Evaluation of different time domain peak models using extreme learning machine-based peak detection for EEG signal

Various peak models have been introduced to detect and analyze peaks in the time domain analysis of electroencephalogram (EEG) signals. In general, peak model in the time domain analysis consists of a set of signal parameters, such as amplitude, width, and slope. Models including those proposed by Dumpala, Acir, Liu, and Dingle are routinely used to detect peaks in EEG signals acquired in clinical studies of epilepsy or eye blink. The optimal peak model is the most reliable peak detection performance in a particular application. A fair measure of performance of different models requires a common and unbiased platform. In this study, we evaluate the performance of the four different peak models using the extreme learning machine (ELM)-based peak detection algorithm. We found that the Dingle model gave the best performance, with 72 % accuracy in the analysis of real EEG data. Statistical analysis conferred that the Dingle model afforded significantly better mean testing accuracy than did the Acir and Liu models, which were in the range 37–52 %. Meanwhile, the Dingle model has no significant difference compared to Dumpala model

Fully convolutional neural network for Malaysian road lane detection

Recently, a deep learning, Fully Convolutional Neural Network (FCN) has been widely studied because it can demonstrate promising results in the application of detection of objects in an image or video. Hence, the FCN approach has been proposed as one of the solution methods in mitigating the issues pertinent to Malaysia’s road lane detection. Previously, FCN model for lane detection has not been tested in Malaysian road conditions. Therefore, this study investigates the further performance of this model in the Malaysia. The network model is trained and validated using the datasets obtained from Machine Learning NanoDegree. In addition, the real-time data collection has been conducted to collect the data sets for the testing at the highway and urban areas in Malaysia. Then, the collected data is used to test the performance of the FCN network in detecting the lane markings on Malaysia road. The results demonstrated that the FCN method is achieving 99% of the training and validation accuracy

Waveguide for vortex mode generation in HVAC cloud management communication

Optical modes allow for the transmission of data by propagating light in a singular coherent form along the channel. By constructing a special waveguide structure, a unique mode may be formed in a plane perpendicular to the transmission axis. This paper elucidates on the design of a waveguide to generate unique vortex modes and analyses the properties of the generated modes

Radial basis function neural network for head roll prediction modelling in a motion sickness study

Motion Sickness (MS) is the result of uneasy feelings that occurs when travelling. In MS mitigation studies, it is necessary to investigate and measure the occupant’s Motion Sickness Incidence (MSI) for analysis purposes. One way to mathematically calculate the MSI is by using a 6-DOF Subjective Vertical Conflict (SVC) model. This model utilises the information of the vehicle lateral acceleration and the occupant’s head roll angle to determine the MSI. The data of the lateral acceleration can be obtained by using a sensor. However, it is impractical to use a sensor to acquire the occupant’s head roll response. Therefore, this study presents the occupant’s head roll prediction model by using the Radial Basis Function Neural Network (RBFNN) method to estimate the actual head roll responses. The prediction model is modelled based on the correlation between lateral acceleration and head roll angle during curve driving. Experiments have been conducted to collect real naturalistic data for modelling purposes. The results show that the predicted responses from the model are similar with the real responses from the experiment. In future, it is expected that the prediction model will be useful in measuring the occupant’s MSI level by providing the estimated head roll responses

Pareto optimality concept for incorporating prior knowledge for system identification problem with insufficient samples

The issue of insufficient samples usually occurs in real engineering problems because of the time-consuming and expensive nature of collecting samples. In general, nonlinear modeling based on limited samples is rather difficult. Incorporating prior knowledge into this type of problem might offer a promising solution. In practice, different forms of prior knowledge may be available, and their use can avoid the weakness of training sample limitation. The primary focus of this study is to introduce an alternative approach for incorporating prior knowledge based on the Pareto optimality concept by improving the initialization of the chromosome and obtaining a reliable Pareto front. In general, the proposed technique relies on the generation of a set of solutions by considering the available training samples and prior knowledge in modeling. As there are many difficulties in obtaining a good Pareto front, we discuss the challenges of implementing the proposed technique, including the formulation of two-objective functions, the uncertainty of the obtained Pareto front and the complexity of the problem space. To validate the proposed technique, a benchmark problem and a control engineering problem are investigated. It is shown that the proposed technique can be implemented by capturing the best solution in the obtained Pareto front, and the accuracy of the prediction for the system identification problem can be improved by up to 10 %

Transfer learning of bci using cur algorithm

The brain computer interface (BCI) are used in many applications including medical, environment, education, economy, and social fields. In order to have a high performing BCI classification, the training set must contain variations of high quality subjects which are discriminative. Variations will also drive transferability of training data for generalization purposes. However, if the test subject is unique from the training set variations, BCI performance may suffer. Previously, this problem was solved by introducing transfer learning in the context of spatial filtering on small training set by creating high quality variations within training subjects. In this study however, it was discovered that transfer learning can also be used to compress the training data into an optimal compact size while improving training data performance. The transfer learning framework proposed was on motor imagery BCI-EEG using CUR matrix decomposition algorithm which decomposes data into two components; C and UR which is each subject’s EEG signal and common matrix derived from historical EEG data, respectively. The method is considered transfer learning process because it utilizes historical data as common matrix for the classification purposes. This framework is implemented in the BCI system along with Common Spatial Pattern (CSP) as features extractor and Extreme Learning Machine (ELM) as classifier and this combination exhibits an increase of accuracy to up to 26% with 83% training database compression

The implementation of EEG transfer learning method using integrated selection for motor imagery signal

Brain-computer interface (BCI) is a system that can translate, manage, and recognize human brain activity. One of the devices used in the BCI system is Electroencephalogram (EEG). The brain signals produced by the EEG are diverse. One of them is the motor imagery signal. Motor imagery signal is used to translate the EEG signal into a specific movement. The performance of motor imagery signal classification is influenced by the number of training and testing data used. In most cases, the training data consists of a higher number of trials than the testing data. However, more trials cause higher subject variation. Previously study mentioned that this problem can be overcome by using transfer learning methods, which aimed at simplifying the training model. In this study, transfer learning in BCI is implemented using the integrated selection (IS) method, which simplifies the training model. Furthermore, IS is optimizing the data by removing the irrelevant channels of the EEG signals. Integrated selection uses the CUR matrix decomposition algorithm. The method split the data into two components, namely identity and historical data, represented by the C and UR matrix, respectively. The characteristic of the data from IS then calculated using three feature extraction methods. They are Fast Fourier Transform (FFT), Hjorth Descriptor, and Common Spatial Pattern (CSP). The features are then classified using the k-Nearest Neighbor (K-NN) method. The use of IS in the BCI system increases the accuracy of more than 6% and six-times faster processing time. In general, the integrated selection method is able to improve the performance of the BCI system

Incorporating prior knowledge in solving system identification problem with insufficient samples based on pareto optimality concept

Non-linear modeling based on limited samples is a difficult problem. Incorporating a prior knowledge to this type of problem might offer a promising solution. Various techniques have been proposed to incorporate prior knowledge but depend on one optimal solution which subject to pre-selection of coefficients. Incorporating the knowledge based on Pareto optimality concept offers simple post-selection of solutions. Yet, the proposed Pareto optimality concept may trap to either under-fitting or over-fitting problem based on the obtained Pareto front. The focus of this study is primarily to improve the initialization of the chromosome in order to obtain a reliable Pareto front. One system identification of control engineering problem is used as a problem to be validated. It is shown that the proposed technique is possible to be implemented by capturing the best solution in the obtained Pareto front and relatively improve the accuracy up to 8% performance of the prediction