Search Results - classifier using interface

Search alternatives:

Refine Results
  1. 1
    Brain machine interface: A comparison between fuzzy and neural classifiers

    Brain machine interface: A comparison between fuzzy and neural classifiers by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Ramachandran, Nagarajan, Sazali, Yaacob, Abdul Hamid, Adom

    Published 2009
    Get full text
    Article
    Save to List
    Saved in:
  2. 2
    Improving classification of EEG signals for a four-state brain machine interface

    Improving classification of EEG signals for a four-state brain machine interface by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr., Abdul Hamid, Adom, Prof. Dr.

    Published 2013
    Get full text
    Working Paper
    Save to List
    Saved in:
  3. 3
    Neuro-Fuzzy based motor imagery classification for a four class brain machine interface

    Neuro-Fuzzy based motor imagery classification for a four class brain machine interface by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesapandian, Sazali, Yaacob, Abdul Hamid, Adom, Ramachandran, Nagarajan

    Published 2009
    Get full text
    Working Paper
    Save to List
    Saved in:
  4. 4
    Comparison of static and dynamic neural network classifiers for brain-machine interfaces / Hema C.R. ...[et al.]

    Comparison of static and dynamic neural network classifiers for brain-machine interfaces / Hema C.R. ...[et al.] by C.R., Hema, M.P., Paulraj, Yaacob, S., Adom, A.H., Nagarajan, R.

    Published 2010
    “…Neural network classifiers are one among the popular modes in the design of brain machine interface (BMI). …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  5. 5
    Classification Of P300 Signals In Brain-Computer Interface Using Neural Networks With Adjustable Activation Functions

    Classification Of P300 Signals In Brain-Computer Interface Using Neural Networks With Adjustable Activation Functions by Aslarzanagh, Seyed Aliakbar Mousavi

    Published 2013
    “…P300 speller application is a BCI that finds the location of target character using P300 signals. This application tries to classify brain‘s P300 signals to find the correct character from character board. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  6. 6
    Brain Machine Interface Controlled Robot Chair

    Brain Machine Interface Controlled Robot Chair by Hema Chengalvarayan, Radhakrishnamurthy

    Published 2010
    “…In this thesis, a novel four-class brain machine interface (BMI) is designed for a robot chair using neural networks. …”
    Get full text
    Thesis
    Save to List
    Saved in:
  7. 7
    Pushing the boundaries of EEG-based emotion classification using consumer-grade wearable brain-computer interfacing devices and ensemble classifiers

    Pushing the boundaries of EEG-based emotion classification using consumer-grade wearable brain-computer interfacing devices and ensemble classifiers by Jason Teo, Nazmi Sofian Suhaimi, James Mountstephens

    Published 2020
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  8. 8
    Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan

    Early detection of anxiety in social media using Convolution neural network / Mohd Tharwan Hadi Ruslan by Ruslan, Mohd Tharwan Hadi

    Published 2021
    “…Then there will be a lot of preprocess step to clean the data. As for the classifier design, the keras function will be used to generate CNN classifier. …”
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  9. 9
    Real time face detection system

    Real time face detection system by Amy Safrina, Mohd Ali

    Published 2009
    “…This system also used Graphical User Interface (GUI) to design client window. …”
    Get full text
    Get full text
    Undergraduates Project Papers
    Save to List
    Saved in:
  10. 10
    Analysis of mental imagery based cognitive tasks for brain computer interface

    Analysis of mental imagery based cognitive tasks for brain computer interface by Tang, Chee Hoe

    Published 2019
    Get full text
    Get full text
    Final Year Project / Dissertation / Thesis
    Save to List
    Saved in:
  11. 11
    Motor imaginary signal classification using adaptive recursive bandpass filter and adaptive autoregressive models for brain machine interface designs

    Motor imaginary signal classification using adaptive recursive bandpass filter and adaptive autoregressive models for brain machine interface designs by Jeyabalan, V., Samraj, A., Kiong, L.C.

    Published 2008
    “…The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  12. 12
    Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface

    Comparing Common Average Referencing to Laplacian Referencing in Detecting Imagination and Intention of Movement for Brain Computer Interface by Badlishah, Ahmad, Syam, S.H.F, Lakany, H, Ahmad, R.B, Conway, B.A

    Published 2017
    “…Features were extracted from the filtered signal using ERSP and power spectrum and classified by k-nearest neighbour (k-NN) and quadratic discriminant analysis (QDA) classifiers. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  13. 13
    Mental Task Motor Imagery Classifications for Noninvasive Brain Computer Interface

    Mental Task Motor Imagery Classifications for Noninvasive Brain Computer Interface by Mohamed, Eltaf Abdalsalam, Yusoff, Mohd Zuki, Kamel, Nidal, Malik, Aamir Saeed, Meselhy, Mohamed

    Published 2014
    “…The Multilayer perception (MLP), Simple logistic and Bagging were utilized to classify the mental tasks motor imagery. The performance of classifications was tested and satisfactory results were obtained with the accuracy rate 80.4% using the Simple logistic classifier. …”
    Get full text
    Get full text
    Conference or Workshop Item
    Save to List
    Saved in:
  14. 14
    Synchronous brain machine interface design using focused time delay networks

    Synchronous brain machine interface design using focused time delay networks by Hema, Chengalvarayan Radhakrishnamurthy, Paulraj, Murugesa Pandiyan, Assoc. Prof. Dr., Sazali, Yaacob, Prof. Dr., Abdul Hamid, Adom, Assoc. Prof. Dr., Ramachandran, Nagarajan, Prof. Dr.

    Published 2012
    Get full text
    Working Paper
    Save to List
    Saved in:
  15. 15
    Affective computation on EEG correlates of emotion from musical and vocal stimuli

    Affective computation on EEG correlates of emotion from musical and vocal stimuli by Khosrowabadi, Reza, Abdul Rahman, Abdul Wahab, Ang, Kai Keng, H Baniasad, Mohammad.

    Published 2009
    “…A classification algorithm is subsequently used to learn and classify the extracted EEG features. …”
    Get full text
    Get full text
    Get full text
    Proceeding Paper
    Save to List
    Saved in:
  16. 16
    Designing Mobile Interface for Elderly

    Designing Mobile Interface for Elderly by Nur Rahmah, Zulkifli

    Published 2010
    “…The problems faced by them can be coped through the study in the mobile phone interface design in order to define the suitable interface for the use of elderly. …”
    Get full text
    Get full text
    Get full text
    Get full text
    Thesis
    Save to List
    Saved in:
  17. 17
    Motor imaginary-based brain machine interface design using programmable logic controllers for the disabled

    Motor imaginary-based brain machine interface design using programmable logic controllers for the disabled by Jeyabalan, V., Samraj, A., Loo, C.K.

    Published 2010
    “…The EEG signals were pre-processed by means of adaptive recursive band-pass filtrations (ARBF) and classified using simplified fuzzy adaptive resonance theory mapping (ARTMAP) in which the classified signals are then translated into control signals used for machine control via the PLC. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  18. 18
    Motor Imagery Classification for Brain Computer Interface Using Deep Metric Learning

    Motor Imagery Classification for Brain Computer Interface Using Deep Metric Learning by Alwasiti, H., Yusoff, M.Z., Raza, K.

    Published 2020
    “…However, this type of deep learning models has not been tested on the classification of electrical brain waves (EEG) for brain computer interface (BCI) applications. For the first time, we propose a triplet network to classify motor imagery (MI) EEG signals. …”
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  19. 19
    Interfacing Google Search Engine to Capture User Web Search Behavior

    Interfacing Google Search Engine to Capture User Web Search Behavior by Mat Yamin, Fadhilah, Ramayah, T.

    Published 2013
    “…Due to the difficulty of obtaining this search log, this paper proposed and develops an interface framework to interface a Google search engine. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:
  20. 20
    Four state brain machine interface design using functional link networks / Dr. Hema C.R. and Dr. Paulraj M.P.

    Four state brain machine interface design using functional link networks / Dr. Hema C.R. and Dr. Paulraj M.P. by C.R., Hema (Dr.), M.P., Paulraj (Dr.)

    Published 2012
    “…Electroencephalogram [EEG] signals acquired during motor imagery for left and right hand movements are used to classify the four controls. The BMI is designed using a Functional Link Neural Classifier [FLNN]. …”
    Get full text
    Get full text
    Get full text
    Article
    Save to List
    Saved in:

Search Tools: