Fuzzy decision-making fuser (FDMF) for integrating human-machine autonomous (HMA) systems with adaptive evidence sources

© 2017 Liu, Pal, Marathe, Wang and Lin. A brain-computer interface (BCI) creates a direct communication pathway between the human brain and an external device or system. In contrast to patient-oriented BCIs, which are intended to restore inoperative or malfunctioning aspects of the nervous system, a growing number of BCI studies focus on designing auxiliary systems that are intended for everyday use. The goal of building these BCIs is to provide capabilities that augment existing intact physical and mental capabilities. However, a key challenge to BCI research is human variability; factors such as fatigue, inattention, and stress vary both across different individuals and for the same individual over time. If these issues are addressed, autonomous systems may provide additional benefits that enhance system performance and prevent problems introduced by individual human variability. This study proposes a human-machine autonomous (HMA) system that simultaneously aggregates human and machine knowledge to recognize targets in a rapid serial visual presentation (RSVP) task. The HMA focuses on integrating an RSVP BCI with computer vision techniques in an image-labeling domain. A fuzzy decision-making fuser (FDMF) is then applied in the HMA system to provide a natural adaptive framework for evidence-based inference by incorporating an integrated summary of the available evidence (i.e., human and machine decisions) and associated uncertainty. Consequently, the HMA system dynamically aggregates decisions involving uncertainties from both human and autonomous agents. The collaborative decisions made by an HMA system can achieve and maintain superior performance more efficiently than either the human or autonomous agents can achieve independently. The experimental results shown in this study suggest that the proposed HMA system with the FDMF can effectively fuse decisions from human brain activities and the computer vision techniques to improve overall performance on the RSVP recognition task. This conclusion demonstrates the potential benefits of integrating autonomous systems with BCI systems

Weighted Fuzzy Dempster-Shafer Framework for Multimodal Information Integration

© 1993-2012 IEEE. This study proposes an architecture based on a weighted fuzzy Dempster-Shafer framework (WFDSF), which can adjust weights associated with inconsistent evidence obtained by different classification approaches, to realize a fusion system for integrating multimodal information. The Dempster-Shafer theory (D-S theory) of evidence enables us to integrate heterogeneous information from multiple sources to obtain collaborative inferences for a given problem. To conquer various uncertainties associated with the collected information, our system assigns beliefs and plausibilities to possible hypotheses of each decision maker and uses a combination rule to fuse multimodal information. For information fusion, an important step in D-S aggregation is to find an appropriate basic probability assignment scheme for allocating support to each possible hypothesis/class, which remains an arduous and unsolved problem. Here, we propose a mathematical structure to aggregate weighted evidence extracted from two different types of approaches: fuzzy Naïve Bayes and nearest mean classification rule. Further, an intuitionistic belief assignment is employed to address uncertainties between hypotheses/classes. Finally, 12 benchmark problems from the UCI machine learning repository for classification are employed to validate the proposed WFDSF-based scheme. In addition, an application of WFDSF to a practical brain-computer interface problem involving multimodal data fusion is demonstrated in this study. The experimental results show that the WFDSF is superior to several existing methods

Experiences and insights from the collection of a novel multimedia EEG dataset

There is a growing interest in utilising novel signal sources such as EEG (Electroencephalography) in multimedia research. When using such signals, subtle limitations are often not readily apparent without significant domain expertise. Multimedia research outputs incorporating EEG signals can fail to be replicated when only minor modifications have been made to an experiment or seemingly unimportant (or unstated) details are changed. This can lead to overoptimistic or overpessimistic viewpoints on the potential real-world utility of these signals in multimedia research activities. This paper describes an EEG/MM dataset and presents a summary of distilled experiences and knowledge gained during the preparation (and utilisiation) of the dataset that supported a collaborative neural-image labelling benchmarking task. The goal of this task was to collaboratively identify machine learning approaches that would support the use of EEG signals in areas such as image labelling and multimedia modeling or retrieval. The contributions of this paper can be listed thus; a template experimental paradigm is proposed (along with datasets and a baseline system) upon which researchers can explore multimedia image labelling using a brain-computer interface, learnings regarding commonly encountered issues (and useful signals) when conducting research that utilises EEG in multimedia contexts are provided, and finally insights are shared on how an EEG dataset was used to support a collaborative neural-image labelling benchmarking task and the valuable experiences gained

Towards the automated localisation of targets in rapid image-sifting by collaborative brain-computer interfaces

The N2pc is a lateralised Event-Related Potential (ERP) that signals a shift of attention towards the location of a potential object of interest. We propose a single-trial target-localisation collaborative Brain-Computer Interface (cBCI) that exploits this ERP to automatically approximate the horizontal position of targets in aerial images. Images were presented by means of the rapid serial visual presentation technique at rates of 5, 6 and 10 Hz. We created three different cBCIs and tested a participant selection method in which groups are formed according to the similarity of participants’ performance. The N2pc that is elicited in our experiments contains information about the position of the target along the horizontal axis. Moreover, combining information from multiple participants provides absolute median improvements in the area under the receiver operating characteristic curve of up to 21% (for groups of size 3) with respect to single-user BCIs. These improvements are bigger when groups are formed by participants with similar individual performance, and much of this effect can be explained using simple theoretical models. Our results suggest that BCIs for automated triaging can be improved by integrating two classification systems: one devoted to target detection and another to detect the attentional shifts associated with lateral targets

Arginase activities and global arginine bioavailability in wild-type and ApoE-deficient mice: Responses to high fat and high cholesterol diets

Increased catabolism of arginine by arginase is increasingly viewed as an important pathophysiological factor in cardiovascular disease, including atherosclerosis induced by high cholesterol diets. Whereas previous studies have focused primarily on effects of high cholesterol diets on arginase expression and arginine metabolism in specific blood vessels, there is no information regarding the impact of lipid diets on arginase activity or arginine bioavailability at a systemic level. We, therefore, evaluated the effects of high fat (HF) and high fat-high cholesterol (HC) diets on arginase activity in plasma and tissues and on global arginine bioavailability (defined as the ratio of plasma arginine to ornithine + citrulline) in apoE-/- and wild-type C57BL/6J mice. HC and HF diets led to reduced global arginine bioavailability in both strains. The HC diet resulted in significantly elevated plasma arginase in both strains, but the HF diet increased plasma arginase only in apoE-/- mice. Elevated plasma arginase activity correlated closely with increased alanine aminotransferase levels, indicating that liver damage was primarily responsible for elevated plasma arginase. The HC diet, which promotes atherogenesis, also resulted in increased arginase activity and expression of the type II isozyme of arginase in multiple tissues of apoE-/- mice only. These results raise the possibility that systemic changes in arginase activity and global arginine bioavailability may be contributing factors in the initiation and/or progression of cardiovascular disease

Effect of the one-child policy on influenza transmission in China: a stochastic transmission model

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