Biomimetic Based EEG Learning for Robotics Complex Grasping and Dexterous Manipulation

There have been tremendous efforts to understand the biological nature of human grasping, in such a way that it can be learned and copied to prosthesis–robotics and dextrous grasping applications. Several biomimetic methods and techniques have been adopted, hence applied to analytically comprehend ways human performs grasping to duplicate human knowledge. A major topic for further study, is related to decoding the resulting EEG brainwaves during motorizing of fingers and moving parts. To accomplish this, there are a number of phases that are performed, including recording, pre-processing, filtration, and understanding of the waves. However, there are two important phases that have received substantial research attentions. The classification and decoding, of such massive and complex brain waves, as they are two important steps towards understanding patterns during grasping. In this respect, the fundamental objective of this research is to demonstrate how to employ advanced pattern recognition methods, like fuzzy c-mean clustering for understanding resulting EEG brain waves, in such a way to control a prosthesis or robotic hand, while relying sets of detected EEG brainwaves. There are a number of decoding and classification methods and techniques, however we shall look into fuzzy based clustering blended with principle component analysis (PAC) technique to help for the decoding mechanism. EEG brainwaves during a grasping and manipulation have been used for this analysis. This involves, movement of almost five fingers during a grasping defined task. The study has found that, it is not a straight forward task to decode all human fingers motions, as due to the complexity of grasping tasks. However, the adopted analysis was able to classify and identify the different narrowly performed and related fundamental events during a simple grasping task

Data-Driven Transducer Design and Identification for Internally-Paced Motor Brain Computer Interfaces: A Review

Brain-Computer Interfaces (BCIs) are systems that establish a direct communication pathway between the users' brain activity and external effectors. They offer the potential to improve the quality of life of motor-impaired patients. Motor BCIs aim to permit severely motor-impaired users to regain limb mobility by controlling orthoses or prostheses. In particular, motor BCI systems benefit patients if the decoded actions reflect the users' intentions with an accuracy that enables them to efficiently interact with their environment. One of the main challenges of BCI systems is to adapt the BCI's signal translation blocks to the user to reach a high decoding accuracy. This paper will review the literature of data-driven and user-specific transducer design and identification approaches and it focuses on internally-paced motor BCIs. In particular, continuous kinematic biomimetic and mental-task decoders are reviewed. Furthermore, static and dynamic decoding approaches, linear and non-linear decoding, offline and real-time identification algorithms are considered. The current progress and challenges related to the design of clinical-compatible motor BCI transducers are additionally discussed

VALIDATION OF A MODEL OF SENSORIMOTOR INTEGRATION WITH CLINICAL BENEFITS

Healthy sensorimotor integration – or how our touch influences our movements – is critical to efficiently interact with our environment. Yet, many aspects of this process are still poorly understood. Importantly, several movement disorders are often considered as originating from purely motor impairments, while a sensory origin could also lead to a similar set of symptoms. To alleviate these issues, we hereby propose a novel biologically-based model of the sensorimotor loop, known as the SMILE model. After describing both the functional, and the corresponding neuroanatomical versions of the SMILE, we tested several aspects of its motor component through functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS). Both experimental studies resulted in coherent outcomes with respect to the SMILE predictions, but they also provided novel scientific outcomes about such broad topics as the sub-phases of motor imagery, the neural processing of bodily representations, or the extend of the role of the extrastriate body area. In the final sections of this manuscript, we describe some potential clinical application of the SMILE. The first one presents the identification of plausible neuroanatomical origins for focal hand dystonia, a yet poorly understood sensorimotor disorder. The last chapter then covers possible improvements on brain-machine interfaces, driven by a better understanding of the sensorimotor system. -- La façon dont votre sens du toucher et vos mouvements interagissent est connue sous le nom d’intégration sensorimotrice. Ce procédé est essentiel pour une interaction normale avec tout ce qui nous entoure. Cependant, plusieurs aspects de ce processus sont encore méconnus. Plus important encore, l’origine de certaines déficiences motrices encore trop peu comprises sont parfois considérées comme purement motrice, alors qu’une origine sensorielle pourrait mener à un même ensemble de symptômes. Afin d’améliorer cette situation, nous proposons ici un nouveau modèle d’intégration sensorimotrice, dénommé « SMILE », basé sur les connaissances de neurobiologie actuelles. Dans ce manuscrit, nous commençons par décrire les caractéristiques fonctionnelles et neuroanatomiques du SMILE. Plusieurs expériences sont ensuite effectuées, via l’imagerie par résonance magnétique fonctionnelle (IRMf), et la stimulation magnétique transcranienne (SMT), afin de tester différents aspects de la composante motrice du SMILE. Si les résultats de ces expériences corroborent les prédictions du SMILE, elles ont aussi mis en évidences d’autres résultats scientifiques intéressants et novateurs, dans des domaines aussi divers que les sous-phases de l’imagination motrice, les processus cérébraux liés aux représentations corporelles, ou encore l’extension du rôle de l’extrastriate body area. Dans les dernières parties de ce manuscrit, nous dévoilons quelques applications cliniques potentielles de notre modèle. Nous utilisons le SMILE afin de proposer deux origines cérébrales plausibles de la dystonie focale de la main. Le dernier chapitre présente comment certaines technologies existantes, telles que les interfaces cerveaux-machines, pourraient bénéficier d’une meilleure compréhension du système sensorimoteur

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

Controlling Assistive Machines in Paralysis Using Brain Waves and Other Biosignals

The extent to which humans can interact with machines significantly enhanced through inclusion of speech, gestures, and eye movements. However, these communication channels depend on a functional motor system. As many people suffer from severe damage of the motor system resulting in paralysis and inability to communicate, the development of brain-machine interfaces (BMI) that translate electric or metabolic brain activity into control signals of external devices promises to overcome this dependence. People with complete paralysis can learn to use their brain waves to control prosthetic devices or exoskeletons. However, information transfer rates of currently available noninvasive BMI systems are still very limited and do not allow versatile control and interaction with assistive machines. Thus, using brain waves in combination with other biosignals might significantly enhance the ability of people with a compromised motor system to interact with assistive machines. Here, we give an overview of the current state of assistive, noninvasive BMI research and propose to integrate brain waves and other biosignals for improved control and applicability of assistive machines in paralysis. Beside introducing an example of such a system, potential future developments are being discussed

Evolving Spatio-temporal Data Machines Based on the NeuCube Neuromorphic Framework: Design Methodology and Selected Applications

The paper describes a new type of evolving connectionist systems (ECOS) called evolving spatio-temporal data machines based on neuromorphic, brain-like information processing principles (eSTDM). These are multi-modular computer systems designed to deal with large and fast spatio/spectro temporal data using spiking neural networks (SNN) as major processing modules. ECOS and eSTDM in particular can learn incrementally from data streams, can include ‘on the fly’ new input variables, new output class labels or regression outputs, can continuously adapt their structure and functionality, can be visualised and interpreted for new knowledge discovery and for a better understanding of the data and the processes that generated it. eSTDM can be used for early event prediction due to the ability of the SNN to spike early, before whole input vectors (they were trained on) are presented. A framework for building eSTDM called NeuCube along with a design methodology for building eSTDM using this are presented. The implementation of this framework in MATLAB, Java, and PyNN (Python) is presented. The latter facilitates the use of neuromorphic hardware platforms to run the eSTDM. Selected examples are given of eSTDM for pattern recognition and early event prediction on EEG data, fMRI data, multisensory seismic data, ecological data, climate data, audio-visual data. Future directions are discussed, including extension of the NeuCube framework for building neurogenetic eSTDM and also new applications of eSTDM

Down-Conditioning of Soleus Reflex Activity using Mechanical Stimuli and EMG Biofeedback

Spasticity is a common syndrome caused by various brain and neural injuries, which can severely impair walking ability and functional independence. To improve functional independence, conditioning protocols are available aimed at reducing spasticity by facilitating spinal neuroplasticity. This down-conditioning can be performed using different types of stimuli, electrical or mechanical, and reflex activity measures, EMG or impedance, used as biofeedback variable. Still, current results on effectiveness of these conditioning protocols are incomplete, making comparisons difficult. We aimed to show the within-session task- dependent and across-session long-term adaptation of a conditioning protocol based on mechanical stimuli and EMG biofeedback. However, in contrast to literature, preliminary results show that subjects were unable to successfully obtain task-dependent modulation of their soleus short-latency stretch reflex magnitude

Learning and adaptation in brain machine interfaces

Balancing subject learning and decoder adaptation is central to increasing brain machine interface (BMI) performance. We addressed these complementary aspects in two studies: (1) a learning study, in which mice modulated “beta” band activity to control a 1D auditory cursor, and (2) an adaptive decoding study, in which a simple recurrent artificial neural network (RNN) decoded intended saccade targets of monkeys. In the learning study, three mice successfully increased beta band power following trial initiations, and specifically increased beta burst durations from 157 ms to 182 ms, likely contributing to performance. Though the task did not explicitly require specific movements, all three mice appeared to modulate beta activity via active motor control and had consistent vibrissal motor cortex multiunit activity and local field potential relationships with contralateral whisker pad electromyograms. The increased burst durations may therefore by a direct result of increased motor activity. These findings suggest that only a subset of beta rhythm phenomenology can be volitionally modulated (e.g. the tonic “hold” beta), therefore limiting the possible set of successful beta neuromodulation strategies. In the adaptive decoding study, RNNs decoded delay period activity in oculomotor and working memory regions while monkeys performed a delayed saccade task. Adaptive decoding sessions began with brain-controlled trials using pre-trained RNN models, in contrast to static decoding sessions in which 300-500 initial eye-controlled training trials were performed. Closed loop RNN decoding performance was lower than predicted by offline simulations. More consistent delay period activity and saccade paths across trials were associated with higher decoding performance. Despite the advantage of consistency, one monkey’s delay period activity patterns changed over the first week of adaptive decoding, and the other monkey’s saccades were more erratic during adaptive decoding than during static decoding sessions. It is possible that the altered session paradigm eliminating eye-controlled training trials led to either frustration or exploratory learning, causing the neural and behavioral changes. Considering neural control and decoder adaptation of BMIs in these studies, future work should improve the “two-learner” subject-decoder system by better modeling the interaction between underlying brain states (and possibly their modulation) and the neural signatures representing desired outcomes