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    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), 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 ...
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    Improving classification of EEG signals for a four-state brain machine interface 

    Hema, Chengalvarayan Radhakrishnamurthy; Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Abdul Hamid, Adom, Prof. Dr. (Institute of Electrical and Electronics Engineers (IEEE), 2012)
    Neural network classifiers are one among the popular modes in the design of classifiers for electroencephalograph based brain machine interfaces. This study presents algorithms to improve the classification performance of ...
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    Classification of EEG colour imagination tasks based BMI using energy and entropy features 

    Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr.; Abdul Hamid, Adom, Assoc. Prof. Dr.; Hema, Chengalvarayan Radhakrishnamurthy; Purushothaman, Divakar (Universiti Malaysia Perlis (UniMAP), 2012-02-27)
    Electroencephalogram (EEG) signals are the electrophysiological measures of brain function and it is used to develop a brain machine interface. Brain machine interface (BMI) system is used to provide a communication and ...
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    Brain machine interface for physically retarded people using colour visual tasks 

    Pandiyan, Paulraj Murugesa, Prof. Dr.; Abdul Hamid, Adom, Prof. Dr.; Hema, Chengalvarayan Radhakrishnamurthy; Purushothaman, D. (IEEE Conference Publications, 2010-05)
    A Brain Machine Interface is a communication system which connects the human brain activity to an external device bypassing the peripheral nervous system and muscular system. It provides a communication channel for the ...
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    BMI using spectral energy entropy for colour visual tasks 

    Divakar, Purushothaman; Paulraj, Murugesa Pandiyan, Prof. Madya Dr.; Abdul Hamid, Adom, Dr.; Hema, Chengalvarayan Radhakrishnamurthy (Universiti Malaysia Perlis (UniMAP), 2010-10-16)
    EEG signals are the electrophysiological measures of brain function and it is used to develop a Brain machine Interface. A Brain machine Interface (BMI) system is used to provide a communication and control technology ...
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    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 ...
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    Voiceless Voice : An Intelligent Communication System for Physically Retarded Communities 

    Paulraj, M. P., Prof. Dr.; Sazali, Yaacob, Prof. Dr.; Abdul Hamid, Adom, Assoc. Prof. Dr.; Hema, Chengalvarayan Radhakrishnamurthy; Sathees, Kumar; Nataraj; Divakar, Purushothaman; Erdy Sulino, Muslim Tan (Universiti Malaysia Perlis, 2010-01-07)
    The Intelligent Voiceless communication is a device which can help the physically retarded peoples to communicate with the outside world by using their brain signals. Our product makes use of the active brain functions of ...
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    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 ...
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    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 ...
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    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 ...
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    Hema, Chengalvarayan Radhakrishnamurthy (22)
    Paulraj, Murugesapandian (13)Sazali, Yaacob (12)Abdul Hamid, Adom (10)Ramachandran, Nagarajan (10)... View MoreSubjectElectroencephalography (9)Brain-computer interfaces (6)Brain machine interfaces (5)Brain machine interface (4)EEG signal processing (4)... View MoreDate Issued2012 (3)2011 (1)2010 (4)2009 (6)2008 (4)2007 (4)

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