Framework for Electroencephalography-based Evaluation of User Experience

Measuring brain activity with electroencephalography (EEG) is mature enough to assess mental states. Combined with existing methods, such tool can be used to strengthen the understanding of user experience. We contribute a set of methods to estimate continuously the user's mental workload, attention and recognition of interaction errors during different interaction tasks. We validate these measures on a controlled virtual environment and show how they can be used to compare different interaction techniques or devices, by comparing here a keyboard and a touch-based interface. Thanks to such a framework, EEG becomes a promising method to improve the overall usability of complex computer systems.Comment: in ACM. CHI '16 - SIGCHI Conference on Human Factors in Computing System, May 2016, San Jose, United State

Adaptive Cognitive Interaction Systems

Adaptive kognitive Interaktionssysteme beobachten und modellieren den Zustand ihres Benutzers und passen das Systemverhalten entsprechend an. Ein solches System besteht aus drei Komponenten: Dem empirischen kognitiven Modell, dem komputationalen kognitiven Modell und dem adaptiven Interaktionsmanager. Die vorliegende Arbeit enthält zahlreiche Beiträge zur Entwicklung dieser Komponenten sowie zu deren Kombination. Die Ergebnisse werden in zahlreichen Benutzerstudien validiert

Deep learning for healthcare applications based on physiological signals: A review

Background and objective: We have cast the net into the ocean of knowledge to retrieve the latest scientific research on deep learning methods for physiological signals. We found 53 research papers on this topic, published from 01.01.2008 to 31.12.2017. Methods: An initial bibliometric analysis shows that the reviewed papers focused on Electromyogram(EMG), Electroencephalogram(EEG), Electrocardiogram(ECG), and Electrooculogram(EOG). These four categories were used to structure the subsequent content review. Results: During the content review, we understood that deep learning performs better for big and varied datasets than classic analysis and machine classification methods. Deep learning algorithms try to develop the model by using all the available input. Conclusions: This review paper depicts the application of various deep learning algorithms used till recently, but in future it will be used for more healthcare areas to improve the quality of diagnosi

Effects of Interpretation Error on User Learning in Novel Input Mechanisms

Novel input mechanisms generate signals that are interpreted as commands in computer systems. Sometimes noise from various sources can cause the system to produce errors when attempting to interpret the signal, causing a misrepresentation of the user's intention. While research has been done in understanding how these interpretation errors affect the performance of users of novel signal-based input mechanisms, such as a brain-computer interface (BCI), there is a lack of knowledge in how user learning is affected. Previous literature in command-based selection tasks has suggested that errors will have a negative impact on expertise development; however, the presence of errors could conversely improve a user's learning by demanding more attention from the user. This thesis begins by studying people's ability to use a novel input mechanism with a noisy input signal: a motor imagery BCI. By converting a user's brain signals into computer commands, a user could complete selection tasks using imagined movement. However, the high degree of interpretation errors caused by noise in the input signals made it difficult to differentiate the user's intent from the noise. As such, the results of the BCI study served as motivation to test the effects of interpretation errors on user learning. Two studies were conducted to determine how user performance and learning were affected by different rates of interpretation error in a novel input mechanism. The results from these two studies showed that interpretation errors led to slower task completion times, lower accuracy in memory recall, greater rates of user errors, and increased frustration. This new knowledge about the effects of interpretation errors can contribute to better design of input mechanisms and training programs for novel input systems

Advanced Augmentative and Alternative Communication System Based in Physiological Control

Dyskinetic Cerebral Palsy (DCP) is mainly characterized by alterations in muscle tone and involuntary movements. Therefore, these people present with difficulties in coordination and movement control, which makes walking difficult and affects their posture when seated. Additionally, their cognitive performance varies between being completely normal and severe mental retardation. People with DCP were selected as the objective of this thesis due to their multiple and complex limitations (speech problems and motor control) and because their capabilities have a great margin for improvement thanks to physiological control systems. Given their communication difficulties, some people with DCP have good motor con-trol and can communicate with written language. However, most have difficulty using Augmentative and Alternative Communication (AAC) systems. People with DCP gen-erally use concept boards to indicate the idea they want to communicate. However, most communication solutions available today are based on proprietary software that makes it difficult to customize the concept board and this type of control system. This is the motivation behind this thesis, with the aim of creating an interface with characteristics, able to be adapted to the user needs and limitations. Thus, this thesis proposes an Augmentative and Alternative Communication System for people with DCP based on physiological control. In addition, an innovative system for direct con-trol of concept boards with EMG is proposed. This control system is based on a physi-cal model that reproduces the muscular mechanical response (stiffness, inertia and viscosity). It allows for a selection of elements thanks to small pulses of EMG signal with sensors on a muscle with motor control. Its main advantage is the possibility of correcting errors during selection associated with uncontrolled muscle impulses, avoid-ing sustained muscle effort and thus reduced fatigue.La Parálisis Cerebral de tipo Discinésica (DCP) se caracteriza principalmente por las alteraciones del tono muscular y los movimientos involuntarios. Por ello, estos pacientes presentan dificultades en la coordinación y en el control de movimientos, lo cual les dificulta el caminar y afecta su postura cuando están sentados. Cabe resaltar que la capacidad cognitiva de las personas con DCP puede variar desde completamente normal, hasta un retraso mental severo. Las personas con DCP han sido seleccionadas como objetivo de esta tesis ya el margen de mejora de sus capacidades es amplio gracias a sistemas de control fisiológico, debido a sus múltiples y complejas limitaciones (problemas de habla y control motor). Debido a sus dificultades de comunicación, algunas personas con DCP se pueden comunicar con lenguaje escrito, siempre y cuando tenga un buen control motor. Sin embargo, la mayoría tienen dificultades para usar sistemas de Comunicación Aumentativos y Alternativos (AAC). De hecho, las personas con DCP utilizan generalmente tableros de conceptos para indicar la idea que quieren transmitir. Sin embargo, la mayoría las soluciones de comunicación disponibles en la actualidad están basadas en software propietario que hacen difícil la personalización del tablero de conceptos y el tipo de sistema de control. Es aquí donde surge esta tesis, con el objetivo de crear una interfaz con esas características, capaz de adaptarse a las necesidades y limitaciones del usuario. De esta forma, esta tesis propone un sistema de comunicación aumentativo y alternativo para personas con DCP basado en control fisiológico. Además, se propone un Sistema innovador de control directo sobre tableros de conceptos basado en EMG. Este Sistema de control se basa en un modelo físico que reproduce la respuesta mecánica muscular (basado en parámetros como Rigidez, Inercia y Viscosidad), permitiendo la selección de elementos gracias a pequeños pulsos de señal EMG con sensores sobre un músculo con control motor. Sus principales ventajas son la posibilidad de corregir errores durante la selección asociado a los impulsos musculares no controlados, evitar el esfuerzo muscular mantenido para alcanzar un nivel y reducir la fatiga.La Paràlisi Cerebral de tipus Discinèsica (DCP) es caracteritza principalment per les alteracions del to muscular i els moviments involuntaris. Per açò, aquests pacients presenten dificultats en la coordinació i en el control de moviments, la qual cosa els dificulta el caminar i afecta la seua postura quan estan asseguts. Cal ressaltar que la capacitat cognitiva de les persones amb DCP pot variar des de completament normal, fins a un retard mental sever. Les persones amb DCP han sigut seleccionades com a objectiu d'aquesta tesi ja el marge de millora de les seues capacitats és ampli gràcies a sistemes de control fisiològic, a causa dels seus múltiples i complexes limitacions (problemes de parla i control motor). A causa de les seues dificultats de comunicació, algunes persones amb DCP es poden comunicar amb llenguatge escrit, sempre que tinga un bon control motor. No obstant açò, la majoria tenen dificultats per a usar sistemes de Comunicació Augmentatius i Alternatius (AAC). De fet, les persones amb DCP utilitzen generalment taulers de conceptes per a indicar la idea que volen transmetre. No obstant açò, la majoria les solucions de comunicació disponibles en l'actualitat estan basades en programari propietari que fan difícil la personalització del tauler de conceptes i el tipus de sistema de control. És ací on sorgeix aquesta tesi, amb l'objectiu de crear una interfície amb aqueixes característiques, capaç d'adaptar-se a les necessitats i limitacions de l'usuari. D'aquesta forma, aquesta tesi proposa un sistema de comunicació augmentatiu i alternatiu per a persones amb DCP basat en control fisiològic. A més, es proposa un sistema innovador de control directe sobre taulers de conceptes basat en EMG. Aquest sistema de control es basa en un model físic que reprodueix la resposta mecànica muscular (basat en paràmetres com a Rigidesa, Inèrcia i Viscositat), permetent la selecció d'elements gràcies a xicotets polsos de senyal EMG amb sensors sobre un múscul amb control motor. Els seus principals avantatges són la possibilitat de corregir errors durant la selecció associat als impulsos musculars no controlats, evitar l'esforç muscular mantingut per a aconseguir un nivell i reduir la fatiga.Díaz Pineda, JA. (2017). Advanced Augmentative and Alternative Communication System Based in Physiological Control [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/90418TESI