Alias Mohd Noor

Electroencephalographic motor imagery brain connectivity analysis for bci: A review

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 for a completely locked-in subject. To provide more useful and informative features, it has been recommended to take into account the relationships among electroencephalographic (EEG) sensor/source signals in the form of brain connectivity as an efficient tool of neuroscience. In this review, we briefly report the challenges and limitations of conventional MI-BCIs. Brain connectivity analysis, particularly functional and effective, has been described as one of the most promising approaches for improving MI-BCI performance. An extensive literature on EEG-based MI brain connectivity analysis of healthy subjects is reviewed. We subsequently discuss the brain connectomes during left and right hand, feet, and tongue MI movements. Moreover, key components involved in brain connectivity analysis that considerably affect the results are explained. Finally, possible technical shortcomings that may have influenced the results in previous research are addressed and suggestions are provided.

EMG-based facial gesture recognition through versatile elliptic basis function neural network

BackgroundRecently, the recognition of different facial gestures using facial neuromuscular activities has been proposed for human machine interfacing applications. Facial electromyograms (EMGs) analysis is a complicated field in biomedical signal processing where accuracy and low computational cost are significant concerns. In this paper, a very fast versatile elliptic basis function neural network (VEBFNN) was proposed to classify different facial gestures. The effectiveness of different facial EMG time-domain features was also explored to introduce the most discriminating.MethodsIn this study, EMGs of ten facial gestures were recorded from ten subjects using three pairs of surface electrodes in a bi-polar configuration. The signals were filtered and segmented into distinct portions prior to feature extraction. Ten different time-domain features, namely, Integrated EMG, Mean Absolute Value, Mean Absolute Value Slope, Maximum Peak Value, Root Mean Square, Simple Square Integral, Variance, Mean Value, Wave Length, and Sign Slope Changes were extracted from the EMGs. The statistical relationships between these features were investigated by Mutual Information measure. Then, the feature combinations including two to ten single features were formed based on the feature rankings appointed by Minimum-Redundancy-Maximum-Relevance (MRMR) and Recognition Accuracy (RA) criteria. In the last step, VEBFNN was employed to classify the facial gestures. The effectiveness of single features as well as the feature sets on the system performance was examined by considering the two major metrics, recognition accuracy and training time. Finally, the proposed classifier was assessed and compared with conventional methods support vector machines and multilayer perceptron neural network.ResultsThe average classification results showed that the best performance for recognizing facial gestures among all single/multi-features was achieved by Maximum Peak Value with 87.1% accuracy. Moreover, the results proved a very fast procedure since the training time during classification via VEBFNN was 0.105 seconds. It was also indicated that MRMR was not a proper criterion to be used for making more effective feature sets in comparison with RA.ConclusionsThis work was accomplished by introducing the most discriminating facial EMG time-domain feature for the recognition of different facial gestures; and suggesting VEBFNN as a promising method in EMG-based facial gesture classification to be used for designing interfaces in human machine interaction systems.

Estimating Effective Connectivity from fMRI Data Using Factor-based Subspace Autoregressive Models

We consider the problem of identifying large-scale effective connectivity of brain networks from fMRI data. Standard vector autoregressive (VAR) models fail to estimate reliably networks with large number of nodes. We propose a new method based on factor modeling for reliable and efficient high-dimensional VAR analysis of large networks. We develop a subspace VAR (SVAR) model from a factor model (FM), where observations are driven by a lower-dimensional subspace of common latent factors with an AR dynamics. We consider two variants of principal components (PC) methods that provide consistent estimates for the FM hence the implied SVAR model, even of large dimensions. Information criterion is used to select the optimal subspace dimension. We established asymptotic normality and convergence rates for the estimated SVAR coefficients matrix. Evaluation on simulated resting-state fMRI shows that the SVAR models are more robust and produce better connectivity estimates than the classical model for a moderately-large network analysis. Results on real data by varying the subspace dimensions identify strong connections in the default mode network and reveal hierarchical connectivity of resting-state networks with distinct functional relevance.

Comparison of Multilayer Perceptron and Radial Basis Function Neural Networks for EMG-Based Facial Gesture Recognition

This paper compared the application of multilayer perceptron (MLP) and radial basis function (RBF) neural networks on a facial gesture recognition system. Electromyogram (EMG) signals generated by ten different facial gestures were recorded through three pairs of electrodes. EMGs were filtered and segmented into non-overlapped portions. The time-domain feature mean absolute value (MAV) and its two modified derivatives MMAV1 and MMAV2 were extracted. MLP and RBF were used to classify the EMG features while six types of activation functions were evaluated for MLP architecture. The discriminating power of single/multi features was also investigated. The results of this study showed that symmetric saturating linear was the most effective activation function for MLP; the feature set MAV + MMAV1 provided the highest accuracy by both classifiers; MLP reached higher recognition ratio for most of features; RBF was the faster algorithm which also offered a reliable trade-off between the two key metrics, accuracy and time.

Morphological Study of the Newly Designed Cementless Femoral Stem

A morphology study was essential to the development of the cementless femoral stem because accurate dimensions for both the periosteal and endosteal canal ensure primary fixation stability for the stem, bone interface, and prevent stress shielding at the calcar region. This paper focused on a three-dimensional femoral model for Asian patients that applied preoperative planning and femoral stem design. We measured various femoral parameters such as the femoral head offset, collodiaphyseal angle, bowing angle, anteversion, and medullary canal diameters from the osteotomy level to 150 mm below the osteotomy level to determine the position of the isthmus. Other indices and ratios for the endosteal canal, metaphyseal, and flares were computed and examined. The results showed that Asian femurs are smaller than Western femurs, except in the metaphyseal region. The canal flare index (CFI) was poorly correlated (r < 0.50) to the metaphyseal canal flare index (MCFI), but correlated well (r = 0.66) with the corticomedullary index (CMI). The diversity of the femoral size, particularly in the metaphyseal region, allows for proper femoral stem design for Asian patients, improves osseointegration, and prolongs the life of the implant.

Fabrication of Low-Cost, Cementless Femoral Stem 316L Stainless Steel Using Investment Casting Technique

Total hip arthroplasty is a flourishing orthopedic surgery, generating billions of dollars of revenue. The cost associated with the fabrication of implants has been increasing year by year, and this phenomenon has burdened the patient with extra charges. Consequently, this study will focus on designing an accurate implant via implementing the reverse engineering of three-dimensional morphological study based on a particular population. By using finite element analysis, this study will assist to predict the outcome and could become a useful tool for preclinical testing of newly designed implants. A prototype is then fabricated using 316L stainless steel by applying investment casting techniques that reduce manufacturing cost without jeopardizing implant quality. The finite element analysis showed that the maximum von Mises stress was 66.88 MPa proximally with a safety factor of 2.39 against endosteal fracture, and micromotion was 4.73 μm, which promotes osseointegration. This method offers a fabrication process of cementless femoral stems with lower cost, subsequently helping patients, particularly those from nondeveloped countries.

Current Issues and Problems in the Joining of Ceramic to Metal

Ceramics and metals are two of the oldest established classes of technologically useful materials. While metals dominate engineering applications, ceramics have some attractive properties compared to metals, which make them useful for specific applications. The properties of individual ceramics and metals can vary widely; however, the characteristics of most materials in the two classes differ significantly. Joints between a metal and ceramic are becoming increasingly important in the manufacturing of a wide variety of technological product. But joining ceramics to metallic materials often remains an unresolved or unsatisfactorily resolved problem. This chapter deals with problems of various studies in recent years on the joining between two materials.

Sensor space time-varying information flow analysis of multiclass motor imagery through Kalman Smoother and EM algorithm

Inter-channel time-varying (TV) relationships of scalp neural recordings offer deep understanding of the brain sensory and cognitive functions. This paper develops a state space-based TV multivariate autoregressive (MVAR) model for estimating TV-information flow (IF) recruited by different motor imagery (MI) movements. TV model coefficients are computed through Kalman filter (KF) by incorporating Kalman smoothing approach and expectation-maximization algorithm for model parameter estimation, KS-EM. Volume conduction (VC) problem is also addressed by considering full noise covariate in observation equation. An automated model initialization is also implemented to deliver optimal estimates. TV-partial directed coherence derived from the proposed model is applied for IF analysis. The performance of KS-EM is assessed and compared with dual extended KF and overlapping sliding window-based MVAR models using simulated data. Finally, TV-IF during four different MI movements is studied. Results show the superiority of KS-EM for tracking the rapid signal parameter changes and eliminating the VC effect in the sensor space EEG. Differences in contralateral/ipsilateral TV-IF around alpha and lower beta bands during each MI task reveal the high potential of this feature for BCI applications.

Measuring Aerodynamic Characteristics Using High Performance Low Speed Wind Tunnel at Universiti Teknologi Malaysia

This paper describes the capability and activities on utilizing a low speed wind tunnel facility at Universiti Teknologi Malaysia (UTM -LST) since its first operation in year 2001 till 2012. The laboratory is setup to meet the educational, research and industrial needs of Malaysia’s developing aero industry. The wind tunnel has high flow quality and can deliver speed up to 288 km/hr. UTM-LST has experiences on a wide range of testing such as aircraft, automotive, civil structure and building, ship and offshore structure. The wind tunnel is primarily equipped with flow visualization facility including Particle Image Velocimetry (PIV), pressure measurement, force measurements and Constant Temperature Anemometer (CTA). Most of the primary aerodynamic parameters can be measured in this facility such as measurement of aerodynamic lift and drag, static stability and control derivatives of aircraft. Measurement of automotive drag, down force and crosswind stability, and measurement of wind loads on civil structures. Correlation between wind tunnel measurements and numerical simulation using Computational Fluid Dynamics (CFD) are becoming more demanding especially related to unsteady aerodynamics. Currently, research related to unsteady aerodynamics such as helicopter rotor wakes, automotive wake turbulence and oscillating aerofoil are more demanding and requires upgrading to the current facility.

Identifying dynamic effective connectivity states in fMRI based on time-varying vector autoregressive models

We propose a framework to estimate the transition of effective connectivity states in functional magnetic resonance imaging (fMRI), with the changing experimental conditions. The fMRI effective connectivity is traditionally assumed to be stationary across the entire scanning time-course. However, recent evidence shows that it exhibits dynamic changes over time. In this study, we employ a non-stationary model based on time-varying autoregression (TV-VAR) to capture the dynamic effective connectivity, and K-means clustering to identify the change-points of the connectivity states. The TV-VAR parameters are estimated sequentially in time using the Kalman filtering and the expectation-maximization (EM) algorithm. The extracted directed connectivities between brain regions are then used as features to the K-means algorithm to be partitioned into a finite number of states and to produce the state change-points, assuming the task condition boundaries are unknown. Experimental results on motor-task fMRI data show the ability of the proposed method in estimating the state-related changes in the motor regions during the resting-state and active conditions, with low squared estimation errors. The estimated brain-state connectivity also reveals different patterns between the healthy subjects and the stroke patients.