Applications of non-invasive brain-computer interfaces for communication and affect recognition

Doctor of PhilosophyDepartment of Electrical and Computer EngineeringDavid E. ThompsonVarious assistive technologies are available for people with communication disorders. While these technologies are quite useful for moderate to severe movement impairments, certain progressive diseases can cause a total locked-in state (TLIS). These conditions include amyotrophic lateral sclerosis (ALS), neuromuscular disease (NMD), and several other disorders that can cause impairment between the neural pathways and the muscles. For people in a locked-in state (LIS), brain-computer interfaces (BCIs) may be the only possible solution. BCIs could help to restore communication to these people, with the help of external devices and neural recordings. The present dissertation investigates the role of latency jitter on BCIs system performance and, at the same time, the possibility of affect recognition using BCIs. BCIs that can recognize human affect are referred to as affective brain-computer interfaces (aBCIs). These aBCIs are a relatively new area of research in affective computing. Estimation of affective states can improve human-computer interaction as well as improve the care of people with severe disabilities. The present work used a publicly available dataset as well as a dataset collected at the Brain and Body Sensing Lab at K-State to assess the effectiveness of EEG recordings in recognizing affective states. This work proposed an extended classifier-based latency estimation (CBLE) method using sparse autoencoders (SAE) to investigate the role of latency jitter on BCI system performance. The recent emergence of autoencoders motivated the present work to develop an SAE based CBLE method. Here, the newly-developed SAE-based CBLE method is applied to a newly-collected dataset. Results from our data showed a significant (p < 0.001) negative correlation between BCI accuracy and estimated latency jitter. Furthermore, the SAE-based CBLE method is also able to predict BCI accuracy. In the aBCI-related investigation, this work explored the effectiveness of different features extracted from EEG to identify the affect of a user who was experiencing affective stimuli. Furthermore, this dissertation reviewed articles that used the Database for Emotion Analysis Using Physiological Signals (DEAP) (i.e., a publicly available affective database) and found that a significant number of studies did not consider the presence of the class imbalance in the dataset. Failing to consider class imbalance creates misleading results. Furthermore, ignoring class imbalance makes comparing results between studies impossible, since different datasets will have different class imbalances. Class imbalance also shifts the chance level. Hence, it is vital to consider class bias while determining if the results are above chance. This dissertation suggests the use of balanced accuracy as a performance metric and its posterior distribution for computing confidence intervals to account for the effect of class imbalance

Ono: an open platform for social robotics

In recent times, the focal point of research in robotics has shifted from industrial ro- bots toward robots that interact with humans in an intuitive and safe manner. This evolution has resulted in the subfield of social robotics, which pertains to robots that function in a human environment and that can communicate with humans in an int- uitive way, e.g. with facial expressions. Social robots have the potential to impact many different aspects of our lives, but one particularly promising application is the use of robots in therapy, such as the treatment of children with autism. Unfortunately, many of the existing social robots are neither suited for practical use in therapy nor for large scale studies, mainly because they are expensive, one-of-a-kind robots that are hard to modify to suit a specific need. We created Ono, a social robotics platform, to tackle these issues. Ono is composed entirely from off-the-shelf components and cheap materials, and can be built at a local FabLab at the fraction of the cost of other robots. Ono is also entirely open source and the modular design further encourages modification and reuse of parts of the platform

Development of Soft Computing Algorithms for the Analysis and Prediction of Motor Task from EEG data

The aim of this research has been to first, acquire a solid understanding of electroencephalogram (EEG) and then contribute scientifically to advance the frontiers of this field. A prerequisite to achieve my defined goals was to understand the fundamentals of the neurophysiological processes that occur within the brain as much as possible. Another area that needed to be researched was the evidence related to movement preparation and planning. Moreover, observing EEG data in practical issues and how it is used to help humans with disability challenges seemed equally important. The objectives of this research are listed below: • To understand the EEG and be able to interpret mental activities with special focus on the time interval associated with movement planning and movement preparation. • Review of the current researches on analysis of EEG recordings prior to a movement or imagination of a movement and their effect on brain computer interfacing. • Design of novel algorithms for extraction and detection of the electric potentials happening before any voluntary movement. • Understanding of on-line analysis of EEG data and, hence, brain computer interfacing in communication, e.i. P300-Speller paradigm

Towards a home-use BCI: fast asynchronous control and robust non-control state detection

Eine Hirn-Computer Schnittstelle (engl. Brain-Computer Interface, BCI) erlaubt einem Nutzer einen Computer nur mittels Gehirn-Aktivität zu steuern. Der Hauptanwendungszweck ist die Wiederherstellung verschiedener Funktionen von motorisch eingeschränkten Menschen, zum Beispiel, die Wiederherstellung der Kommunikationsfähigkeit. Bisherige BCIs die auf visuell evozierten Potentialen (VEPs) basieren, erlauben bereits hohe Kommunikationsgeschwindigkeiten. VEPs sind Reaktionen, die im Gehirn durch visuelle Stimulation hervorgerufen werden. Allerdings werden bisherige BCIs hauptsächlich in der Forschung verwendet und sind nicht für reale Anwendungszwecke geeignet. Grund dafür ist, dass sie auf dem synchronen Steuerungsprinzip beruhen, dies bedeutet, dass Aktionen nur in vorgegebenen Zeitslots ausgeführt werden können. Dies bedeutet wiederum, dass der Nutzer keine Aktionen nach seinem Belieben ausführen kann, was für reale Anwendungszwecke ein Problem darstellt. Um dieses Problem zu lösen, müssen BCIs die Intention des Nutzers, das System zu steuern oder nicht, erkennen. Solche BCIs werden asynchron oder selbstbestimmt genannt. Bisherige asynchrone BCIs zeigen allerdings keine ausreichende Genauigkeit bei der Erkennung der Intention und haben zudem eine deutlich reduzierte Kommunikationsgeschwindigkeit im Vergleich zu synchronen BCIs. In dieser Doktorarbeit wird das erste asynchrone BCI vorgestellt, welches sowohl eine annäherungsweise perfekte Erkennung der Intention des Nutzers als auch eine ähnliche Kommunikationsgeschwindigkeit wie synchrone BCIs erzielt. Dies wurde durch die Entwicklung eines allgemeinen Modells für die Vorhersage von sensorischen Reizen erzielt. Dadurch können beliebige visuelle Stimulationsmuster basierend auf den gemessenen VEPs vorhergesagt werden. Das Modell wurde sowohl mit einem "traditionellen" maschinellen Lernverfahren als auch mit einer deep-learning Methode implementiert und evaluiert. Das resultierende asynchrone BCI übertrifft bisherige Methoden in mehreren Disziplinen um ein Vielfaches und ist ein wesentlicher Schritt, um BCI-Anwendungen aus dem Labor in die Praxis zu bringen. Durch weitere Optimierungen, die in dieser Arbeit diskutiert werden, könnte es sich zum allerersten geeigneten BCI für Endanwender entwickeln, da es effektiv (hohe Genauigkeit), effizient (schnelle Klassifizierungen), und einfach zu bedienen ist. Ein weiteres Alleinstellungsmerkmal ist, dass das entwickelte BCI für beliebige Szenarien verwendet werden kann, da es annähernd unendlich viele gleichzeitige Aktionsfelder erlaubt.Brain-Computer Interfaces (BCIs) enable users to control a computer by using pure brain activity. Their main purpose is to restore several functionalities of motor disabled people, for example, to restore the communication ability. Recent BCIs based on visual evoked potentials (VEPs), which are brain responses to visual stimuli, have shown to achieve high-speed communication. However, BCIs have not really found their way out of the lab yet. This is mainly because all recent high-speed BCIs are based on synchronous control, which means commands can only be executed in time slots controlled by the BCI. Therefore, the user is not able to select a command at his own convenience, which poses a problem in real-world applications. Furthermore, all those BCIs are based on stimulation paradigms which restrict the number of possible commands. To be suitable for real-world applications, a BCI should be asynchronous, or also called self-paced, and must be able to identify the user’s intent to control the system or not. Although there some asynchronous BCI approaches, none of them achieved suitable real-world performances. In this thesis, the first asynchronous high-speed BCI is proposed, which allows using a virtually unlimited number of commands. Furthermore, it achieved a nearly perfect distinction between intentional control (IC) and non-control (NC), which means commands are only executed if the user intends to. This was achieved by a completely different approach, compared to recent methods. Instead of using a classifier trained on specific stimulation patterns, the presented approach is based on a general model that predicts arbitrary stimulation patterns. The approach was evaluated with a "traditional" as well as a deep machine learning method. The resultant asynchronous BCI outperforms recent methods by a multi-fold in multiple disciplines and is an essential step for moving BCI applications out of the lab and into real life. With further optimization, discussed in this thesis, it could evolve to the very first end-user suitable BCI, as it is effective (high accuracy), efficient (fast classifications), ease of use, and allows to perform as many different tasks as desired

Optimised use of independent component analysis for EEG signal processing

Electroencephalography (EEG) is the prevalent technique for monitoring brain function. It employs a set of electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of unwanted signals, which are known as artefacts, usually mix with the EEG at any point during the recording process. As the amplitudes of the EEG and ERPs are very small (in the order of microvolts), they can be buried in the artefacts which have very high amplitudes in the order of millivolts. Therefore, contamination of EEG activity by the artefacts can degrade the quality of the EEG recording and may cause error in EEG/ERP signal interpretation. Several EEG artefact removal methods already exist in the literature and these previous studies have concentrated on manual or automatic detection of either one or, of a few types of EEG artefacts. Among the proposed methods, Independent Component Analysis (ICA) based techniques are commonly applied to successfully detect the artefacts. Different types of ICA algorithms have been developed, which aim to estimate the individual sources of a linearly mixed signal. However, the estimation criterion differs across various ICA algorithms, which may deliver different results