NON INVASIVE INVESTIGATION OF SENSORIMOTOR CONTROL FOR FUTURE DEVELOPMENT OF BRAIN-MACHINE-INTERFACE (BMI)

My thesis focuses on describing novel functional connectivity properties of the sensorimotor system that are of potential interest in the field of brain-machine interface. In particular, I have investigated how the connectivity changes as a consequence of either pathologic conditions or spontaneous fluctuations of the brain's internal state. An ad-hoc electronic device has been developed to implement the appropriate experimental settings. First, the functional communication among sensorimotor primary nodes was investigated in multiple sclerosis patients afflicted by persistent fatigue. I selected this condition, for which there is no effective pharmacological treatment, since existing literature links this type of fatigue to the motor control system. In this study, electroencephalographic (EEG) and electromyographic (EMG) traces were acquired together with the pressure exerted on a bulb during an isometric hand grip. The results showed a higher frequency connection between central and peripheral nervous systems (CMC) and an overcorrection of the exerted movement in fatigued multiple sclerosis patients. In fact, even though any fatigue-dependent brain and muscular oscillatory activity alterations were absent, their connectivity worked at higher frequencies as fatigue increased, explaining 67% of the fatigue scale (MFIS) variance (p=.002). In other terms, the functional communication within the central-peripheral nervous systems, namely involving primary sensorimotor areas, was sensitive to tiny alterations in neural connectivity leading to fatigue, well before the appearance of impairments in single nodes of the network. The second study was about connectivity intended as propagation of information and studied in dependence on spontaneous fluctuations of the sensorimotor system triggered by an external stimulus. Knowledge of the propagation mechanisms and of their changes is essential to extract significant information from single trials. The EEG traces were acquired during transcranial magnetic stimulation (TMS) to yield to a deeper knowledge about the response to an external stimulation while the cortico-spinal system passes through different states. The results showed that spontaneous increases of the excitation of the node originating the transmission within the hand control network gave rise to dynamic recruitment patterns with opposite behaviors, weaker in homotopic and parietal circuits, stronger in frontal ones. As probed by TMS, this behavior indicates that the effective connectivity within bilateral circuits orchestrating hand control are dynamically modulated in time even in resting state. The third investigation assessed the plastic changes in the sensorimotor system after stroke induced by 3 months of robotic rehabilitation in chronic phase. A functional source extraction procedure was applied on the acquired EEG data, enabling the investigation of the functional connectivity between homologous areas in the resting state. The most significant result was that the clinical ameliorations were associated to a ‘normalization’ of the functional connectivity between homologous areas. In fact, the brain connectivity did not necessarily increase or decrease, but it settled within a ‘physiological’ range of connectivity. These studies strengthen our knowledge about the behavioral role of the functional connectivity among neuronal networks’ nodes, which will be essential in future developments of enhanced rehabilitative interventions, including brain-machine interfaces. The presented research also moves the definition of new indices of clinical state evaluation relevant for compensating interventions, a step forward

Development of EEG-based technologies for the characterization and treatment of neurological diseases affecting the motor function

This thesis presents a set of studies applying signal processing and data mining techniques in real-time working systems to register, characterize and condition the movement-related cortical activity of healthy subjects and of patients with neurological disorders affecting the motor function. Patients with two of the most widespread neurological affections impairing the motor function are considered here: patients with essential tremor and patients who have suffered a cerebro-vascular accident. The different chapters in the presented thesis show results regarding the normal cortical activity associated with the planning and execution of motor actions with the upper-limb, and the pathological activity related to the patients' motor dysfunction (measurable with muscle electrodes or movement sensors). The initial chapters of the book present i) a revision of the basic concepts regarding the role of the cerebral cortex in the motor control and the way in which the electroencephalographic activity allows its analysis and conditioning, ii) a study on the cortico-muscular interaction at the tremor frequency in patients with essential tremor under the effects of a drug reducing their tremor, and finally iii) a study based on evolutionary algorithms that aims to identify cortical patterns related to the planning of a number of motor tasks performed with a single arm. In the second half of the thesis book, two brain-computer interface systems to be used in rehabilitation scenarios with essential tremor patients and with patients with a stroke are proposed. In the first system, the electroencephalographic activity is used to anticipate voluntary movement actions, and this information is integrated in a multimodal platform estimating and suppressing the pathological tremors. In the second case, a conditioning paradigm for stroke patients based on the identification of the motor intention with temporal precision is presented and tested with a cohort of four patients along a month during which the patients undergo eight intervention sessions. The presented thesis has yielded advances from both the technological and the scientific points of view in all studies proposed. The main contributions from the technological point of view are: ¿ The design of an integrated upper-limb platform working in real-time. The platform was designed to acquire information from different types of noninvasive sensors (EEG, EMG and gyroscopic sensors) characterizing the planning and execution of voluntary movements. The platform was also capable of processing online the acquired data and generating an electrical feedback. ¿ The development of signal processing and classifying techniques adapted to the kind of signal recorded in the two kinds of patients considered in this thesis (patients with essential tremor and patients with a stroke) and to the requirements of online processing and real-time single-trial function desired for BCI applications. Especially in this regard, an original methodology to detect onsets of voluntary movements using slow cortical potentials and cortical rhythms has been presented. ¿ The design and validation in real-time of asynchronous BCI systems using motor planning EEG segments to anticipate or detect when patients begin a voluntary movement with the upper-limb. ¿ The proof of concept of the advantages of an EEG system integrated in a multimodal human-robot interface architecture that constitutes the first multimodal interface using the combined acquisition of EEG, EMG and gyroscopic data, which allows the concurrent characterization of different parts of the body associated with the execution of a movement. The main scientific contributions of this thesis are: ¿ The study of the EEG-based anticipation of voluntary movements presented in Chapter 5 of the thesis was the first demonstration (to the author's knowledge) of the capacity of the EEG signal to provide reliable movement predictions based on single-trial classification of online data of healthy subjects and ET patients. This study also provides, for the first time, the results of a BCI system tested in ET patients and it represents an original approach to BCI applications for this group of patients. ¿ It has been presented the first neurophysiological study using EEG and EMG data to analyze the effects of a drug on cortical activity and tremors of patients with ET. In addition, the obtained results have shown for the first time that a significant correlation exists between the dynamics of specific cortical oscillations and pathological tremor manifestation as a consequence of the drug effects. ¿ It has been proposed for the first time an experiment to inspect whether the EEG signal carries enough information to classify up to seven different tasks performed with a single limb. Both the methodology applied and the validation procedure are also innovative in this sort of studies. ¿ It has been demonstrated for the first time the relevance of combining different cortical sources of information (such as BP and ERD) to estimate the initiation of voluntary movements with the upper-limb. In this line, special relevance may be given to the positive results achieved with stroke patients, improving the results presented by similar previous EEG-based studies by other research groups. It has also been proposed for the first time an upper-limb intervention protocol for stroke patients using BP and ERD patterns to provide proprioceptive feedback tightly associated with the patients' expectations of movement. The effects of the proposed intervention have been studied with a small group of patients

EEG-based cognitive control behaviour assessment: an ecological study with professional air traffic controllers

Several models defining different types of cognitive human behaviour are available. For this work, we have selected the Skill, Rule and Knowledge (SRK) model proposed by Rasmussen in 1983. This model is currently broadly used in safety critical domains, such as the aviation. Nowadays, there are no tools able to assess at which level of cognitive control the operator is dealing with the considered task, that is if he/she is performing the task as an automated routine (skill level), as procedures-based activity (rule level), or as a problem-solving process (knowledge level). Several studies tried to model the SRK behaviours from a Human Factor perspective. Despite such studies, there are no evidences in which such behaviours have been evaluated from a neurophysiological point of view, for example, by considering brain activity variations across the different SRK levels. Therefore, the proposed study aimed to investigate the use of neurophysiological signals to assess the cognitive control behaviours accordingly to the SRK taxonomy. The results of the study, performed on 37 professional Air Traffic Controllers, demonstrated that specific brain features could characterize and discriminate the different SRK levels, therefore enabling an objective assessment of the degree of cognitive control behaviours in realistic setting

Recent Applications in Graph Theory

Graph theory, being a rigorously investigated field of combinatorial mathematics, is adopted by a wide variety of disciplines addressing a plethora of real-world applications. Advances in graph algorithms and software implementations have made graph theory accessible to a larger community of interest. Ever-increasing interest in machine learning and model deployments for network data demands a coherent selection of topics rewarding a fresh, up-to-date summary of the theory and fruitful applications to probe further. This volume is a small yet unique contribution to graph theory applications and modeling with graphs. The subjects discussed include information hiding using graphs, dynamic graph-based systems to model and control cyber-physical systems, graph reconstruction, average distance neighborhood graphs, and pure and mixed-integer linear programming formulations to cluster networks

Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Operant EEG-based BMI: Learning and consolidating device control with brain activity

"Whether you are reading this thesis on paper or screen, it is easy to take for granted all the highly specialized movements you are doing at this very moment just to go through each page. Just to turn a page, you have to reach for and grasp it, turn it and let go at the precise moment not to rip it.(...)

Brain-Computer Interfaces for Detection and Localization of Targets in Aerial Images

Objective. The N2pc event-related potential (ERP) appears on the opposite side of the scalp with respect to the visual hemisphere where an object of interest is located. We explored the feasibility of using it to extract information on the spatial location of targets in aerial images shown by means of a rapid serial visual presentation (RSVP) protocol using single-Trial classification. Methods. Images were shown to 11 participants at a presentation rate of 5 Hz while recording electroencephalographic signals. With the resulting ERPs, we trained linear classifiers for single-Trial detection of target presence and location. We analyzed the classifiers' decisions and their raw output scores on independent test sets as well as the averages and voltage distributions of the ERPs. Results. The N2pc is elicited in RSVP presentation of complex images and can be recognized in single trials (the median area under the receiver operating characteristic curve was 0.76 for left versus right classification). Moreover, the peak amplitude of this ERP correlates with the horizontal position of the target within an image. The N2pc varies significantly depending on handedness, and these differences can be used for discriminating participants in terms of their preferred hand. Conclusion and Significance. The N2pc is elicited during RSVP presentation of real complex images and contains analogue information that can be used to roughly infer the horizontal position of targets. Furthermore, differences in the N2pc due to handedness should be taken into account when creating collaborative brain-computer interfaces