Towards Predicting Control of a Brain-Computer Interface

Individuals suffering from locked-in syndrome are completely paralyzed and unable to speak but otherwise cognitively intact. Traditional assistive technology is ineffective for this population of users due to the physical nature of input devices. Brain-computer and biometric interfaces offer users with severe motor disabilities a non-muscular input channel for communication and control, but require that users be able to harness their appropriate electrophysiological responses for effective use of the interface. There is currently no formalized process for determining a user’s aptitude for control of various biometric interfaces without testing on an actual system. This study presents how basic information captured about users may be used to predict their control of a brain-computer interface that is based on electrical variations in the motor cortex region of the brain. Based on data from 55 able-bodied users, we found that the interaction of age and daily average amount of hand-and-arm movement by individuals correlates to their ability in brain- computer interface control. This research may be expanded into a more robust model linking individual characteristics and control of various biometric interfaces

Reconocimiento de niveles de ansiedad a partir del análisis multimodal y técnicas de aprendizaje de máquina en señales fisiológicas

Aunque en la actualidad, los sistemas de reconocimiento de niveles de ansiedad son ampliamente utilizados en la fase de terapia cognitiva conductual, interfaz hombre computador, técnicas de manejo afectivas, las técnicas de procesamiento desarrolladas aun no alcanzan porcentajes de acierto altos, debido a problemas presentes en las mismas señales, como pueden ser los procesos de filtrado, artefactos, sistemas de caracterización que no determinan a ciencia cierta el contenido emocional presente en dichas señales ó sistemas de clasificación que no realizan en forma óptima su tarea. De igual forma se describe la idea fundamental de cada una de las técnicas con sus pro y sus contra, con el fin de establecer un marco comparativo y de esta forma encontrar una combinación de técnicas que ofrezcan el mínimo de error en el reconocimiento de la emoción presente procedente de una señal fisiológica. Con base en los problemas que presentan los sistemas de reconocimiento de emociones, se requiere de un sistema capaz de realizar esta tarea de manera robusta que aplique técnicas que sean capaces de cuantificar la información presente en las diferentes señales fisiológicas estudiadas. Por tanto se propone el desarrollo de una metodología para el reconocimiento de estados de ansiedad, mediante un análisis multimodal y técnicas de aprendizaje de maquina en señales fisiológicas con el fin de aplicar esta metodología en el tratamiento psicológico para el control de la ansiedad.Although inthe present systems of emotion recognition are widely used in the phase of cognitive behavioral therapy, computer human interface, techniques of emotional management, the techniques developed processing even not reach percentages higher confidence due to problems at the same signals, such as filtering processes, devices, systems characterization not determine with certainty the emotional content present in these signals or classification systems that do not perform optimally in their task. Likewise the fundamental idea of each of the techniques with their pros and cons, in order to establish a comparison frame and thus to find a combination of techniques which offer the least error in recognizing emotion described present from a physiological signal. Based on the problems presented by the emotion recognition systems, requires a system capable of performing this task robustly implement techniques that are able to quantify the information in the different physiological signals studied. Therefore a methodology for the recognition of anxiety states proposed by multimodal analysis techniques and machine learning in physiological signals in order to apply this methodology in psychological treatment for anxiety management

Reconocimiento de niveles de ansiedad a partir del análisis multimodal y técnicas de aprendizaje de máquina en señales fisiológicas

Aunque en la actualidad, los sistemas de reconocimiento de niveles de ansiedad son ampliamente utilizados en la fase de terapia cognitiva conductual, interfaz hombre computador, técnicas de manejo afectivas, las técnicas de procesamiento desarrolladas aun no alcanzan porcentajes de acierto altos, debido a problemas presentes en las mismas señales, como pueden ser los procesos de filtrado, artefactos, sistemas de caracterización que no determinan a ciencia cierta el contenido emocional presente en dichas señales ó sistemas de clasificación que no realizan en forma óptima su tarea. De igual forma se describe la idea fundamental de cada una de las técnicas con sus pro y sus contra, con el fin de establecer un marco comparativo y de esta forma encontrar una combinación de técnicas que ofrezcan el mínimo de error en el reconocimiento de la emoción presente procedente de una señal fisiológica. Con base en los problemas que presentan los sistemas de reconocimiento de emociones, se requiere de un sistema capaz de realizar esta tarea de manera robusta que aplique técnicas que sean capaces de cuantificar la información presente en las diferentes señales fisiológicas estudiadas. Por tanto se propone el desarrollo de una metodología para el reconocimiento de estados de ansiedad, mediante un análisis multimodal y técnicas de aprendizaje de maquina en señales fisiológicas con el fin de aplicar esta metodología en el tratamiento psicológico para el control de la ansiedad.Although inthe present systems of emotion recognition are widely used in the phase of cognitive behavioral therapy, computer human interface, techniques of emotional management, the techniques developed processing even not reach percentages higher confidence due to problems at the same signals, such as filtering processes, devices, systems characterization not determine with certainty the emotional content present in these signals or classification systems that do not perform optimally in their task. Likewise the fundamental idea of each of the techniques with their pros and cons, in order to establish a comparison frame and thus to find a combination of techniques which offer the least error in recognizing emotion described present from a physiological signal. Based on the problems presented by the emotion recognition systems, requires a system capable of performing this task robustly implement techniques that are able to quantify the information in the different physiological signals studied. Therefore a methodology for the recognition of anxiety states proposed by multimodal analysis techniques and machine learning in physiological signals in order to apply this methodology in psychological treatment for anxiety management

Investigation of Cultural Bias Using Physiological Metrics: Applications to International Business

In today\u27s world, many business transactions and interactions are conducted cross-culturally. When going to a business meeting, it is essential avoid a major cultural faux pas in order to not offend your business partners. The Cultural Lens model is used to understand the origins of cultural mismatches. An individual must adjust their approach to a situation to create a cultural match. In adjusting this approach, cognitive biases are a potential result in cross-cultural scenarios. We investigate the Mirror Imaging Bias, which has been found to be a common result of a shortcut to decide how to act in a situation. Physiological metrics were used to see if these biases can be detected in a non-invasive manner. It was found that pupil diameter is a reliable indicator of when Mirror Imaging Bias is present. By understanding how individuals process information and are influenced by Mirror Imaging Bias, we can help create applications as well as provide training to help avoid cultural faux pas

Individual-Technology Fit: Matching Individual Characteristics and Features of Biometric Interface Technologies with Performance

Abstract INDIVIDUAL-TECHNOLOGY FIT: MATCHING INDIVIDUAL CHARACTERISTICS AND FEATURES OF BIOMETRIC INTERFACE TECHNOLOGIES WITH PERFORMANCE By ADRIANE B. RANDOLPH MAY 2007 Committee Chair: Dr. Melody Moore Jackson Major Department: Computer Information Systems The term biometric literally means “to measure the body”, and has recently been associated with physiological measures commonly used for personal verification and security applications. In this work, biometric describes physiological measures that may be used for non-muscularly controlled computer applications, such as brain-computer interfaces. Biometric interface technology is generally targeted for users with severe motor disabilities which may last long-term due to illness or injury or short-term due to temporary environmental conditions. Performance with a biometric interface can vary widely across users depending upon many factors ranging from health to experience. Unfortunately, there is no systematic method for pairing users with biometric interface technologies to achieve the best performance. The current methods to accommodate users through trial-and-error result in the loss of valuable time and resources as users sometimes have diminishing abilities or suffer from terminal illnesses. This dissertation presents a framework and methodology that links user characteristics and features of biometric interface technologies with performance, thus expediting the technology-fit process. The contributions include an outline of the underlying components of capturing and representing individual user characteristics and the impact on the performance of basic interaction tasks using a methodology called biometric user profiling. In addition, this work describes a methodology for objectively measuring an individual’s ability to control a specific biometric interface technology such as one based on measures of galvanic skin response or neural activity. Finally, this work incorporates these concepts into a new individual-technology fit framework for biometric interface technologies stemming from literature on task-technology fit. Key words: user profiles, biometric user profiling, biometric interfaces, fit, individual-technology fit, galvanic skin response, functional near-infrared, brain-computer interfac

Studies on the impact of assistive communication devices on the quality of life of patients with amyotrophic lateral sclerosis

Tese de doutoramento, Ciências Biomédicas (Neurociências), Universidade de Lisboa, Faculdade de Medicina, 2016Amyotrophic Lateral Sclerosis (ALS) is a progressive neuromuscular disease with rapid and generalized degeneration of motor neurons. Patients with ALS experiment a relentless decline in functions that affect performance of most activities of daily living (ADL), such as speaking, eating, walking or writing. For this reason, dependence on caregivers grows as the disease progresses. Management of the respiratory system is one of the main concerns of medical support, since respiratory failure is the most common cause of death in ALS. Due to increasing muscle weakness, most patients experience dramatic decrease of speech intelligibility and difficulties in using upper limbs (UL) for writing. There is growing evidence that mild cognitive impairment is common in ALS, but most patients are self-conscious of their difficulties in communicating and, in very severe stages, locked-in syndrome can occur. When no other resources than speech and writing are used to assist communication, patients are deprived of expressing needs or feelings, making decisions and keeping social relationships. Further, caregivers feel increased dependence due to difficulties in communication with others and get frustrated about difficulties in understanding partners’ needs. Support for communication is then very important to improve quality of life of both patients and caregivers; however, this has been poorly investigated in ALS. Assistive communication devices (ACD) can support patients by providing a diversity of tools for communication, as they progressively lose speech. ALS, in common with other degenerative conditions, introduces an additional challenge for the field of ACD: as the disease progresses, technologies must adapt to different conditions of the user. In early stages, patients may need speech synthesis in a mobile device, if dysarthria is one of the initial symptoms, or keyboard modifications, as weakness in UL increases. When upper limbs’ dysfunction is high, different input technologies may be adapted to capture voluntary control (for example, eye-tracking devices). Despite the enormous advances in the field of Assistive Technologies, in the last decade, difficulties in clinical support for the use of assistive communication devices (ACD) persist. Among the main reasons for these difficulties are lack of assessment tools to evaluate communication needs and determine proper input devices and to indicate changes over disease progression, and absence of clinical evidence that ACD has relevant impact on the quality of life of affected patients. For this set of reasons, support with communication tools is delayed to stages where patients are severely disabled. Often in these stages, patients face additional clinical complications and increased dependence on their caregivers’ decisions, which increase the difficulty in adaptation to new communication tools. This thesis addresses the role of assistive technologies in the quality of life of early-affected patients with ALS. Also, it includes the study of assessment tools that can improve longitudinal evaluation of communication needs of patients with ALS. We longitudinally evaluated a group of 30 patients with bulbar-onset ALS and 17 caregivers, during 2 to 29 months. Patients were assessed during their regular clinical appointments, in the Hospital de Santa Maria-Centro Hospitalar Lisboa_Norte. Evaluation of patients was based on validated instruments for assessing the Quality of Life (QoL) of patients and caregivers, and on methodologies for recording communication and measuring its performance (including speech, handwriting and typing). We tested the impact of early support with ACD on the QoL of patients with ALS, using a randomized, prospective, longitudinal design. Patients were able to learn and improve their skills to use communication tools based on electronic assistive devices. We found a positive impact of ACD in psychological and wellbeing domains of quality of life in patients, as well as in the support and psychological domains in caregivers. We also studied performance of communication (words per minute) using UL. Performance in handwriting may decline faster than performance in typing, supporting the idea that the use of touchscreen-based ACD supports communication for longer than handwriting. From longitudinal recordings of speech and typing activity we could observe that ACD can support tools to detect early markers of bulbar and UL dysfunction in ALS. Methodologies that were used in this research for recording and assessing function in communication can be replicated in the home environment and form part of the original contributions of this research. Implementation of remote monitoring tools in daily use of ACD, based on these methodologies, is discussed. Considering those patients who receive late support for the use of ACD, lack of time or daily support to learn how to control complex input devices may hinder its use. We developed a novel device to explore the detection and control of various residual movements, based on sensors of accelerometry, electromyography and force, as input signals for communication. The aim of this input device was to develop a tool to explore new communication channels in patients with generalized muscle weakness. This research contributed with novel tools from the Engineering field to the study of assistive communication in patients with ALS. Methodologies that were developed in this work can be further applied to the study of the impact of ACD in other neurodegenerative diseases that affect speech and motor control of UL

Affect-based information retrieval

One of the main challenges Information Retrieval (IR) systems face nowadays originates from the semantic gap problem: the semantic difference between a user’s query representation and the internal representation of an information item in a collection. The gap is further widened when the user is driven by an ill-defined information need, often the result of an anomaly in his/her current state of knowledge. The formulated search queries, which are submitted to the retrieval systems to locate relevant items, produce poor results that do not address the users’ information needs. To deal with information need uncertainty IR systems have employed in the past a range of feedback techniques, which vary from explicit to implicit. The first category of feedback techniques necessitates the communication of explicit relevance judgments, in return for better query reformulations and recommendations of relevant results. However, the latter happens at the expense of users’ cognitive resources and, furthermore, introduces an additional layer of complexity to the search process. On the other hand, implicit feedback techniques make inferences on what is relevant based on observations of user search behaviour. By doing so, they disengage users from the cognitive burden of document rating and relevance assessments. However, both categories of RF techniques determine topical relevance with respect to the cognitive and situational levels of interaction, failing to acknowledge the importance of emotions in cognition and decision making. In this thesis I investigate the role of emotions in the information seeking process and develop affective feedback techniques for interactive IR. This novel feedback framework aims to aid the search process and facilitate a more natural and meaningful interaction. I develop affective models that determine topical relevance based on information gathered from various sensory channels, and enhance their performance using personalisation techniques. Furthermore, I present an operational video retrieval system that employs affective feedback to enrich user profiles and offers meaningful recommendations of unseen videos. The use of affective feedback as a surrogate for the information need is formalised as the Affective Model of Browsing. This is a cognitive model that motivates the use of evidence extracted from the psycho-somatic mobilisation that occurs during cognitive appraisal. Finally, I address some of the ethical and privacy issues that arise from the social-emotional interaction between users and computer systems. This study involves questionnaire data gathered over three user studies, from 74 participants of different educational background, ethnicity and search experience. The results show that affective feedback is a promising area of research and it can improve many aspects of the information seeking process, such as indexing, ranking and recommendation. Eventually, it may be that relevance inferences obtained from affective models will provide a more robust and personalised form of feedback, which will allow us to deal more effectively with issues such as the semantic gap

An Investigation into the Effects of Using Dynamic Representation to Reflect Users’ Emotional States During Physical Activity

Many people struggle to maintain exercise routines (Biddle and Fox, 1989). Affective state (emotion, mood and sentiment) plays a key role in supporting or undermining intentions to exercise (Hanin, 2000). Presenting users with representations of their emotions can result in positive adjustments to their behavior (Brave and Nass, 2002). This project aimed to evaluate the impact of running with such a representation on a user’s positivity during a run, and upon its completion.A mobile application, EmotiRun, was iteratively designed to capture user feelings whilst running via self-reporting functionality; using this to dynamically represent inferences made of the positivity of the positivity of a user’s emotional state.A user study with EmotiRun didn’t reveal significant differences in positivity with an ‘emotional’ display present. Participants did however note their awareness of the dynamic representation changing, and its potential as a motivational cue. The design and evaluation context in the physical setting of running are considered in relation to the fields of Affective Computing, and more broadly Human Computer Interaction. Further work is proposed to build upon the study method and design approach, of in-situ real-time self-reporting for physical activities

Recent Application in Biometrics

In the recent years, a number of recognition and authentication systems based on biometric measurements have been proposed. Algorithms and sensors have been developed to acquire and process many different biometric traits. Moreover, the biometric technology is being used in novel ways, with potential commercial and practical implications to our daily activities. The key objective of the book is to provide a collection of comprehensive references on some recent theoretical development as well as novel applications in biometrics. The topics covered in this book reflect well both aspects of development. They include biometric sample quality, privacy preserving and cancellable biometrics, contactless biometrics, novel and unconventional biometrics, and the technical challenges in implementing the technology in portable devices. The book consists of 15 chapters. It is divided into four sections, namely, biometric applications on mobile platforms, cancelable biometrics, biometric encryption, and other applications. The book was reviewed by editors Dr. Jucheng Yang and Dr. Norman Poh. We deeply appreciate the efforts of our guest editors: Dr. Girija Chetty, Dr. Loris Nanni, Dr. Jianjiang Feng, Dr. Dongsun Park and Dr. Sook Yoon, as well as a number of anonymous reviewers