Sim Kuan Goh

Bio: Sim Kuan Goh is an academic researcher from Nanyang Technological University. The author has contributed to research in topics: Air traffic control & Flight dynamics. The author has an hindex of 7, co-authored 21 publications receiving 164 citations. Previous affiliations of Sim Kuan Goh include National University of Singapore.

Evolutionary Big Optimization (BigOpt) of Signals

Abstract: Challenging multi-modal optimization problems have been very successfully solved by evolutionary computation (EC) techniques. To date, many methods have been proposed on evolutionary optimization for both single and multiobjective large scale problems. In the age of Big Data, there is an urge to take evolutionary optimization techniques to the next level for solving problems with even larger scales: thousands and millions of variables. These problems arise in many domains ranging from bioinformatics, to neuroscience and social simulations. In this paper, we investigate the use of EC to solve Big electroencephalography (EEG) data optimization problems with thousands of variables. The optimization problem attempts to identify maximum information that should be kept from a signal while minimizing the artifact. The high level of epistasis inherent in a signal can slow down the evolution. Therefore, we investigate the advantages of optimizing the problem in the frequency domain with different thresholds as opposed to the time domain. We propose synthetic EEG data sets of various scale and noise level. These data sets were the basis for the Optimization of Big Data 2015 Competition (BigOpt), CEC 2015. Two state-of-art multiobjective evolutionary algorithms (MOEAs) were evaluated. The results of this work suggest that frequency representation of the signals facilitates dimensionality reduction for big scale optimization of time series data, and hence provides faster and better quality solutions for EEG data cleaning. Moreover, the results suggest that existing state-of-art multiobjective evolutionary computation methods are extremely slow. Methods that can optimize the problem faster and with high quality are needed.

Spatio–Spectral Representation Learning for Electroencephalographic Gait-Pattern Classification

Abstract: The brain plays a pivotal role in locomotion by coordinating muscles through interconnections that get established by the peripheral nervous system. To date, many attempts have been made to reveal the underlying mechanisms of humans’ gait. However, decoding cortical processes associated with different walking conditions using EEG signals for gait-pattern classification is a less-explored research area. In this paper, we design an EEG-based experiment with four walking conditions (i.e., free walking, and exoskeleton-assisted walking at zero, low, and high assistive forces by the use of a unilateral exoskeleton to right lower limb). We proposed spatio-spectral representation learning (SSRL), a deep neural network topology with shared weights to learn the spatial and spectral representations of multi-channel EEG signals during walking. Adoption of weight sharing reduces the number of free parameters, while learning spatial and spectral equivariant features. SSRL outperformed state-of-the-art methods in decoding gait patterns, achieving a classification accuracy of 77.8%. Moreover, the features extracted in the intermediate layer of SSRL were observed to be more discriminative than the hand-crafted features. When analyzing the weights of the proposed model, we found an intriguing spatial distribution that is consistent with the distribution found in well-known motor-activated cortical regions. Our results show that SSRL advances the ability to decode human locomotion and it could have important implications for exoskeleton design, rehabilitation processes, and clinical diagnosis.

Automatic EEG Artifact Removal Techniques by Detecting Influential Independent Components

Abstract: Electroencephalography (EEG) data are used to design useful indicators that act as proxies for detecting humans’ mental activities. However, these electrical signals are susceptible to different forms of interferences—known as artifacts—from voluntarily and involuntarily muscle movements that greatly obscure the information in the signal. It is pertinent to design effective artifact removal techniques (ARTs) capable of removing or reducing the impact of these artifacts. However, most ARTs have been focusing on handling a few specific types, or a single type, of EEG artifacts. EEG processing that generalizes to multiple types of artifacts remains a major challenge. In this paper, we investigate a variety of eight different and typical artifacts that occur in practice. We characterize the spatiotemporal-frequency influence of these EEG artifacts and offer two heuristics. The proposed heuristics extend influential independent component analysis to clean the contaminated EEG signal. These proposed heuristics are compared against four state-of-the-art EEG ARTs using both real and synthesized EEG, collected in the presence of multiple artifacts. The results show that both heuristics offer superior spatiotemporal-frequency performance in automatic artifacts removal and are able to reconstruct clean EEG signals.

Artifact Removal from EEG Using a Multi-objective Independent Component Analysis Model

Abstract: Independent Component Analysis (ICA) has been widely used for separating artifacts from Electroencephalographic (EEG) signals Still, a few challenging problems remain

Reinforcement Learning for Two-Aircraft Conflict Resolution in the Presence of Uncertainty

Abstract: Recently, the advances in reinforcement learning have enabled an artificial intelligent agent to solve many challenging problems (e.g. AlphaGo) at unprecedented levels. However, the robustness of reinforcement learning in safety critical operation remains unclear. In this work, the applicability of reinforcement learning in Air Traffic Control was explored. We focus on building an algorithm to automate flight conflict resolution which is an ultimate goal of air traffic control. For that purpose, a simulator, that provides learning environment for reinforcement learning, was developed to simulate a variety of air traffic scenarios. We propose a variant of reinforcement learning approach to resolve conflict in an airspace and investigate the performance of the method in achieving that. Reinforcement learning model, specifically deep deterministic policy gradient, was adopted to learn the conflict resolution with continuous action spaces. Experimental results demonstrate that our proposed method is effective in resolving conflict between two aircraft even in the presence of uncertainty. The accuracy of our model is $\approx 87\%$ at different uncertainty levels. Our findings suggest that reinforcement learning is a promising approach for conflict resolution.

Deep learning for electroencephalogram (EEG) classification tasks: a review.

Abstract: Objective Electroencephalography (EEG) analysis has been an important tool in neuroscience with applications in neuroscience, neural engineering (e.g. Brain-computer interfaces, BCI's), and even commercial applications. Many of the analytical tools used in EEG studies have used machine learning to uncover relevant information for neural classification and neuroimaging. Recently, the availability of large EEG data sets and advances in machine learning have both led to the deployment of deep learning architectures, especially in the analysis of EEG signals and in understanding the information it may contain for brain functionality. The robust automatic classification of these signals is an important step towards making the use of EEG more practical in many applications and less reliant on trained professionals. Towards this goal, a systematic review of the literature on deep learning applications to EEG classification was performed to address the following critical questions: (1) Which EEG classification tasks have been explored with deep learning? (2) What input formulations have been used for training the deep networks? (3) Are there specific deep learning network structures suitable for specific types of tasks? Approach A systematic literature review of EEG classification using deep learning was performed on Web of Science and PubMed databases, resulting in 90 identified studies. Those studies were analyzed based on type of task, EEG preprocessing methods, input type, and deep learning architecture. Main results For EEG classification tasks, convolutional neural networks, recurrent neural networks, deep belief networks outperform stacked auto-encoders and multi-layer perceptron neural networks in classification accuracy. The tasks that used deep learning fell into five general groups: emotion recognition, motor imagery, mental workload, seizure detection, event related potential detection, and sleep scoring. For each type of task, we describe the specific input formulation, major characteristics, and end classifier recommendations found through this review. Significance This review summarizes the current practices and performance outcomes in the use of deep learning for EEG classification. Practical suggestions on the selection of many hyperparameters are provided in the hope that they will promote or guide the deployment of deep learning to EEG datasets in future research.

Bioelectrical Signal Processing In Cardiac And Neurological Applications

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Functional Neuroimaging Studies of Motor Recovery After Stroke in Adults

Abstract: Background— The precise mechanisms of and biological basis for motor recovery after stroke in adults are still largely unknown. Reorganization of the motor system after stroke as assessed by functional neuroimaging is an intriguing but challenging new field of research. Provocative but equivocal findings have been reported to date. Summary of Review— We present an overview of functional neuroimaging studies (positron emission tomography or functional MRI) of motor tasks in patients recovered or still recovering from motor deficit after stroke. After a brief account of the connectivity of motor systems and the imaging findings in normal subjects, the literature concerning stroke patients is reviewed and discussed, and a general model is proposed. Conclusions— Both cross-sectional and longitudinal studies have demonstrated that the damaged adult brain is able to reorganize to compensate for motor deficits. Rather than a complete substitution of function, the main mechanism underlying recovery of motor abili...