Multichannel EEG : towards applications in clinical neurology.

Electroencephalogram (EEG) measures the electric activity produced by the brain with electrodes placed on the scalp. It is used for monitoring or as diagnostic tool for neurological disorders. In practice a maximum of 21 electrodes are generally used for a clinical EEG recording. However, EEG systems with 128 and 256 electrodes are also available and used for fundamental research. In this thesis we investigate whether the extra information obtained with 128-channel recordings is clinically relevant. We have focused on evoked potentials (EPs). EP is the electric activity of the brain caused by a stimulus (e.g. a flashlight). We showed that a measure often used for evoked potentials, the peak amplitude, can be estimated more accurately by using 128 channels recordings than by conventional recordings. Therefore this technique might be more sensitive to pathological changes. In addition, we developed a new technique to estimate EP symmetry (similarity of EPs generated in left and right hemisphere). This technique might be useful for diagnosis of neurological disorders with brain damage in one hemisphere. Both methods have been applied to a group of patients with parkinsonism; neurological symptoms typical for Parkinson’s disease. No differences could be observed in amplitude or symmetry between patients with different parkinsonian disorders. Therefore, (so far) these methods cannot be used as diagnostic tool for neurological disorders. Future research will show whether small adaptations to the stimulation method or analysis technique will result in an improvement of the diagnostic value and whether these methods are useful for other neurological disorders.

The potential of error-related potentials. Analysis and decoding for control, neuro-rehabilitation and motor substitution

Las interfaces cerebro-máquina (BMIs, por sus siglas en inglés) permiten la decodificación de patrones de activación neuronal del cerebro de los usuarios para proporcionar a personas con movilidad severamente limitada, ya sea debido a un accidente o a una enfermedad neurodegenerativa, una forma de establecer una conexión directa entre su cerebro y un dispositivo. En este sentido, las BMIs basadas en técnicas no invasivas, como el electroencefalograma (EEG) han ofrecido a estos usuarios nuevas oportunidades para recuperar el control sobre las actividades de su vida diaria que de otro modo no podrían realizar, especialmente en las áreas de comunicación y control de su entorno.En los últimos años, la tecnología está avanzando a grandes pasos y con ella la complejidad de dispositivos ha incrementado significativamente, ampliando el número de posibilidades para controlar sofisticados dispositivos robóticos, prótesis con numerosos grados de libertad o incluso para la aplicación de complejos patrones de estimulación eléctrica en las propias extremidades paralizadas de un usuario, que le permitan ejecutar movimientos precisos. Sin embargo, la cantidad de información que se puede transmitir entre el cerebro y estos dispositivos sigue siendo muy limitada, tanto por el número como por la velocidad a la que se pueden decodificar los comandos neuronales. Por lo tanto, depender únicamente de las señales neuronales no garantiza un control óptimo y preciso.Para poder sacar el máximo partido de estas tecnologías, el campo de las BMIs adoptó el conocido enfoque de “control-compartido". Esta estrategia de control pretende crear un sistema de cooperación entre el usuario y un dispositivo inteligente, liberando al usuario de las tareas más pesadas requeridas para ejecutar la tarea sin llegar a perder la sensación de estar en control. De esta manera, los usuarios solo necesitan centrar su atención en los comandos de alto nivel (por ejemplo, elegir un elemento específico que agarrar, o elegir el destino final donde moverse) mientras el agente inteligente resuelve problemas de bajo nivel (como planificación de trayectorias, esquivar obstáculos, etc.) que permitan realizar la tarea designada de la manera óptima.En particular, esta tesis gira en torno a una señal neuronal cognitiva de alto nivel originada como la falta de coincidencia entre las expectativas del usuario y las acciones reales ejecutadas por los dispositivos inteligentes. Estas señales, denominadas potenciales de error (ErrPs), se consideran una forma natural de intercomunicar nuestro cerebro con máquinas y, por lo tanto, los usuarios solo requieren monitorizar las acciones de un dispositivo y evaluar mentalmente si este último se comporta correctamente o no. Esto puede verse como una forma de supervisar el comportamiento del dispositivo, en el que la decodificación de estas evaluaciones mentales se utiliza para proporcionar a estos dispositivos retroalimentación directamente relacionada con la ejecución de una tarea determinada para que puedan aprender y adaptarse a las preferencias del usuario.Dado que la respuesta neuronal de ErrP está asociada a un evento exógeno (dispositivo que comete una acción errónea), la mayoría de los trabajos desarrollados han intentado distinguir si una acción es correcta o errónea mediante la explotación de eventos discretos en escenarios bien controlados. Esta tesis presenta el primer intento de cambiar hacia configuraciones asíncronas que se centran en tareas relacionadas con el aumento de las capacidades motoras, con el objetivo de desarrollar interfaces para usuarios con movilidad limitada. En este tipo de configuraciones, dos desafíos importantes son que los eventos correctos o erróneos no están claramente definidos y los usuarios tienen que evaluar continuamente la tarea ejecutada, mientras que la clasificación de las señales EEG debe realizarse de forma asíncrona. Como resultado, los decodificadores tienen que lidiar constantemente con la actividad EEG de fondo, que típicamente conduce a una gran cantidad de errores de detección de firmas de error. Para superar estos desafíos, esta tesis aborda dos líneas principales de trabajo.Primero, explora la neurofisiología de las señales neuronales evocadas asociadas con la percepción de errores durante el uso interactivo de un BMI en escenarios continuos y más realistas.Se realizaron dos estudios para encontrar características alternativas basadas en el dominio de la frecuencia como una forma de lidiar con la alta variabilidad de las señales del EEG. Resultados, revelaron que existe un patrón estable representado como oscilaciones "theta" que mejoran la generalización durante la clasificación. Además, se utilizaron técnicas de aprendizaje automático de última generación para aplicar el aprendizaje de transferencia para discriminar asincrónicamente los errores cuando se introdujeron de forma gradual y no se conoce presumiblemente el inicio que desencadena los ErrPs. Además, los análisis de neurofisiología arrojan algo de luz sobre los mecanismos cognitivos subyacentes que provocan ErrP durante las tareas continuas, lo que sugiere la existencia de modelos neuronales en nuestro cerebro que acumulan evidencia y solo toman una decisión al alcanzar un cierto umbral. En segundo lugar, esta tesis evalúa la implementación de estos potenciales relacionados con errores en tres aplicaciones orientadas al usuario. Estos estudios no solo exploran cómo maximizar el rendimiento de decodificación de las firmas ErrP, sino que también investigan los mecanismos neuronales subyacentes y cómo los diferentes factores afectan las señales provocadas.La primera aplicación de esta tesis presenta una nueva forma de guiar a un robot móvil que se mueve en un entorno continuo utilizando solo potenciales de error como retroalimentación que podrían usarse para el control directo de dispositivos de asistencia. Con este propósito, proponemos un algoritmo basado en el emparejamiento de políticas para el aprendizaje de refuerzo inverso para inferir el objetivo del usuario a partir de señales cerebrales.La segunda aplicación presentada en esta tesis contempla los primeros pasos hacia un BCI híbrido para ejecutar distintos tipos de agarre de objetos, con el objetivo de ayudar a las personas que han perdido la funcionalidad motora de su extremidad superior. Este BMI combina la decodificación del tipo de agarre a partir de señales de EEG obtenidas del espectro de baja frecuencia con los potenciales de error provocados como resultado de la monitorización de movimientos de agarre erróneos. Los resultados muestran que, en efecto los ErrP aparecen en combinaciones de señales motoras originadas a partir de movimientos de agarre consistentes en una única repetición. Además, la evaluación de los diferentes factores involucrados en el diseño de la interfaz híbrida (como la velocidad de los estímulos, el tipo de agarre o la tarea mental) muestra cómo dichos factores afectan la morfología del subsiguiente potencial de error evocado.La tercera aplicación investiga los correlatos neuronales y los procesos cognitivos subyacentes asociados con desajustes somatosensoriales producidos por perturbaciones inesperadas durante la estimulación eléctrica neuromuscular en el brazo de un usuario. Este estudio simula los posibles errores que ocurren durante la terapia de neuro-rehabilitación, en la que la activación simultánea de la estimulación aferente mientras los sujetos se concentran en la realización de una tarea motora es crucial para una recuperación óptima. Los resultados muestran que los errores pueden aumentar la atención del sujeto en la tarea y desencadenar mecanismos de aprendizaje que al mismo tiempo podrían promover la neuroplasticidad motora.En resumen, a lo largo de esta tesis, se han diseñado varios paradigmas experimentales para mejorar la comprensión de cómo se generan los potenciales relacionados con errores durante el uso interactivo de BMI en aplicaciones orientadas al usuario. Se han propuesto diferentes métodos para pasar de la configuración bloqueada en el tiempo a la asíncrona, tanto en términos de decodificación como de percepción de los eventos erróneos; y ha explorado tres aplicaciones relacionadas con el aumento de las capacidades motoras, en las cuales los ErrPs se pueden usar para el control de dispositivos, la sustitución de motores y la neuro-rehabilitación.Brain-machine interfaces (BMIs) allow the decoding of cortical activation patterns from the users brain to provide people with severely limited mobility, due to an accident or disease, a way to establish a direct connection between their brain and a device. In this sense, BMIs based in noninvasive recordings, such as the electroencephalogram (EEG) have o↵ered these users new opportunities to regain control over activities of their daily life that they could not perform otherwise, especially in the areas of communication and control of their environment. Over the past years and with the latest technological advancements, devices have significantly grown on complexity expanding the number of possibilities to control complex robotic devices, prosthesis with numerous degrees of freedom or even to apply compound patterns of electrical stimulation on the subjects own paralyzed extremities to execute precise movements. However, the band-with of communication between brain and devices is still very limited, both in terms of the number and the speed at which neural commands can be decoded, and thus solely relying on neural signals do not guarantee accurate control them. In order to benefit of these technologies, the field of BMIs adopted the well-known approach of shared-control. This strategy intends to create a cooperation system between the user and an intelligent device, liberating the user from the burdensome parts of the task without losing the feeling of being in control. Here, users only need to focus their attention on high-level commands (e.g. choose the final destination to reach, or a specific item to grab) while the intelligent agent resolve low-level problems (e.g. trajectory planning, obstacle avoidance, etc) to perform the designated task in the optimal way. In particular, this thesis revolves around a high-level cognitive neural signal originated as the mismatch between the expectations of the user and the actual actions executed by the intelligent devices. These signals, denoted as error-related potentials (ErrPs), are thought as a natural way to intercommunicate our brain with machines and thus users only require to monitor the actions of a device and mentally assess whether the latter is behaving correctly or not. This can be seen as a way to supervise the device’s behavior, in which the decoding of these mental assessments is used to provide these devices with feedback directly related with the performance of a given task so they can learn and adapt to the user’s preferences. Since the ErrP’s neural response is associated to an exogenous event (device committing an erroneous action), most of the developed works have attempted to distinguish whether an action is correct or erroneous by exploiting discrete events under well-controlled scenarios. This thesis presents the first attempt to shift towards asynchronous settings that focus on tasks related with the augmentation of motor capabilities, with the objective of developing interfaces for users with limited mobility. In this type of setups, two important challenges are that correct or erroneous events are not clearly defined and users have to continuously evaluate the executed task, while classification of EEG signals has to be performed asynchronously. As a result, the decoders have to constantly deal with background EEG activity, which typically leads to a large number of missdetection of error signatures. To overcome these challenges, this thesis addresses two main lines of work. First, it explores the neurophysiology of the evoked neural signatures associated with the perception of errors during the interactive use of a BMI in continuous and more realistic scenarios. Two studies were performed to find alternative features based on the frequency domain as a way of dealing with the high variability of EEG signals. Results, revealed that there exists a stable pattern represented as theta oscillations that enhance generalization during classification. Also, state-of-the-art machine learning techniques were used to apply transfer learning to asynchronously discriminate errors when they were introduced in a gradual fashion and the onset that triggers the ErrPs is not presumably known. Furthermore, neurophsysiology analyses shed some light about the underlying cognitive mechanisms that elicit ErrP during continuous tasks, suggesting the existence of neural models in our brain that accumulate evidence and only take a decision upon reaching a certain threshold. Secondly, this thesis evaluates the implementation of these error-related potentials in three user-oriented applications. These studies not only explore how to maximize the decoding performance of ErrP signatures but also investigate the underlying neural mechanisms and how di↵erent factors a↵ect the elicited signals. The first application of this thesis presents a new way to guide a mobile robot moving in a continuous environment using only error potentials as feedback which could be used for the direct control of assistive devices. With this purpose, we propose an algorithm based on policy matching for inverse reinforcement learning to infer the user goal from brain signals. The second application presented in this thesis contemplates the first steps towards a hybrid BMI for grasping oriented to assist people who have lost motor functionality of their upper-limb. This BMI combines the decoding of the type of grasp from low-frequency EEG signals with error-related potentials elicited as the result of monitoring an erroneous grasping. The results show that ErrPs are elicited in combination of motor signatures from the low-frequency spectrum originated from single repetition grasping tasks and evaluates how di↵erent design factors (such as the speed of the stimuli, type of grasp or mental task) impact the morphology of the subsequent evoked ErrP. The third application investigates the neural correlates and the underlying cognitive processes associated with somatosensory mismatches produced by unexpected disturbances during neuromsucular electrical stimulation on a user’s arm. This study simulates possible errors that occur during neurorehabilitation therapy, in which the simultaneous activation of a↵erent stimulation while the subjects are concentrated in performing a motor task is crucial for optimal recovery. The results showed that errors may increase subject’s attention on the task and trigger learning mechanisms that at the same time could promote motor neuroplasticity. In summary, throughout this thesis, several experimental paradigms have been designed to improve the understanding of how error-related potentials are generated during the interactive use of BMIs in user-oriented applications. Di↵erent methods have been proposed to shift from time-locked to asynchronous settings, both in terms of decoding and perception of the erroneous events; and it has explored three applications related with the augmentation of motor capabilities, in which ErrPs can be used for control of devices, motor substitution and neurorehabilitation.<br /

Electroencephalographic Responses to Frictional Stimuli: Measurement Setup and Processing Pipeline

Tactility is a key sense in the human interaction with the environment. The understanding of tactile perception has become an exciting area in industrial, medical and scienti c research with an emphasis on the development of new haptic technologies. Surprisingly, the quanti cation of tactile perception has, compared to other senses, only recently become a eld of scienti c investigation. The overall goal of this emerging scienti c discipline is an understanding of the causal chain from the contact of the skin with materials to the brain dynamics representing recognition of and emotional reaction to the materials. Each link in this chain depends on individual and environmental factors ranging from the in uence of humidity on contact formation to the role of attention for the perception of touch. This thesis reports on the research of neural correlates to the frictional stimulation of the human ngertip. Event-related electroencephalographic potentials (ERPs) upon the change in ngertip friction are measured and studied, when pins of a programmable Braille-display were brought into skin contact. In order to contribute to the understanding of the causal chain mentioned above, this work combines two research areas which are usually not connected to each other, namely tribology and neuroscience. The goal of the study is to evaluate contributions of friction to the process of haptic perception. Key contributions of this thesis are: 1) Development of a setup to simultaneously record physical forces and ERPs upon tactile stimulation. 2) Implementation of a dedicated signal processing pipeline for the statistical analysis of ERP -amplitudes, -latencies and -instantaneous phases. 3) Interpretation of skin friction data and extraction of neural correlates with respect to varying friction intensities. The tactile stimulation of the ngertip upon raising and lowering of di erent lines of Braille-pins (one, three and ve) caused pronounced N50 and P100 components in the event-related ERPsequences, which is in line with the current literature. Friction between the ngertip and the Braille-system exhibited a characteristic temporal development which is attributed to viscoelastic skin relaxation. Although the force stimuli varied by a factor of two between the di erent Braillepatterns, no signi cant di erences were observed between the amplitudes and latencies of ERPs after standard across-trial averaging. Thus, for the rst time a phase measure for estimating singletrial interactions of somatosensory potentials is proposed. Results show that instantaneous phase coherency is evoked by friction, and that higher friction induces stronger and more time-localized phase coherencyDie Taktilität ist ein zentraler Sinn in der Interaktion mit unserer Umwelt. Das Bestreben, fundierte Erkenntnisse hinsichtlich der taktilenWahrnehmung zu gewinnen erhält groÿen Zuspruch in der industriellen, medizinischen und wissenschaftlichen Forschung, meist mit einem Fokus auf der Entwicklung von haptischen Technologien. Erstaunlicherweise ist jedoch die wissenschaftliche Quanti zierung der taktilen Wahrnehmung, verglichen mit anderen Sinnesmodalitäten, erst seit kurzem ein sich entwickelnder Forschungsbereich. Fokus dieser Disziplin ist es, die kognitive und emotionale Reaktion nach physischem Kontakt mit Materialien zu beschreiben, und die kausale Wirkungskette von der Berührung bis zur Reaktion zu verstehen. Dabei unterliegen die einzelnen Faktoren dieser Kette sowohl individuellen als auch externen Ein üssen, welche von der Luftfeuchtigkeit während des Kontaktes bis hin zur Rolle der Aufmerksamkeit für die Wahrnehmung reichen. Die vorliegende Arbeit beschäftigt sich mit der Untersuchung von neuronalen Korrelaten nach Reibungsstimulation des menschlichen Fingers. Dazu wurden Reibungsänderungen, welche durch den Kontakt der menschlichen Fingerspitze mit schaltbaren Stiften eines Braille-Display erzeugt wurden, untersucht und die entsprechenden neuronalen Korrelate aufgezeichnet. Um zu dem Verst ändnis der oben erwähnten Wirkungskette beizutragen, werden Ansätze aus zwei für gewöhnlich nicht zusammenhängenden Forschungsbereichen, nämlich der Tribologie und der Neurowissenschaft, kombiniert. Folgende Beiträge sind Hauptbestandteile dieser Arbeit: 1) Realisierung einer Messumgebung zur simultanen Ableitung von Kräften und ereigniskorrelierten Potentialen nach taktiler Stimulation der Fingerspitze. 2) Aufbau einer speziellen Signalverarbeitungskette zur statistischen Analyse von stimulationsabh ängigen EEG -Amplituden, -Latenzen und -instantanen Phasen. 3) Interpretation der erhobenen Reibungsdaten und Extraktion neuronaler Korrelate hinsichtlich variierender Stimulationsintensitäten. Unsere Resultate zeigen, dass die taktile Stimulation der Fingerspitze nach Anheben und Senken von Braille-Stiften zu signi kanten N50 und P100 Komponenten in den ereigniskorrelierten Potentialen führt, im Einklang mit der aktuellen Literatur. Die Reibung zwischen der Fingerspitze und dem Braille-System zeigte einen charakteristischen Signalverlauf, welcher auf viskoelastische Hautrelaxation zurückzuführen ist. Trotz der um einen Faktor zwei verschiedenen Intensit ätsunterschiede zwischen den Stimulationsmustern zeigten sich keine signi kanten Unterschiede zwischen den einfach gemittelten Amplituden der evozierten Potentialen. Erstmalig wurde ein Phasen-Maÿ zur Identi zierung von Unterschieden zwischen somatosensorischen "single-trial" Interaktionen angewandt. Diese Phasenanalyse zeigte, im Gegensatz zur Amplituden- und Latenzanalyse, deutlichere und signi kantere Unterschiede zwischen den Stimulationsparadigmen. Es wird gefolgert, dass Kohärenz zwischen den Momentanphasen durch Reibungsereignisse herbeigef ührt wird und dass durch stärkere Reibung diese Kohärenz, im zeitlichen Verlauf, stärker und lokalisierter wird

Personalized rTMS for Depression: A Review

Personalized treatments are gaining momentum across all fields of medicine. Precision medicine can be applied to neuromodulatory techniques, where focused brain stimulation treatments such as repetitive transcranial magnetic stimulation (rTMS) are used to modulate brain circuits and alleviate clinical symptoms. rTMS is well-tolerated and clinically effective for treatment-resistant depression (TRD) and other neuropsychiatric disorders. However, despite its wide stimulation parameter space (location, angle, pattern, frequency, and intensity can be adjusted), rTMS is currently applied in a one-size-fits-all manner, potentially contributing to its suboptimal clinical response (~50%). In this review, we examine components of rTMS that can be optimized to account for inter-individual variability in neural function and anatomy. We discuss current treatment options for TRD, the neural mechanisms thought to underlie treatment, differences in FDA-cleared devices, targeting strategies, stimulation parameter selection, and adaptive closed-loop rTMS to improve treatment outcomes. We suggest that better understanding of the wide and modifiable parameter space of rTMS will greatly improve clinical outcome

Feedback Controlled Increases in P300 Visual Evoked Potential Amplitudes and Neurological Functioning and Academic Performance in Learning Disabled Children

Results indicated that LD children can learn to control the amplitude of their VEPs. Analysis of variance found that the experimental children were able to significantly raise their baseline VEP amplitudes when provided with feedback. Cognitively, the effects of VEP amplitude increases in LD children were best seen in measures reflecting basic neurological functioning. Tukey\u27s Honesty Significant Differences found LD experimental subjects to have significantly improved scores on the Halsted-Reitan Index for Children when compared to a group of normal children. VEP training appeared to affect the EEG primarily in the right parietal-occipital areas

Individual test-retest reliability of evoked and induced alpha activity in human EEG data

Diverse psychological mechanisms have been associated with modulations of different EEG frequencies. To the extent of our knowledge, there are few studies of the test-retest reliability of these modulations in the human brain. To assess evoked and induced alpha reliabilities related to cognitive processing, EEG data from twenty subjects were recorded in 58 derivations in two different sessions separated by 49.5 +/- 48.9 (mean +/- standard deviation) days. A visual oddball was selected as the cognitive task, and three main parameters were analyzed for evoked and induced alpha modulations (latency, amplitude and topography). Latency and amplitude for evoked and induced modulations showed stable behavior between the two sessions. The correlation between sessions for alpha evoked and induced topographies in the grand average (group level) was r = 0.923, p<0.001; r = 0.962, p<0.001, respectively. The within-subject correlation values for evoked modulation ranged from 0.472 to 0.974 (mean: 0.766), whereas induced activity showed a different range, 0.193 to 0.892 (mean: 0.655). Individual analysis of the test-retest reliability showed a higher heterogeneity in the induced modulation, probably due to the heterogeneous phases found in the second case. However, despite this heterogeneity in phase values for induced activity relative to the onset of the stimuli, an excellent correlation score was obtained for group topography, with values that were better than those of the grand average evoked topography. As a main conclusion, induced alpha activity can be observed as a stable and reproducible response in the cognitive processing of the human brain

Neural synchrony in cortical networks : history, concept and current status

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies

Neural synchrony in cortical networks : history, concept and current status

Following the discovery of context-dependent synchronization of oscillatory neuronal responses in the visual system, the role of neural synchrony in cortical networks has been expanded to provide a general mechanism for the coordination of distributed neural activity patterns. In the current paper, we present an update of the status of this hypothesis through summarizing recent results from our laboratory that suggest important new insights regarding the mechanisms, function and relevance of this phenomenon. In the first part, we present recent results derived from animal experiments and mathematical simulations that provide novel explanations and mechanisms for zero and nero-zero phase lag synchronization. In the second part, we shall discuss the role of neural synchrony for expectancy during perceptual organization and its role in conscious experience. This will be followed by evidence that indicates that in addition to supporting conscious cognition, neural synchrony is abnormal in major brain disorders, such as schizophrenia and autism spectrum disorders. We conclude this paper with suggestions for further research as well as with critical issues that need to be addressed in future studies