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.

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.

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 ≈ 87% at different uncertainty levels. Our findings suggest that reinforcement learning is a promising approach for conflict resolution.

Approach and landing are phases of flight with the highest accident risk. Advanced instruments and procedures have been developed to provide precise navigation for a stabilized approach and landing. With the proliferation of sensing techniques, real-time 4D trajectories can be captured at higher spatial-temporal resolution and enable data-driven decision-making for air traffic controllers (ATCO). This research attempts to augment the existing rule-based stable approach criteria using data-driven and interpretable tunnel Gaussian process (TGP) models to probabilistically characterize the 4D approach and landing parameters. The TGP explicitly and continuously models the underlying distribution of approach and landing parameters and their interrelations. In addition, it provides a comprehensible probabilistic description of anomalies in approach and landing parameters. Based on the trained TGP, we infer the landing parameters of go-around tracks recorded by the advanced surface movement guidance and control system (A-SMGCS) and analyze their adherence to stabilized approach criteria. Empirical results show that anomalous scores were in line with the factors (as reported to ATCOs) in all go-around data in the test dataset, between 0.5 NM (missed approach point) to 7.6 NM from the touchdown threshold, and provides better probabilistic insights of non-compliance, comparing to existing work. Hence, the proposed TGP can provide a ground-based safety net for the compliance of stable approaches. Furthermore, the proposed TGP-based anomaly tracking methods can be directly applied to other types of landing systems (e.g., GNSS landing system and RNAV approaches).