PhyCS 2018 Abstracts


Area 1 - Devices

Full Papers
Paper Nr: 6
Title:

Exploring the Feasibility and Performance of One-step Three-factor Authentication with Ear-EEG

Authors:

Max T. Curran, Nick Merrill, Swapan Gandhi and John Chuang

Abstract: Multi-factor authentication presents a robust method to secure our private information, but typically requires multiple actions by the user resulting in a high cost to usability and limiting adoption. A usable system should also be unobtrusive and inconspicuous. We present and discuss a system with the potential to engage all three factors of authentication (inherence, knowledge, and possession) in a single step using an earpiece that implements brain-based authentication using electroencephalography (EEG). We demonstrate its potential by collecting EEG data using manufactured custom-fit earpieces with embedded electrodes and testing a variety of authentication scenarios. Across all participants’ best-performing “passthoughts”, we are able to achieve 0% false acceptance and 0.36% false rejection rates, for an overall accuracy of 99.82%, using one earpiece with three electrodes. Furthermore, we find no successful attempts simulating impersonation attacks. We also report on perspectives from our participants. Our results suggest that a relatively inexpensive system using a single electrode-laden earpiece could provide a discreet, convenient, and robust method for one-step multi-factor authentication.

Paper Nr: 19
Title:

Making Sense: Experiences with Multi-Sensor Fusion in Industrial Assistance Systems

Authors:

Benedikt Gollan, Michael Haslgruebler, Alois Ferscha and Josef Heftberger

Abstract: This workshop paper discusses the application of various sensors in an industrial assembly scenario, in which multiple sensors are deployed to enable the detailed monitoring of worker activity, task progress and also cognitive and mental states. The described and evaluated sensors include stationary (RGBD cameras, stereo vision depth sensors) and wearable devices (IMUs, GSR, ECG, mobile eye tracker). Furthermore, this paper discusses the associated challenges mainly related to multi-sensor fusion, real-time data processing and semantic interpretation of data.

Short Papers
Paper Nr: 3
Title:

The Feasibility and Effectiveness of P300 Responses using Low Fidelity Equipment in Three Distinctive Environments

Authors:

Patrick Schembri, Richard Anthony and Mariusz Pelc

Abstract: In this paper we investigate the viability, practicability and efficacy of eliciting P300 responses based on the P300 speller BCI paradigm (oddball) and the xDAWN algorithm, with five healthy subjects; while using a non-invasive Brain Computer Interface (BCI) based on low fidelity electroencephalographic (EEG) equipment. The experiments were performed in three distinctive environments: lab conditions, mild and controlled user distractions, and real world environment (realistic sound and visual distractions present). Our main contribution is the assessment of the ways and extents to which different degrees of user distraction affect the detection success achievable using low fidelity equipment. Our results demonstrate the applicability of using off-the-shelf equipment as a means to successfully and effectively detect P300 responses, with different degrees of success across the three distinctive types of environment.

Area 2 - Human Factors

Full Papers
Paper Nr: 2
Title:

Regression Analyses between Physiological Indexes and Level of Understanding with VAS of a Listening Task

Authors:

Masaki Omata and Kazuya Nakazawa

Abstract: This paper describes logistic regression analyses and multiple regression analyses to explain relationships between physiological signals and subjective self-reported levels of understanding of a second language listening task by using a visual analog scale (VAS) of 999 degrees. Mean contribution ratio of the logistic regression expressions was 0.72, and mean contribution ratio of the multiple regression expressions was 0.70. Power value of theta band of brain waves has a certain tendency to change according to the level of understanding. Accuracy of the regression expressions using VAS was the same or more than that of the four-level scale as our previous work.

Paper Nr: 7
Title:

Flow Neurophysiology in Knowledge Work: Electroencephalographic Observations from Two Cognitive Tasks

Authors:

Michael T. Knierim, Mario Nadj, Anuja Hariharan and Christof Weinhardt

Abstract: In an effort to study flow experiences in the context of less structured knowledge work (KW), we explored a paradigm we call controlled experience sampling (cESM). Participants worked on a naturalistic, cognitive task (a personal scientific thesis), and a difficulty-manipulated math task. Results show that the cESM approach elicits a consistent flow experience with intensities as least as high as in the math task flow condition. An interesting finding is that given similar flow intensities, different perceptions of stress arise between the two paradigms. EEG results from both tasks suggest increased frontal upper alpha band (10-12Hz) activity with increased task attention, that has higher temporal stability in flow than in a boredom condition, and that is laterally indifferent. Integrating with the presently available literature, the results further consolidate an understanding of flow as a state of fronto-lateral activation.

Area 3 - Methodologies and Methods

Short Papers
Paper Nr: 4
Title:

Integrated Protocol for Objective Pain Assessment

Authors:

Maria Ghita, Mihaela Ghita, Clara Ionescu and Dana Copot

Abstract: In the absence of any standardized objective aid for measuring pain levels in human body, a manifold of subjective tools have been developed to monitor chronic pain patients and intra-/post-operative analgesic drug management. However, due to the subjective nature of the evaluation methods and tools, pain remains a challenging phenomenon to be characterised for objective assessment and monitoring. In this paper we briefly describe a protocol and methodology for non-invasive evaluation of pain as result of nociceptor stimulation via skin impedance measurements. Both time-frequency domain analysis is performed, providing interesting observations.

Paper Nr: 13
Title:

Deep Learning in EMG-based Gesture Recognition

Authors:

P. Tsinganos, B. Cornelis, J. Cornelis, B. Jansen and A. Skodras

Abstract: In recent years, Deep Learning methods have been successfully applied to a wide range of image and speech recognition problems highly impacting other research fields. As a result, new works in biomedical engineering are directed towards the application of these methods to electromyography-based gesture recognition. In this paper, we present a brief overview of Deep Learning methods for electromyography-based hand gesture recognition along with an analysis of a modified simple model based on Convolutional Neural Networks. The proposed network yields a 3% improvement on the classification accuracy of the basic model, whereas the analysis helps in understanding the limitations of the model and exploring new ways to improve the performance.

Paper Nr: 15
Title:

Empirical Evaluation of the Potential of Low-cost and Open Source “On-the-Person” ECG for Cardiopathy Pre-screening

Authors:

Hélio B. M. Lourenço, Víctor Sanfins, Sílvia Ala, Francisco Barros, Hugo P. Silva and Manuel J.C.S. Reis

Abstract: Electrocardiographic (ECG) data analysis can reveal crucial information about the cardiovascular physiological phenomenon, which is modulated by the Autonomic Nervous System. Hereupon, beyond cardiovascular diagnosis, ECG markers can also reflect workload levels, or even physical and mental performance, through Heart Rate Variability (HRV) analysis. Building upon previous work found within the state-of-the-art, this pilot research explores the potential of using a low-cost device for cardiopathy pre-screening, through ECG signal analysis. With the aim of performing the rhythmical analysis, we performed empirical tests from a population of 21 control subjects in a resting position, and an additional 2 subjects, one of them in dynamic condition, in the scope of an exploratory research, using ECG wave segments analysis and HRV features extraction for numerical analysis. Results have demonstrated that the signal quality allows reliable ECG acquisition for further rhythmical and HRV analysis, in stationary and dynamic monitoring, for the bipolar leads applied. There was also evidence to suggest a benefit from including ECG morphological analysis with this hardware and software setup for prevention and diagnosis of cardiovascular disorders, although requiring further investigation.

Paper Nr: 16
Title:

How Wild Is Too Wild: Lessons Learned and Recommendations for Ecological Validity in Physiological Computing Research

Authors:

Elise Labonte-LeMoyne, François Courtemanche, Marc Fredette and Pierre-Majorique Léger

Abstract: While many call for increased ecological validity in physiological computing research, implementing very naturalistic studies can be challenging. In this paper, we present a way to quantify ecological validity to allow comparisons between studies. We also present a critical look at four types of studies that have emerged from quantifying the ecological validity of our past experiments. Finally, we provide recommendations and lessons learned from our own work conducting studies that span a wide range of levels of ecological validity for researchers who wish to do more in the wild research.

Paper Nr: 17
Title:

Developing Personas based on Physiological Measures

Authors:

Vanessa Georges, François Courtemanche, Marc Fredette, Pierre-Majorique Léger and Sylvain Sénécal

Abstract: The objective of this paper is to propose a novel approach for the creation of user personas using common patterns in psychophysiological signals. We illustrate the persona creation process through a case example. Using this method, we were able to identify 4 distinct subgroups of varying experience and satisfaction levels. This novel approach illustrates the potential of physiological measures in the identification of various user clusters, based on one or more experiential aspect, as these signals can provide information as to what users are experiencing during the interaction without interference. This should be useful for user experience researchers, practitioners and designers alike to build more accurate user profiles, especially in the context of large scale public installations and immersive experiences.

Posters
Paper Nr: 10
Title:

Efficient Classification of Digital Images based on Pattern-features

Authors:

Angelo Furfaro and Simona E. Rombo

Abstract: Selecting a suitable set of features, which is able to represent the data to be processed while retaining the relevant distinctive information, is one of the most important issues in classification problems. While different features can be extracted from the raw data, only few of them are actually relevant and effective for the classification process. Since relevant features are often unknown a priori, many candidate features are usually introduced. This degrades both the speed and the predictive accuracy of the classifier due to the presence of redundancy in the feature candidate set. We propose a class of features for image classification based on the notion of irredundant bidimensional pair-patterns, and we present an algorithm for image classification based on their extraction. The devised technique scales well on parallel multi-core architectures, as witnessed by the experimental results that have been obtained exploiting a benchmark image dataset.

Area 4 - Applications

Full Papers
Paper Nr: 14
Title:

Exploring Biofeedback with a Tangible Interface Designed for Relaxation

Authors:

Morgane Hamon, Rémy Ramadour and Jérémy Frey

Abstract: Anxiety is a common health issue that can occur throughout one’s existence. In this pilot study we explore an alternative technique to regulate it: biofeedback. The long-term objective is to offer an ecological device that could help people cope with anxiety, by exposing their inner state in a comprehensive manner. We propose a first iteration of this device, “Inner Flower”, that uses heart rate to adapt a breathing guide to the user; and we investigate its efficiency and usability. Traditionally, such device requires user’s full attention. We propose an ambient modality during which the device operates in the peripheral vision. Beside comparing “Ambient” and “Focus” conditions, we also compare the biofeedback with a sham feedback (fixed breathing guide). We found that the Focus group demonstrated higher relaxation and performance on a cognitive task (N-back). However, there was no noticeable effect of the Ambient feedback, and the biofeedback condition did not yield any significant difference when compared to the sham feedback. These results, while promising, highlight the pitfalls of any research related to biofeedback, where it is difficult to fully comprehend the underlying mechanisms of such technique.

Posters
Paper Nr: 11
Title:

A Preliminary Study about the Music Influence on EEG and ECG Signals

Authors:

Manuel Merino-Monge, Isabel M. Gómez-González, Juan A. Castro-García, Alberto J. Molina-Cantero and Roylán Quesada-Tabares

Abstract: In this work, music is used to elicit emotions and the impact produced by it on the electrocardiogram and electroencephalogram signals is measured. Test consists a sequence of 12 songs where each one is played during 1 minute. Songs were grouped in 4 sets based on pleasant/activation level. In this preliminary study, 6 male subjects realized the trial. Individuals scored each song using Self-Assessment Manikin (SAM) survey. Biosignal parameters were analyzed with Kruskal-Wallis test (KWT). Although the sample of the subjects on whom the test was performed is small, significant variation is observed in 3 parameters extracted from the electrocardiogram when features are grouped using SAM values from survey filled in by subjects. These parameters show an increasing of heart rate with arousal level and when songs are not totally matched with individual preferences. The use of information extracted from biosignals in therapies for individuals with low interaction is proposed for future studies.