Non-Linear Adapted Spatio-Temporal Filter for Single-Trial Identification of Movement-Related Cortical Potential

The execution or imagination of a movement is reflected by a cortical potential that can be recorded by electroencephalography (EEG) as Movement-Related Cortical Potentials (MRCPs). The identification of MRCP from a single trial is a challenging possibility to get a natural control of a Brain–Computer Interface (BCI). We propose a novel method for MRCP detection based on optimal non-linear filters, processing different channels of EEG including delayed samples (getting a spatio-temporal filter). Different outputs can be obtained by changing the order of the temporal filter and of the non-linear processing of the input data. The classification performances of these filters are assessed by cross-validation on a training set, selecting the best ones (adapted to the user) and performing a majority voting from the best three to get an output using test data. The method is compared to another state-of-the-art filter recently introduced by our group when applied to EEG data recorded from 16 healthy subjects either executing or imagining 50 self-paced upper-limb palmar grasps. The new approach has a median accuracy on the overall dataset of 80%, which is significantly better than that of the previous filter (i.e., 63%). It is feasible for online BCI system design with asynchronous, self-paced applications

Targeting the Brain in Brain-Computer Interfacing: The Effect of Transcranial Current Stimulation and Control of a Physical Effector on Performance and Electrophysiology Underlying Noninvasive Brain-Computer Interfaces

University of Minnesota Ph.D. dissertation. July 2017. Major: Biomedical Engineering. Advisor: Bin He. 1 computer file (PDF); vii, 123 pages.Brain-computer interfaces (BCIs) and neuromodulation technologies have recently begun to fulfill their promises of restoring function, improving rehabilitation, and enhancing abilities and learning. However, lengthy user training to achieve acceptable accuracy is a barrier to BCI acceptance and use by patients and the general population. Transcranial direct current stimulation (tDCS) is a noninvasive neuromodulation technology whereby a low level of electrical current is injected into the brain to alter neural activity and has been found to improve motor learning and task performance. A barrier to optimizing behavioral effects of tDCS is that we do not yet understand how neural networks are affected by stimulation and how stimulation interacts with ongoing endogenous activity. The purpose of this dissertation was to elucidate strategies to improve BCI control by targeting the user through two approaches: 1. Subject control of a robotic arm to enhance user motivation and 2. tDCS application to improve behavioral outcomes and alter networks underlying sensorimotor rhythm-based BCI performance. The primary results illustrate that targeted tDCS of the motor network interacts with task specific neural activity to improve BCI performance and alter neural electrophysiology. This effect on neural activity extended across the task network, beyond the area of direct stimulation, and altered connectivity unilaterally and bilaterally between frontal and parietal cortical regions. These findings suggest targeted neuromodulation interacts with endogenous neural activity and can be used to improve motor-cognitive task performance

Investigating the Prerequisites for a robust Neurotutor: The Detection of mixed User States containing Working Memory Load, Affective Valence and Affective Dominance

Intelligent tutoring systems are software environments that aim to simulate a human tutor. While current systems show effectiveness comparable to human tutors, they still suffer from the ‘assistance dilemma’. This drawback refers to the inability to infer the ongoing user state which can lead to situations where the system provides no or inadequate support. To alleviate this situation, user state detection has been implemented in some systems. However, at the current time, only behavioral indicators are used to infer the ongoing user state. Such overt behaviors are not specific enough to provide a detailed representation of the user state. This is the reason why I suggest to investigate the potential use of the electroencephalogram to infer the ongoing user state. This combination of an intelligent tutoring system and an EEG-based user state detection is called a neurotutor. EEG-based user state detection usually focusses on narrow user states which can be detected in controlled lab environments. I assume that real-life environments like a classroom evoke complex user states which consist of multiple different components. I therefore propose a three component framework that enables the tracking of different processes that are active during a complex user state. The first two studies focus on the separation of working memory load and affective valence in a highly controlled setting with the use of established measures from classical neuroscience. I found that measures used to infer working memory load can be used to track changes in working memory load under different affective valence. Furthermore, I found that said measures were also sensitive to changes in affective valence. Surprisingly, I found that measures used to infer affective valence were not sensitive to changes in affective valence under working memory load. Additional analyses revealed that working memory load and affective valence can be automatically detected with accuracies sufficient for the use in a neurotutor. The third study successfully replicated the findings from the first two studies in a more realistic, although less controlled setting. A simplified learning game was used to induce the complex user state of perceived loss of control that simultaneously evoked cognitive as well as affective processes. With the help of the framework I was able to integrate the findings from three different studies that all analyzed the same dataset. This would not have been possible without an adequate theoretical framework.Intelligente Tutorensysteme sind EDV-Programme, welche versuchen einen menschlichen Tutor zu simulieren. Obwohl derzeit erhältliche Systeme ähnlich effektiv sind wie menschliche Tutoren, leiden sie immer noch unter dem ‚Assistenzdilemma‘. Dies referenziert auf die Unfähigkeit den aktuellen Nutzerzustand zu erkennen, was dazu führen kann, dass das System keine oder inadäquate Unterstützung anbietet. Um diesen Umstand zu beseitigen wurde in manchen Systemen bereits eine Nutzerzustandserkennung implementiert. Derzeit werden jedoch nur beobachtbare Verhaltensweisen verwendet, um den aktuellen Nutzerzustand abzuleiten. Solch offenkundige Verhaltensweisen sind jedoch nicht spezifisch genug, um ein detailliertes Bild des Nutzers zu erfassen. Deshalb schlage ich vor, dass man das Elektroenzephalogramm verwenden soll, um den aktuellen Nutzerzustand zu erkennen. Diese Kombination eines intelligenten Tutorensystems mit einer EEG-basierten Zustandserkennung nennt man einen Neurotutor. Normalerweise fokussiert sich EEG-basierte Zustandserkennung jedoch nur auf begrenzte Zustände, welche innerhalb kontrollierter Laborumgebungen erkannt werden. Ich nehme an, dass realistische Settings wie ein Klassenzimmer komplexe Zustände hervorrufen, welche aus mehreren verschiedenen Komponenten bestehen. Daher stelle ich ein Bezugssystem mit drei Komponenten auf, welcher die Zuordnung der verschiedenen Prozesse, die während eines komplexen Nutzerzustandes aktiv sind, ermöglicht. Die ersten beiden Studien befassen sich mit der Separation von Arbeitsgedächtnisbelastungen und affektiver Valenz durch die Verwendung von etablierten EEG Maßen in einem hoch kontrollierten Setting. Dabei habe ich herausgefunden, dass Maße, welche zum Erkennen von Arbeitsgedächtnisbelastungen verwendet werden, auch geeignet sind, um Veränderungen in der Belastung des Arbeitsgedächtnisses unter gleichzeitiger emotioneller Stimulation zu erkennen. Außerdem habe ich entdeckt, dass die erwähnten Maße auch sensitiv gegenüber Veränderungen in der affektiven Valenz sind. Überraschender Weise hat sich herausgestellt, dass Maße, welche zum Erkennen von affektiver Valenz verwendet werden, nicht sensitiv gegenüber Veränderungen in der affektiven Valenz sind, wenn gleichzeitig das Arbeitsgedächtnis belastet wird. Zusätzliche Analysen haben aufgedeckt, dass Arbeitsgedächtnisbelastungen und affektive Valenz mit einer Genauigkeit erkannt werden kann, welche für den Gebrauch in einem Neurotutor ausreichend sind. In der dritten Studie konnten die Ergebnisse der ersten beiden Studien erfolgreich in einem relevanteren, aber weniger kontrollierten Kontext repliziert werden. Dabei wurde ein vereinfachtes Lernspiel verwendet, um einen wahrgenommenen Kontrollverlust zu induzieren. Durch Verwendung des Bezugsrahmens war es mir möglich die Ergebnisse von drei verschiedenen Studien zu integrieren, welche alle denselben Datensatz analysierten. Dies wäre nicht ohne adäquaten theoretischen Rahmen möglich gewesen

Blind source separation via independent and sparse component analysis with application to temporomandibular disorder

Blind source separation (BSS) addresses the problem of separating multi channel signals observed by generally spatially separated sensors into their constituent underlying sources. The passage of these sources through an unknown mixing medium results in these observed multichannel signals. This study focuses on BSS, with special emphasis on its application to the temporomandibular joint disorder (TMD). TMD refers to all medical problems related to the temporomandibular joint (TMJ), which holds the lower jaw (mandible) and the temporal bone (skull). The overall objective of the work is to extract the two TMJ sound sources generated by the two TMJs, from the bilateral recordings obtained from the auditory canals, so as to aid the clinician in diagnosis and planning treatment policies. Firstly, the concept of 'variable tap length' is adopted in convolutive blind source separation. This relatively new concept has attracted attention in the field of adaptive signal processing, notably the least mean square (LMS) algorithm, but has not yet been introduced in the context of blind signal separation. The flexibility of the tap length of the proposed approach allows for the optimum tap length to be found, thereby mitigating computational complexity or catering for fractional delays arising in source separation. Secondly, a novel fixed point BSS algorithm based on Ferrante's affine transformation is proposed. Ferrante's affine transformation provides the freedom to select the eigenvalues of the Jacobian matrix of the fixed point function and thereby improves the convergence properties of the fixed point iteration. Simulation studies demonstrate the improved convergence of the proposed approach compared to the well-known fixed point FastICA algorithm. Thirdly, the underdetermined blind source separation problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter achieves source recovery by employing the i-norm algorithm. Additionally, it will be shown that FastICA can also be utilised to extract the sources. Furthermore, it is illustrated how this scenario is particularly suitable for the separation of TMJ sounds. Finally, estimation of fractional delays between the mixtures of the TMJ sources is proposed as a means for TMJ separation. The estimation of fractional delays is shown to simplify the source separation to a case of in stantaneous BSS. Then, the estimated delay allows for an alignment of the TMJ mixtures, thereby overcoming a spacing constraint imposed by a well- known BSS technique, notably the DUET algorithm. The delay found from the TMJ bilateral recordings corroborates with the range reported in the literature. Furthermore, TMJ source localisation is also addressed as an aid to the dental specialist.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Blind source separation via independent and sparse component analysis with application to temporomandibular disorder

Blind source separation (BSS) addresses the problem of separating multi channel signals observed by generally spatially separated sensors into their constituent underlying sources. The passage of these sources through an unknown mixing medium results in these observed multichannel signals. This study focuses on BSS, with special emphasis on its application to the temporomandibular joint disorder (TMD). TMD refers to all medical problems related to the temporomandibular joint (TMJ), which holds the lower jaw (mandible) and the temporal bone (skull). The overall objective of the work is to extract the two TMJ sound sources generated by the two TMJs, from the bilateral recordings obtained from the auditory canals, so as to aid the clinician in diagnosis and planning treatment policies. Firstly, the concept of 'variable tap length' is adopted in convolutive blind source separation. This relatively new concept has attracted attention in the field of adaptive signal processing, notably the least mean square (LMS) algorithm, but has not yet been introduced in the context of blind signal separation. The flexibility of the tap length of the proposed approach allows for the optimum tap length to be found, thereby mitigating computational complexity or catering for fractional delays arising in source separation. Secondly, a novel fixed point BSS algorithm based on Ferrante's affine transformation is proposed. Ferrante's affine transformation provides the freedom to select the eigenvalues of the Jacobian matrix of the fixed point function and thereby improves the convergence properties of the fixed point iteration. Simulation studies demonstrate the improved convergence of the proposed approach compared to the well-known fixed point FastICA algorithm. Thirdly, the underdetermined blind source separation problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter achieves source recovery by employing the i-norm algorithm. Additionally, it will be shown that FastICA can also be utilised to extract the sources. Furthermore, it is illustrated how this scenario is particularly suitable for the separation of TMJ sounds. Finally, estimation of fractional delays between the mixtures of the TMJ sources is proposed as a means for TMJ separation. The estimation of fractional delays is shown to simplify the source separation to a case of in stantaneous BSS. Then, the estimated delay allows for an alignment of the TMJ mixtures, thereby overcoming a spacing constraint imposed by a well- known BSS technique, notably the DUET algorithm. The delay found from the TMJ bilateral recordings corroborates with the range reported in the literature. Furthermore, TMJ source localisation is also addressed as an aid to the dental specialist.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Blind source separation via independent and sparse component analysis with application to temporomandibular disorder

Blind source separation (BSS) addresses the problem of separating multi channel signals observed by generally spatially separated sensors into their constituent underlying sources. The passage of these sources through an unknown mixing medium results in these observed multichannel signals. This study focuses on BSS, with special emphasis on its application to the temporomandibular joint disorder (TMD). TMD refers to all medical problems related to the temporomandibular joint (TMJ), which holds the lower jaw (mandible) and the temporal bone (skull). The overall objective of the work is to extract the two TMJ sound sources generated by the two TMJs, from the bilateral recordings obtained from the auditory canals, so as to aid the clinician in diagnosis and planning treatment policies. Firstly, the concept of 'variable tap length' is adopted in convolutive blind source separation. This relatively new concept has attracted attention in the field of adaptive signal processing, notably the least mean square (LMS) algorithm, but has not yet been introduced in the context of blind signal separation. The flexibility of the tap length of the proposed approach allows for the optimum tap length to be found, thereby mitigating computational complexity or catering for fractional delays arising in source separation. Secondly, a novel fixed point BSS algorithm based on Ferrante's affine transformation is proposed. Ferrante's affine transformation provides the freedom to select the eigenvalues of the Jacobian matrix of the fixed point function and thereby improves the convergence properties of the fixed point iteration. Simulation studies demonstrate the improved convergence of the proposed approach compared to the well-known fixed point FastICA algorithm. Thirdly, the underdetermined blind source separation problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter achieves source recovery by employing the i-norm algorithm. Additionally, it will be shown that FastICA can also be utilised to extract the sources. Furthermore, it is illustrated how this scenario is particularly suitable for the separation of TMJ sounds. Finally, estimation of fractional delays between the mixtures of the TMJ sources is proposed as a means for TMJ separation. The estimation of fractional delays is shown to simplify the source separation to a case of in stantaneous BSS. Then, the estimated delay allows for an alignment of the TMJ mixtures, thereby overcoming a spacing constraint imposed by a well- known BSS technique, notably the DUET algorithm. The delay found from the TMJ bilateral recordings corroborates with the range reported in the literature. Furthermore, TMJ source localisation is also addressed as an aid to the dental specialist

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