P300 wave detection using Emotiv EPOC+ headset: effects of matrix size, flash duration, and colors

Includes bibliographical references.2016 Fall.Brain-computer interfaces (BCIs) allow interactions between human beings and comput- ers without using voluntary muscle. Enormous research effort has been employed in the last few decades to design convenient and user-friendly interfaces. The aim of this study is to provide the people with severe neuromuscular disorders a new augmentative communication technology so that they can express their wishes and communicate with others. The research investigates the capability of Emotiv EPOC+ headset to capture and record one of the BCIs signals called P300 that is used in several applications such as the P300 speller. The P300 speller is a BCI system used to enable severely disabled people to spell words and convey their thoughts without any physical effort. In this thesis, the effects of matrix size, flash duration, and colors were studied. Data are collected from five healthy subjects in their home environments. Different programs are used in this experiment such as OpenViBE platform and MATLAB to pre-process and classify the EEG data. Moreover, the Linear Discriminate Analysis (LDA) classification algorithm is used to classify the data into target and non-target samples

Visual Flow-based Programming Plugin for Brain Computer Interface in Computer-Aided Design

Over the last half century, the main application of Brain Computer Interfaces, BCIs has been controlling wheelchairs and neural prostheses or generating text or commands for people with restricted mobility. There has been very limited attention in the field to applications for computer aided design, despite the potential of BCIs to provide a new form of environmental interaction. In this paper we introduce the development and application of Neuron, a novel BCI tool that enables designers with little experience in neuroscience or computer programming to gain access to neurological data, along with established metrics relevant to design, create BCI interaction prototypes, both with digital onscreen objects and physical devices, and evaluate designs based on neurological information and record measurements for further analysis. After discussing the BCI tool development, the article presents its capabilities through two case studies, along with a brief evaluation of the tool performance and a discussion of implications, limitations, and future improvement

Requirement analysis and sensor specifications – First version

In this first version of the deliverable, we make the following contributions: to design the WEKIT capturing platform and the associated experience capturing API, we use a methodology for system engineering that is relevant for different domains such as: aviation, space, and medical and different professions such as: technicians, astronauts, and medical staff. Furthermore, in the methodology, we explore the system engineering process and how it can be used in the project to support the different work packages and more importantly the different deliverables that will follow the current. Next, we provide a mapping of high level functions or tasks (associated with experience transfer from expert to trainee) to low level functions such as: gaze, voice, video, body posture, hand gestures, bio-signals, fatigue levels, and location of the user in the environment. In addition, we link the low level functions to their associated sensors. Moreover, we provide a brief overview of the state-of-the-art sensors in terms of their technical specifications, possible limitations, standards, and platforms. We outline a set of recommendations pertaining to the sensors that are most relevant for the WEKIT project taking into consideration the environmental, technical and human factors described in other deliverables. We recommend Microsoft Hololens (for Augmented reality glasses), MyndBand and Neurosky chipset (for EEG), Microsoft Kinect and Lumo Lift (for body posture tracking), and Leapmotion, Intel RealSense and Myo armband (for hand gesture tracking). For eye tracking, an existing eye-tracking system can be customised to complement the augmented reality glasses, and built-in microphone of the augmented reality glasses can capture the expert’s voice. We propose a modular approach for the design of the WEKIT experience capturing system, and recommend that the capturing system should have sufficient storage or transmission capabilities. Finally, we highlight common issues associated with the use of different sensors. We consider that the set of recommendations can be useful for the design and integration of the WEKIT capturing platform and the WEKIT experience capturing API to expedite the time required to select the combination of sensors which will be used in the first prototype.WEKI

Wireless Sensors for Brain Activity—A Survey

Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation.</jats:p

Wireless Sensors for Brain Activity — A Survey

Over the last decade, the area of electroencephalography (EEG) witnessed a progressive move from high-end large measurement devices, relying on accurate construction and providing high sensitivity, to miniature hardware, more specifically wireless wearable EEG devices. While accurate, traditional EEG systems need a complex structure and long periods of application time, unwittingly causing discomfort and distress on the users. Given their size and price, aside from their lower sensitivity and narrower spectrum band(s), wearable EEG devices may be used regularly by individuals for continuous collection of user data from non-medical environments. This allows their usage for diverse, nontraditional, non-medical applications, including cognition, BCI, education, and gaming. Given the reduced need for standardization or accuracy, the area remains a rather incipient one, mostly driven by the emergence of new devices that represent the critical link of the innovation chain. In this context, the aim of this study is to provide a holistic assessment of the consumer-grade EEG devices for cognition, BCI, education, and gaming, based on the existing products, the success of their underlying technologies, as benchmarked by the undertaken studies, and their integration with current applications across the four areas. Beyond establishing a reference point, this review also provides the critical and necessary systematic guidance for non-medical EEG research and development efforts at the start of their investigation

Improving EEG-Based Motor Imagery Classification for Real-Time Applications Using the QSA Method

We present an improvement to the quaternion-based signal analysis (QSA) technique to extract electroencephalography (EEG) signal features with a view to developing real-time applications, particularly in motor imagery (IM) cognitive processes. The proposed methodology (iQSA, improved QSA) extracts features such as the average, variance, homogeneity, and contrast of EEG signals related to motor imagery in a more efficient manner (i.e., by reducing the number of samples needed to classify the signal and improving the classification percentage) compared to the original QSA technique. Specifically, we can sample the signal in variable time periods (from 0.5 s to 3 s, in half-a-second intervals) to determine the relationship between the number of samples and their effectiveness in classifying signals. In addition, to strengthen the classification process a number of boosting-technique-based decision trees were implemented. The results show an 82.30% accuracy rate for 0.5 s samples and 73.16% for 3 s samples. This is a significant improvement compared to the original QSA technique that offered results from 33.31% to 40.82% without sampling window and from 33.44% to 41.07% with sampling window, respectively. We can thus conclude that iQSA is better suited to develop real-time applications

Integration of Assistive Technologies into 3D Simulations: Exploratory Studies

Virtual worlds and environments have many purposes, ranging from games to scientific research. However, universal accessibility features in such virtual environments are limited. As the impairment prevalence rate increases yearly, so does the research interests in the field of assistive technologies. This work introduces research in assistive technologies and presents three software developments that explore the integration of assistive technologies within virtual environments, with a strong focus on Brain-Computer Interfaces. An accessible gaming system, a hands-free navigation software system, and a Brain-Computer Interaction plugin have been developed to study the capabilities of accessibility features within virtual 3D environments. Details of the specification, design, and implementation of these software applications are presented in the thesis. Observations and preliminary results as well as directions of future work are also included

Workload-aware systems and interfaces for cognitive augmentation

In today's society, our cognition is constantly influenced by information intake, attention switching, and task interruptions. This increases the difficulty of a given task, adding to the existing workload and leading to compromised cognitive performances. The human body expresses the use of cognitive resources through physiological responses when confronted with a plethora of cognitive workload. This temporarily mobilizes additional resources to deal with the workload at the cost of accelerated mental exhaustion. We predict that recent developments in physiological sensing will increasingly create user interfaces that are aware of the user’s cognitive capacities, hence able to intervene when high or low states of cognitive workload are detected. In this thesis, we initially focus on determining opportune moments for cognitive assistance. Subsequently, we investigate suitable feedback modalities in a user-centric design process which are desirable for cognitive assistance. We present design requirements for how cognitive augmentation can be achieved using interfaces that sense cognitive workload. We then investigate different physiological sensing modalities to enable suitable real-time assessments of cognitive workload. We provide empirical evidence that the human brain is sensitive to fluctuations in cognitive resting states, hence making cognitive effort measurable. Firstly, we show that electroencephalography is a reliable modality to assess the mental workload generated during the user interface operation. Secondly, we use eye tracking to evaluate changes in eye movements and pupil dilation to quantify different workload states. The combination of machine learning and physiological sensing resulted in suitable real-time assessments of cognitive workload. The use of physiological sensing enables us to derive when cognitive augmentation is suitable. Based on our inquiries, we present applications that regulate cognitive workload in home and work settings. We deployed an assistive system in a field study to investigate the validity of our derived design requirements. Finding that workload is mitigated, we investigated how cognitive workload can be visualized to the user. We present an implementation of a biofeedback visualization that helps to improve the understanding of brain activity. A final study shows how cognitive workload measurements can be used to predict the efficiency of information intake through reading interfaces. Here, we conclude with use cases and applications which benefit from cognitive augmentation. This thesis investigates how assistive systems can be designed to implicitly sense and utilize cognitive workload for input and output. To do so, we measure cognitive workload in real-time by collecting behavioral and physiological data from users and analyze this data to support users through assistive systems that adapt their interface according to the currently measured workload. Our overall goal is to extend new and existing context-aware applications by the factor cognitive workload. We envision Workload-Aware Systems and Workload-Aware Interfaces as an extension in the context-aware paradigm. To this end, we conducted eight research inquiries during this thesis to investigate how to design and create workload-aware systems. Finally, we present our vision of future workload-aware systems and workload-aware interfaces. Due to the scarce availability of open physiological data sets, reference implementations, and methods, previous context-aware systems were limited in their ability to utilize cognitive workload for user interaction. Together with the collected data sets, we expect this thesis to pave the way for methodical and technical tools that integrate workload-awareness as a factor for context-aware systems.Tagtäglich werden unsere kognitiven Fähigkeiten durch die Verarbeitung von unzähligen Informationen in Anspruch genommen. Dies kann die Schwierigkeit einer Aufgabe durch mehr oder weniger Arbeitslast beeinflussen. Der menschliche Körper drückt die Nutzung kognitiver Ressourcen durch physiologische Reaktionen aus, wenn dieser mit kognitiver Arbeitsbelastung konfrontiert oder überfordert wird. Dadurch werden weitere Ressourcen mobilisiert, um die Arbeitsbelastung vorübergehend zu bewältigen. Wir prognostizieren, dass die derzeitige Entwicklung physiologischer Messverfahren kognitive Leistungsmessungen stets möglich machen wird, um die kognitive Arbeitslast des Nutzers jederzeit zu messen. Diese sind in der Lage, einzugreifen wenn eine zu hohe oder zu niedrige kognitive Belastung erkannt wird. Wir konzentrieren uns zunächst auf die Erkennung passender Momente für kognitive Unterstützung welche sich der gegenwärtigen kognitiven Arbeitslast bewusst sind. Anschließend untersuchen wir in einem nutzerzentrierten Designprozess geeignete Feedbackmechanismen, die zur kognitiven Assistenz beitragen. Wir präsentieren Designanforderungen, welche zeigen wie Schnittstellen eine kognitive Augmentierung durch die Messung kognitiver Arbeitslast erreichen können. Anschließend untersuchen wir verschiedene physiologische Messmodalitäten, welche Bewertungen der kognitiven Arbeitsbelastung in Realzeit ermöglichen. Zunächst validieren wir empirisch, dass das menschliche Gehirn auf kognitive Arbeitslast reagiert. Es zeigt sich, dass die Ableitung der kognitiven Arbeitsbelastung über Elektroenzephalographie eine geeignete Methode ist, um den kognitiven Anspruch neuartiger Assistenzsysteme zu evaluieren. Anschließend verwenden wir Eye-Tracking, um Veränderungen in den Augenbewegungen und dem Durchmesser der Pupille unter verschiedenen Intensitäten kognitiver Arbeitslast zu bewerten. Das Anwenden von maschinellem Lernen führt zu zuverlässigen Echtzeit-Bewertungen kognitiver Arbeitsbelastung. Auf der Grundlage der bisherigen Forschungsarbeiten stellen wir Anwendungen vor, welche die Kognition im häuslichen und beruflichen Umfeld unterstützen. Die physiologischen Messungen stellen fest, wann eine kognitive Augmentierung sich als günstig erweist. In einer Feldstudie setzen wir ein Assistenzsystem ein, um die erhobenen Designanforderungen zur Reduktion kognitiver Arbeitslast zu validieren. Unsere Ergebnisse zeigen, dass die Arbeitsbelastung durch den Einsatz von Assistenzsystemen reduziert wird. Im Anschluss untersuchen wir, wie kognitive Arbeitsbelastung visualisiert werden kann. Wir stellen eine Implementierung einer Biofeedback-Visualisierung vor, die das Nutzerverständnis zum Verlauf und zur Entstehung von kognitiver Arbeitslast unterstützt. Eine abschließende Studie zeigt, wie Messungen kognitiver Arbeitslast zur Vorhersage der aktuellen Leseeffizienz benutzt werden können. Wir schließen hierbei mit einer Reihe von Applikationen ab, welche sich kognitive Arbeitslast als Eingabe zunutze machen. Die vorliegende wissenschaftliche Arbeit befasst sich mit dem Design von Assistenzsystemen, welche die kognitive Arbeitslast der Nutzer implizit erfasst und diese bei der Durchführung alltäglicher Aufgaben unterstützt. Dabei werden physiologische Daten erfasst, um Rückschlüsse in Realzeit auf die derzeitige kognitive Arbeitsbelastung zu erlauben. Anschließend werden diese Daten analysiert, um dem Nutzer strategisch zu assistieren. Das Ziel dieser Arbeit ist die Erweiterung neuartiger und bestehender kontextbewusster Benutzerschnittstellen um den Faktor kognitive Arbeitslast. Daher werden in dieser Arbeit arbeitslastbewusste Systeme und arbeitslastbewusste Benutzerschnittstellen als eine zusätzliche Dimension innerhalb des Paradigmas kontextbewusster Systeme präsentiert. Wir stellen acht Forschungsstudien vor, um die Designanforderungen und die Implementierung von kognitiv arbeitslastbewussten Systemen zu untersuchen. Schließlich stellen wir unsere Vision von zukünftigen kognitiven arbeitslastbewussten Systemen und Benutzerschnittstellen vor. Durch die knappe Verfügbarkeit öffentlich zugänglicher Datensätze, Referenzimplementierungen, und Methoden, waren Kontextbewusste Systeme in der Auswertung kognitiver Arbeitslast bezüglich der Nutzerinteraktion limitiert. Ergänzt durch die in dieser Arbeit gesammelten Datensätze erwarten wir, dass diese Arbeit den Weg für methodische und technische Werkzeuge ebnet, welche kognitive Arbeitslast als Faktor in das Kontextbewusstsein von Computersystemen integriert

Efficiency evaluation of external environments control using bio-signals

There are many types of bio-signals with various control application prospects. This dissertation regards possible application domain of electroencephalographic signal. The implementation of EEG signals, as a source of information used for control of external devices, became recently a growing concern in the scientific world. Application of electroencephalographic signals in Brain-Computer Interfaces (BCI) (variant of Human-Computer Interfaces (HCI)) as an implement, which enables direct and fast communication between the human brain and an external device, has become recently very popular. Currently available on the market, BCI solutions require complex signal processing methodology, which results in the need of an expensive equipment with high computing power. In this work, a study on using various types of EEG equipment in order to apply the most appropriate one was conducted. The analysis of EEG signals is very complex due to the presence of various internal and external artifacts. The signals are also sensitive to disturbances and non-stochastic, what makes the analysis a complicated task. The research was performed on customised (built by the author of this dissertation) equipment, on professional medical device and on Emotiv EPOC headset. This work concentrated on application of an inexpensive, easy to use, Emotiv EPOC headset as a tool for gaining EEG signals. The project also involved application of embedded system platform - TS-7260. That solution caused limits in choosing an appropriate signal processing method, as embedded platforms characterise with a little efficiency and low computing power. That aspect was the most challenging part of the whole work. Implementation of the embedded platform enables to extend the possible future application of the proposed BCI. It also gives more flexibility, as the platform is able to simulate various environments. The study did not involve the use of traditional statistical or complex signal processing methods. The novelty of the solution relied on implementation of the basic mathematical operations. The efficiency of this method was also presented in this dissertation. Another important aspect of the conducted study is that the research was carried out not only in a laboratory, but also in an environment reflecting real-life conditions. The results proved efficiency and suitability of the implementation of the proposed solution in real-life environments. The further study will focus on improvement of the signal-processing method and application of other bio-signals - in order to extend the possible applicability and ameliorate its effectiveness