Sheikh Hussain Shaikh Salleh

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Recent research has reached a consensus on the feasibility of motor imagery brain-computer interface (MI-BCI) for different applications, especially in stroke rehabilitation. Most MI-BCI systems rely on temporal, spectral, and spatial features of single channels to distinguish different MI patterns. However, no successful communication has been established(More)
In this paper, we present back-propagation neural network (BPNN) as back-end classifier for face verification. Face features are extracted based on principal component analysis (PCA) and linear discriminant analysis (LDA). PCA efficiently reduces dimension of face images and represent them with eigenfaces; while LDA is alternatively used to improve(More)
This paper proposes non-Gaussian models for parametric spectral estimation with application to event-related desynchronization (ERD) estimation of nonstationary EEG. Existing approaches for time-varying spectral estimation use time-varying autoregressive (TVAR) state-space models with Gaussian state noise. The parameter estimation is solved by a(More)
This paper proposes a new approach for electrocardiogram (ECG) based personal identification based on extended Kalman filtering (EKF) framework. The framework uses nonlinear ECG dynamic models formulated to represent noisy ECG signal. The advantage of the models is the ability to capture distinct ECG features used for biometric recognition such as temporal(More)
In this paper, we compare two approaches in selecting neural network learning parameters and architecture. Traditionally they are found by trial and error (handcrafted) and alternatively, it can be found using genetic algorithm. Trial and error can find good solution but the drawback for this method are time consuming and it can only try few possible(More)
This paper explains works in speech recognition using neural network. The main objective of the experiment is to choose suitable number of nodes in hidden layer and learning parameters for malay iIsolated digit speech problem through trial and error method. The network used in the experiment is feed forward multilayer perceptron trained with back(More)
Speech signal is temporally and acoustically varies. Recognition of speech by static input Neural Network requires temporal normalization of the speech to be equal to the number of input nodes of the NN while maintaining the properties of the speech. This paper compares three methods for speech temporal normalization namely the linear, extended linear and(More)
This paper describes the architecture and implementation of telemedicine via Internet for heart sounds and hearing screening diagnosis. Web based application are used as a medium for interaction between patients and doctors. Using ActiveX technology and Internet protocol, the biomedical signals are captured and sent to server. To strengthen analyses of(More)