PhyCS 2017 Abstracts


Area 1 - Devices

Full Papers
Paper Nr: 3
Title:

Entropic Brain-computer Interfaces - Using fNIRS and EEG to Measure Attentional States in a Bayesian Framework

Authors:

Samuel Hincks, Sarah Bratt, Sujit Poudel, Vir V. Phoha, Robert J. K. Jacob, Daniel C. Dennett and Leanne Hirshfield

Abstract: Implicit Brain-Computer Interfaces (BCI) adapt system settings subtly based on real time measures of brain activation without the user’s explicit awareness. For example, measures of the user’s cognitive profile might drive a system that alters the timing of notifications in order to minimize user interruption. Here, we consider new avenues for implicit BCI based on recent discoveries in cognitive neuroscience and conduct a series of experiments using BCI’s principal non-invasive brain sensors, fNIRS and EEG. We show how Bayesian and systems neuroscience formulations explain the difference in performance of machine learning algorithms trained on brain data in different conditions. These new formulations posit that the brain aims to minimize its long-term surprisal of sensory data and organizes its calculations on two anti-correlated networks. We consider how to use real-time input that portrays a user along these dimensions in designing Bidirectional BCIs, which are Implicit BCIs that aim to optimize the user’s state by modulating computer output based on feedback from a brain monitor. We introduce Entropic Brain-Computer Interfacing as a type of Bidirectional BCI which uses physiological measurements of information theoretical dimensions of the user’s state to evaluate the digital flow of information to the user’s brain, tweaking this output in a feedback loop to the user’s benefit.

Paper Nr: 4
Title:

How Commercial Food Videos Affect Female Customers - Measuring Female Bio-response Towards Commercial Food Videos

Authors:

Xintong Zhu, Yong Wang, Zhenzheng Wang, Xiguang Wang and Chen Wang

Abstract: The concept design of a food television commercial (TVC) could affect the effectiveness of immersive media and user experience. Traditional methods (e.g., surveys or eye tracking) for evaluating the targeted consumer’s responses are serious limited in several aspects. In this paper, through closely working with the TVC designers, we used the data gathered from physiological sensor to measure viewers’ watching experiences. We thereby conclude how we used our own Galvanic Skin Response (GSR) sensors to measure audience responses to the three clips of food TVCs. The results demonstrate that GSR sensors can provide fine-grained information for the advertisement community. Compared to subjective evaluation methods, the continuous user experience can be vividly visualized, and this enables designers to efficiently evaluate the impact of a TVC.

Paper Nr: 10
Title:

The Biocybernetic Loop Engine: An Integrated Tool for Creating Physiologically Adaptive Videogames

Authors:

J. E. Muñoz, E. R. Gouveia, M. Cameirão and S. Bermudez I. Badia

Abstract: Biocybernetic loops (BLs) are physiological adaptation mechanisms created to augment human-computer interaction by interpreting human behaviour via physiological responses. Because of its inherent complexity, the development of BLs has been mainly utilized within the academic environment, with limited use of physiologically adaptive systems in promising fields such as assistive and gaming technologies. The Biocybernetic Loop Engine (BL Engine) is an integrated software tool designed for an easy creation of physiologically modulated videogames by means of wearable sensors. The BL Engine includes a signal acquisition panel, which facilitates the connectivity of multiple physiological sensors and the processing of their signals, a biocybernetic console to rapidly create and iterate adaptive rules using a visual scripting module, and a game connector tool that ties physiological modulations to game variables. In this paper, we present the BL Engine software architecture, its design and implementation process, as well as a proof-of-concept of the system applied to an exergaming experience aiming to improve cardiorespiratory fitness training in older adults. By developing integrated tools that aid the design and implementation of BLs in videogames, we aim to contribute to the dissemination and widespread use of this approach in the gaming industry and serious gaming applications.

Short Papers
Paper Nr: 5
Title:

Detection of Electroencephalography Artefacts using Low Fidelity Equipment

Authors:

Patrick Schembri, Richard Anthony and Mariusz Pelc

Abstract: The use of Electroencephalography (EEG) signals in the field of Brain Computer Interface (BCI) has gained prominence over the past decade, with the availability of diverse applications especially in the clinical sector. The major downside is that the current equipment being used at medical level is specialized, complex and very expensive. Our research goals are to further increase accessibility to this technology by providing a unique approach in data analysis techniques, which in return will allow the usage of cheaper and simpler EEG hardware devices targeted for end users. We use non-invasive BCIs designed on EEG, mainly due to its fine temporal resolution, portability and ease of use. The main shortcoming of EEG is that it is frequently contaminated by various artefacts. In this paper we provide vital groundwork by identifying and categorizing artefacts using low fidelity equipment. This work forms part of a wider project in which we attempt to use those artefacts constructively, when others try to filter them out. The main contribution is to create awareness of the extent to which artefacts can be encountered, identified and categorized using off-the shelf equipment. Our results illustrate that we are able to adequately identify and categorize the most commonly encountered artefacts in a non-clinical environment, using low fidelity equipment.

Paper Nr: 6
Title:

A Hardware/Software Platform to Acquire Bioelectrical Signals. A Case Study: Characterizing Computer Access through Attention

Authors:

Alberto J. Molina, Isabel M. Gómez, Jaime Guerrero, Manuel Merino, Juan A. Castro, Roylán Quesada, Santiago Berrazueta and María Hermoso-de-Mendoza

Abstract: This paper describes a hardware/software platform to acquire human body signals. In the field of physiological computing it is desirable to have a system that allows the synchronized acquisition of signals coming from different sources. Here is described how to unify the whole process of acquiring signals from both customized hardware and low cost commercial devices such as Neurosky’s mindwave. A case study using this platform is also shown: studying the feasibility of using sustained attention to access a computer. In order to do that brain activity was measured using Neurosky’s mindwave. The participants in this study were asked to keep their attention high/low for as long as possible during several trials. Experimentation was performed by 7 normally developed subjects and 3 people with cerebral palsy (CP). Our preliminary work shows that 60% of participants might be potential users of this technology. Eventually, modulating the attention to access a communication board needs a scanning period greater than 5.76s.

Paper Nr: 11
Title:

Using Frontal Brain Asymmetry to Control Sensory Treatment of Anxiety and Depression

Authors:

Tim J. C. Jacob, Jeremy Warden-Smith, Neil Kernot and Malyka Galay Burgos

Abstract: Anxiety and depression are increasingly common disorders. Globally, more than 350 million people of all ages suffer from these illnesses. Depression and anxiety are treated with medication, psychotherapy, or electroconvulsive therapy (ECT), either individually or in combination. Drugs and ECT are not cures and often involve unpalatable adverse side-effects necessitating safer more sustainable alternatives. The antidepressant properties of bright light are well established and aroma stimulation has been shown to improve mood and reduce markers for anxiety and depression. A combinatory therapy of light and smell stimulation has been shown to have a positive impact on mood, physiological markers for stress, anxiety and depression. In particular, negative alphawave brain asymmetry, an objective marker for depression, is reduced by a 15min stimulus treatment. The proposal outlined in this paper is that real-time frontal alpha asymmetry, recorded by EEG, be used to control the frequency, duration and amplitude of the light and aroma signals to optimise the effectiveness of the treatment. The object of this treatment is to rebalance the frontal asymmetry restoring a frontal activity representative of a non-depressed, non-anxious state.

Area 2 - Methodologies and Methods

Full Papers
Paper Nr: 1
Title:

Clinical Risk Groups Analysis for Chronic Hypertensive Patients in Terms of ICD9-CM Diagnosis Codes

Authors:

Javier Fernández-Sánchez, Cristina Soguero-Ruiz, Pablo de Miguel-Bohoyo, Francisco Javier Rivas-Flores, Ángel Gómez-Delgado, Francisco Javier Gutiérrez-Expósito and Inmaculada Mora-Jiménez

Abstract: Hypertension is a chronic condition that has a considerable prevalence in the elderly. Furthermore, hypertensive patients double cost of normotensive individuals. The budget reduction and the increasing concern about the sustainability of the healthcare system have caused that improving the efficiency and use of resources are a priority in developed countries. Identification of chronic hypertensive patients, i.e., patients with high blood pressure, can be performed by means of population classification systems such as Clinical Risk Groups (CRGs). CRGs classify individuals in health status categories taking both demographic and clinical information of the encounters that individuals have with the healthcare system during a defined period of time. In this work, we determine the characteristic profile and the evolution of diagnosis codes according to the International Classification of Diseases 9 revision, Clinical Modification (ICD9-CM), focusing on healthy and chronic hypertensive patients at different chronic statuses (CRG). Our data correspond to the population associated to the University Hospital of Fuenlabrada (Madrid, Spain) during the year 2012, providing about 46000/16000 healthy/hypertensive individuals. We found that profiles associated to different health statuses have different patterns in terms of ICD-9 diagnosis codes. Furthermore, a prediction method is proposed to determine the health status of a new patient according to demographic (age and gender) and clinical (diagnosis codes) data. We conclude that gender is the less informative characteristic, though the combination of age and diagnosis codes have a great potential when they are non linearly combined.

Area 3 - Applications

Short Papers
Paper Nr: 2
Title:

Towards Bidirectional Brain-computer Interfaces that Use fNIRS and tDCS

Authors:

Samuel W. Hincks, Maya DeBellis, Eun Youb Lee, Ronna ten Brink, Birger Moëll and Robert Jacob

Abstract: We envision a future user interface that measures its user’s mental state and responds not only through a display but also by sending output directly to the brain, leading to a primitive bidirectional brain-computer interface. Previous interactive systems have measured brain state with functional near-infrared spectroscopy (fNIRS) for communication from user to computer; we now explore transcranial direct-current stimulation (tDCS) as a channel in the opposite direction. Our goal is to integrate this with brain measurements from fNIRS, so that the stimulation parameters governing tDCS may be set dynamically to enhance user cognition based on current mental state and task demands. To do this, the first step is to determine how long it takes for tDCS to register cognitive effects and how long these effects last. We present an experiment that investigates the temporal dimension of tDCS for this purpose. The findings suggest a long lag-time between the onset of stimulation and any measurable cognitive effect, which may prohibit the effectiveness of tDCS in a brainadaptive application.

Paper Nr: 12
Title:

Emotions Detection based on a Single-electrode EEG Device

Authors:

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

Abstract: The study of emotions using multiple channels of EEG represents a widespread practice in the field of research related to brain computer interfaces (Brain Computer Interfaces). To date, few studies have been reported in the literature with a reduced number of channels, which when used in the detection of emotions present results that are less accurate than the rest. To detect emotions using an EEG channel and the data obtained is useful for classifying emotions with an accuracy comparable to studies in which there is a high number of channels, is of particular interest in this research framework. This article uses the Neurosky Maindwave device; which has a single electrode to acquire the EEG signal, Matlab software and IBM SPSS Modeler; which process and classify the signals respectively. The accuracy obtained in the detection of emotions in relation to the economic resources of the hardware dedicated to the acquisition of EEG signal is remarkable.