IEEE), 2008-12-01)
      Motor imagery is the mental simulation of a motor act that includes preparation for movement and mental operations of motor representations implicitly or explicitly. The ability of an individual to control his EEG through ...

    • Stereo Sensors-based Object Segmentation and Location for a Bin Picking Adept SCARA Robot 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesapandian; Ramachandran, Nagarajan; Sazali, Yaacob (Universiti Malaysia Perlis, 2007)
      In this paper we present a stereo vision system for segmentation of partially occluded objects and computation of object grasping point in bin picking environments. The stereo vision system was interfaced with an Adept ...

    • Synchronous brain machine interface design using focused time delay networks 

      Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Abdul Hamid, Adom, Assoc. Prof. Dr.; Ramachandran, Nagarajan, Prof. Dr. (Universiti Malaysia Perlis (UniMAP)School of Mechatronic Engineering, 2012-02-27)
      Focused time delay neural network based design for a four-state Brain Machine Interface (BMI) to drive a wheelchair is analyzed. Motor imagery signals recorded noninvasively using two bipolar electrodes are used in the ...