The Berlin Brain–Computer Interface: Non-Medical Uses of BCI Technology

Brain–computer interfacing (BCI) is a steadily growing area of research. While initially BCI research was focused on applications for paralyzed patients, increasingly more alternative applications in healthy human subjects are proposed and investigated. In particular, monitoring of mental states and decoding of covert user states have seen a strong rise of interest. Here, we present some examples of such novel applications which provide evidence for the promising potential of BCI technology for non-medical uses. Furthermore, we discuss distinct methodological improvements required to bring non-medical applications of BCI technology to a diversity of layperson target groups, e.g., ease of use, minimal training, general usability, short control latencies

Longitudinal ALS study: research of EEG biomarkers during the progression of the disease

ALS is a neurodegenerative disorder that brings patients to a state of complete paralysis. In this thesis a longitudinal analysis of EEG resting state data is performed for three patients, setting a signal processing pipeline to detect which features of the signal are changing over the observation period. It has been found a substantial difference in the spectral content of EEG signal between late-stage patients and the one observed during the transition to completely locked-in state (CLIS)

Electrophysiological Signatures of Fear Conditioning: From Methodological Considerations to Catecholaminergic Mechanisms and Translational Perspectives

Fear conditioning describes a learning mechanism during which a specific stimulus gets associated with an aversive event (i.e., an unconditioned stimulus; US). Thereby, this initially neutral or arbitrary stimulus becomes a so-called “conditioned” stimulus (CS), which elicits a conditioned threat response. Fear extinction refers to the decrease in conditioned threat responses as soon as the CS is repeatedly presented in the absence of the US. While fear conditioning is an important learning model for understanding the etiology and maintenance of anxiety and fear-related disorders, extinction learning is considered to reflect the most important learning process of exposure therapy. Neurophysiological signatures of fear conditioning have been widely studied in rodents, leading to the development of groundbreaking neurobiological models, including brain regions such as the amygdala, insula, and prefrontal areas. These models aim to explain neural mechanisms of threat processing, with the ultimate goal to improve treatment strategies for pathological fear. Recording intracranial electrical activity of single units in animals offers the opportunity to uncover neural processes involved in threat processing with excellent spatial and temporal resolution. A large body of functional magnetic resonance imaging (fMRI) studies have helped to translate this knowledge about the anatomy of fear conditioning into the human realm. fMRI is an imaging technique with a high spatial resolution that is well suited to study slower brain processes. However, the temporal resolution of fMRI is relatively poor. By contrast, electroencephalography (EEG) is a neuroscientific method to capture fast and transient cortical processes. While EEG offers promising opportunities to unravel the speed of neural threat processing, it also provides the possibility to study oscillatory brain activity (e.g., prefrontal theta oscillations). The present thesis contains six research manuscripts, describing fear conditioning studies that mainly applied EEG methods in combination with other central (fMRI) and peripheral (skin conductance, heart rate, and fear-potentiated startle) measures. A special focus of this thesis lies in methodological considerations for EEG fear conditioning research. In addition, catecholaminergic mechanisms are studied, with the ultimate goal of opening up new translational perspectives. Taken together, the present thesis addresses several methodological challenges for neuroscientific (in particular, EEG) fear conditioning research (e.g., appropriate US types and experimental designs, signal-to-noise ratio, simultaneous EEG-fMRI). Furthermore, this thesis gives critical insight into catecholaminergic (noradrenaline and dopamine) mechanisms. A variety of neuroscientific methods (e.g., EEG, fMRI, peripheral physiology, pharmacological manipulation, genetic associations) have been combined, an approach that allowed us (a) to translate knowledge from animal studies to human research, and (b) to stimulate novel clinical directions

Cognitive Assessment and Rehabilitation of subjects with Traumatic Brain Injury

This thesis regards the study and the development of new cognitive assessment and rehabilitation techniques of subjects with traumatic brain injury (TBI). In particular, this thesis i) provides an overview about the state of art of this new assessment and rehabilitation technologies, ii) suggests new methods for the assessment and rehabilitation and iii) contributes to the explanation of the neurophysiological mechanism that is involved in a rehabilitation treatment. Some chapters provide useful information to contextualize TBI and its outcome; they describe the methods used for its assessment/rehabilitation. The other chapters illustrate a series of experimental studies conducted in healthy subjects and TBI patients that suggest new approaches to assessment and rehabilitation. The new proposed approaches have in common the use of electroencefalografy (EEG). EEG was used in all the experimental studies with a different purpose, such as diagnostic tool, signal to command a BCI-system, outcome measure to evaluate the effects of a treatment, etc. The main achieved results are about: i) the study and the development of a system for the communication with patients with disorders of consciousness. It was possible to identify a paradigm of reliable activation during two imagery task using EEG signal or EEG and NIRS signal; ii) the study of the effects of a neuromodulation technique (tDCS) on EEG pattern. This topic is of great importance and interest. The emerged founding showed that the tDCS can manipulate the cortical network activity and through the research of optimal stimulation parameters, it is possible move the working point of a neural network and bring it in a condition of maximum learning. In this way could be possible improved the performance of a BCI system or to improve the efficacy of a rehabilitation treatment, like neurofeedback

EEG and ECoG features for Brain Computer Interface in Stroke Rehabilitation

The ability of non-invasive Brain-Computer Interface (BCI) to control an exoskeleton was used for motor rehabilitation in stroke patients or as an assistive device for the paralyzed. However, there is still a need to create a more reliable BCI that could be used to control several degrees of Freedom (DoFs) that could improve rehabilitation results. Decoding different movements from the same limb, high accuracy and reliability are some of the main difficulties when using conventional EEG-based BCIs and the challenges we tackled in this thesis. In this PhD thesis, we investigated that the classification of several functional hand reaching movements from the same limb using EEG is possible with acceptable accuracy. Moreover, we investigated how the recalibration could affect the classification results. For this reason, we tested the recalibration in each multi-class decoding for within session, recalibrated between-sessions, and between sessions. It was shown the great influence of recalibrating the generated classifier with data from the current session to improve stability and reliability of the decoding. Moreover, we used a multiclass extension of the Filter Bank Common Spatial Patterns (FBCSP) to improve the decoding accuracy based on features and compared it to our previous study using CSP. Sensorimotor-rhythm-based BCI systems have been used within the same frequency ranges as a way to influence brain plasticity or controlling external devices. However, neural oscillations have shown to synchronize activity according to motor and cognitive functions. For this reason, the existence of cross-frequency interactions produces oscillations with different frequencies in neural networks. In this PhD, we investigated for the first time the existence of cross-frequency coupling during rest and movement using ECoG in chronic stroke patients. We found that there is an exaggerated phase-amplitude coupling between the phase of alpha frequency and the amplitude of gamma frequency, which can be used as feature or target for neurofeedback interventions using BCIs. This coupling has been also reported in another neurological disorder affecting motor function (Parkinson and dystonia) but, to date, it has not been investigated in stroke patients. This finding might change the future design of assistive or therapeuthic BCI systems for motor restoration in stroke patients

The Neurological Underpinnings of Hypnosis and its Clinical Applications

The brain is so complex that it is almost impossible to select one variable as the reason for a specific observation. This paper will discuss the neurological basis of hypnosis, and how hypnosis has made unique contributions to the refinement and development of cognitive neuroscience. In addition, hypnosis has been proven to cure many psychological and neurologically based diseases. Due to in-depth study of the neurological underpinnings of hypnosis, much advancement has been made in elucidating the relationship between the complex neural circuitry of the brain, its direct correlation to consciousness, and both the efferent and afferent neurological systems. New neuroimaging techniques, such as fMRI and other brain scanning methods such as electroencephalography (EEG) and positron emission tomography (PET), have made it possible to localize task related regionally specific brain activity and cognitive mental state, which allows researchers to scientifically examine and construe the many obscure theories surrounding the phenomenon of hypnosis