Brain signals refer to the biometric information collected from the human brain. The research on brain signals aims to discover the underlying neurological or physical status of the individuals by signal decoding. The emerging deep learning techniques have improved the study of brain signals significantly in recent years. In this work, we first present a taxonomy of non-invasive brain signals and the basics of deep learning algorithms. Then, we provide a comprehensive survey of the frontiers of applying deep learning for non-invasive brain signals analysis, by summarizing a large number of recent publications. Moreover, upon the deep learning-powered brain signal studies, we report the potential real-world applications which benefit not only disabled people but also normal individuals. Finally, we discuss the opening challenges and future directions.

https://iopscience.iop.org/article/10.1088/1741-2552/abc902/pdf

A survey on deep learning-based non-invasive brain signals: recent advances and new frontiers.

Brain-Computer Interface (BCI) bridges the human's neural world and the outer physical world by decoding individuals' brain signals into commands recognizable by computer devices. Deep learning has lifted the performance of brain-computer interface systems significantly in recent years. In this article, we systematically investigate brain signal types for BCI and related deep learning concepts for brain signal analysis. We then present a comprehensive survey of deep learning techniques used for BCI, by summarizing over 230 contributions most published in the past five years. Finally, we discuss the applied areas, opening challenges, and future directions for deep learning-based BCI.

A Survey on Deep Learning based Brain Computer Interface: Recent Advances and New Frontiers.

In recent years, visual object tracking has become a very active research area. An increasing number of tracking algorithms are being proposed each year. It is because tracking has wide applications in various real-world problems such as human-computer interaction, autonomous vehicles, robotics, surveillance, and security just to name a few. In the current study, we review latest trends and advances in the tracking area and evaluate the robustness of different trackers based on the feature extraction methods. The first part of this work includes a comprehensive survey of the recently proposed trackers. We broadly categorize trackers into Correlation Filter based Trackers (CFTs) and Non-CFTs. Each category is further classified into various types based on the architecture and the tracking mechanism. In the second part of this work, we experimentally evaluated 24 recent trackers for robustness and compared handcrafted and deep feature based trackers. We observe that trackers using deep features performed better, though in some cases a fusion of both increased performance significantly. To overcome the drawbacks of the existing benchmarks, a new benchmark Object Tracking and Temple Color (OTTC) has also been proposed and used in the evaluation of different algorithms. We analyze the performance of trackers over 11 different challenges in OTTC and 3 other benchmarks. Our study concludes that Discriminative Correlation Filter (DCF) based trackers perform better than the others. Our study also reveals that inclusion of different types of regularizations over DCF often results in boosted tracking performance. Finally, we sum up our study by pointing out some insights and indicating future trends in the visual object tracking field.

Handcrafted and Deep Trackers: Recent Visual Object Tracking Approaches and Trends

A visual tracking system is essential as a basis for visual servoing, autonomous navigation, path planning, robot-human interaction and other robotic functions. To execute various tasks in diverse and ever-changing environments, a mobile robot requires high levels of robustness, precision, environmental adaptability and real-time performance of the visual tracking system. In keeping with the application characteristics of our amphibious spherical robot, which was proposed for flexible and economical underwater exploration in 2012, an improved RGB-D visual tracking algorithm is proposed and implemented. Given the limited power source and computational capabilities of mobile robots, compressive tracking (CT), which is the effective and efficient algorithm that was proposed in 2012, was selected as the basis of the proposed algorithm to process colour images. A Kalman filter with a second-order motion model was implemented to predict the state of the target and select candidate patches or samples for the CT tracker. In addition, a variance ratio features shift (VR-V) tracker with a Kalman estimation mechanism was used to process depth images. Using a feedback strategy, the depth tracking results were used to assist the CT tracker in updating classifier parameters at an adaptive rate. In this way, most of the deficiencies of CT, including drift and poor robustness to occlusion and high-speed target motion, were partly solved. To evaluate the proposed algorithm, a Microsoft Kinect sensor, which combines colour and infrared depth cameras, was adopted for use in a prototype of the robotic tracking system. The experimental results with various image sequences demonstrated the effectiveness, robustness and real-time performance of the tracking system.

http://www.guolab.org/Papers/2015/SENSORS2015.pdf

A Kinect-Based Real-Time Compressive Tracking Prototype System for Amphibious Spherical Robots

Improved anti-occlusion object tracking algorithm using Unscented Rauch-Tung-Striebel smoother and kernel correlation filter

Visual tracking is a popular and challenging topic in computer vision and robotics. Owing to changes in the appearance of the target and complicated variations that may occur in various scenes, online learning scheme is necessary for advanced visual tracking framework to adopt. This paper briefly introduces the challenges and applications of visual tracking and focuses on discussing the state-of-the-art online-learning-based tracking methods by category. We provide detail descriptions of representative methods in each category, and examine their pros and cons. Moreover, several most representative algorithms are implemented to provide quantitative reference. At last, we outline several trends for future visual tracking research.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-cvi.2013.0134

Survey of single-target visual tracking methods based on online learning

This paper presents a pattern recognition model using multiple features and the kernel extreme learning machine (ELM), improving the accuracy of automatic epilepsy diagnosis After simple preprocessing, temporal- and wavelet-based features are extracted from epileptic EEG signals A combined kernel-function-based ELM approach is then proposed for feature classification To further reduce the computation, Cholesky decomposition is introduced during the process of calculating the output weights The experimental results show that the proposed method can achieve satisfactory accuracy with less computation time

Epileptic seizure detection based on the kernel extreme learning machine.

This paper introduces a deep learning approach to the feature extraction of P300 cognitive component existing in electroencephalogram signals collected in an autobiographical paradigm test A thorough belief mechanism is used for the extraction of deep characteristics rather than raw feature vectors to train the classifier It is shown that the classification accuracy is satisfactory by learning deep from the experimental data Experiments have validated the usefulness of the algorithm The hidden information has been obtained accurately with a single electroencephalogram channel Moreover, performances of support vector machine with different feature extraction methods are compared

Deep Belief Networks for EEG-Based Concealed Information Test

In this paper, we introduce a metric learning approach for the classification process in the recognition procedure for P300 waves in electroencephalographic (EEG) signals. We show that the accuracy of support machine vector (SVM) classification is significantly improved by learning a similarity metric from the training data instead of using the default Euclidean metric. The effectiveness of the algorithm is validated through experiments on the dataset II of the brain-computer interface (BCI) Competition III(P300 speller). Index Terms – Metric learning, SVM, P300

Metric learning for event-related potential component classification in EEG signals

In this paper, the principle of the kernel extreme learning machine (ELM) is analyzed Based on that, we introduce a kind of multi-scale wavelet kernel extreme learning machine classifier and apply it to electroencephalographic (EEG) signal feature classification Experiments show that our classifier achieves excellent performance

Xiaoguang Zhao

Papers

Survey of single-target visual tracking methods based on online learning

Epileptic seizure detection based on the kernel extreme learning machine.

Deep Belief Networks for EEG-Based Concealed Information Test

Metric learning for event-related potential component classification in EEG signals

Multi-scale wavelet kernel extreme learning machine for EEG feature classification