Neurotechnology for Brain Repair

Neurotechnology is the application of scientific knowledge to the practical purpose of understanding, interacting and/or repairing the brain or, in a broader sense, the nervous system. The development of novel approaches to decode functional information from the brain, to enhance specific properties of neural tissue and to restore motor output in real end-users is a fundamental challenge to translate these novel solutions into clinical practice. In this Thesis, I introduce i) a novel imaging method to characterize movement-related electroencephalographic (EEG) potentials; ii) a brain stimulation strategy to improve brain-computer interface (BCI) control; iii) and a therapy for motor recovery involving a neuroprosthesis. Overall, results show i) that stable EEG topographies present a subject-independent organization that can be used to robustly decode actual or attempted movements in sub-acute stroke patients and healthy controls, with minimal a-priori information; ii) that transcranial direct-current stimulation (tDCS) enhances the modulability of sensorimotor rhythms used for brain-computer interaction in chronic Spinal Cord Injured (SCI) individuals and healthy controls; iii) that neuromuscular electrical stimulation (NMES) controlled via closed-loop neural activity induces significantly stronger upper limb functional recovery in chronic stroke patients than sham NMES therapy, and that these changes are clinically relevant. These results have or might have important implications in i) disease diagnostics and monitoring through EEG; ii) assistive technology and reduction of permanent disability following SCI; iii) rehabilitation and recovery of upper limb function following a stroke, also after several years of complete paralysis. Briefly, this Thesis provides the conceptual framework, scientific rationale, technical details and clinical evidence supporting translational Neurotechnology that improves, optimizes and disrupts current medical practice in monitoring, substituting and recovering lost upper limb function.

Development of a Transient Neural Interface for Minimally Invasive Recording and Stimulation

Adele Fanelli

Transient electronics enabling devices to safely disappear in the environment can be applied not only in green electronics, but also in bioelectronic medicine. Neural implants able to degrade harmlessly inside the body eliminate the need for removal surgery, allow for safe tissue remodelling, and potentially reduce inflammation responses and the chronic adverse reactions hindering device functionality and putting patients' health at risk. Sadly, current transient neurotechnology can be used only for very short-term applications. Fast degrading metals generally used to build these interfaces allow in fact for operations rarely longer than a few days, strongly limiting the available time window for collecting information or for a therapy to be finished.To solve this problem, we propose a transient, all-polymeric neural interface able to work for longer time scales. This would enable new applications of transient neurotechnology, such as recording of brain activity for a prolonged and more useful period of time before resection of epilepsy foci or tumours. The device consists of a polycaprolactone (PCL) scaffold with patterned poly 3,4-ethylene dioxythiophene : polystyrene sulfonate (PEDOT:PSS) traces and electrodes. In vivo neural recording demonstrated low noise levels for twelve weeks and histological analysis highlighted the cellular infiltration inside the PCL scaffold over time, hinting to potential tissue remodelling processes, which would not occur with a non-degradable device. To increase the device potential for a clinical translation, a minimally invasive procedure for its implantation has been proposed. The blood vasculature supplying the brain and nerves can be used as an access route to the neural tissue, offering a close enough location without direct damage to the tissue. By re-shaping the all-polymeric transient neural probe using a stent-inspired design, the new interface can be navigated through a 5Fr catheter and deployed inside 2 mm-diameter channels. Characterization showed promising electrodes endurance to pulsatile flow and prolonged stimulation, together with a good safety profile. This device can be used for neural recording or stimulation from within a blood vessel, for either central or peripheral nervous system. By simplifying the implantation procedure of the neural interface and maintaining its transiency, physicians and patients could feel more confident in applying innovative neurotechnology-based treatments, widening for example the access to deep brain stimulation therapy. To enable crossing of complex vasculature without wall ruptures caused by current manual push of guidewires, a new navigation paradigm for endovascular devices has been designed and tested on thin polyimide strips. A physiological pulsatile flow can propel these probes, even at curvatures as high as 0.25 mm-1, and low amplitude magnetic fields can steer their direction. Electrodes and sensors for temperature and flow can be embedded onto these micro-probes, for unprecedented local interrogation of deep vasculature regions. In conclusion, this thesis introduces new neural interfaces in the field of minimally invasive neurotechnology, reducing adverse effects and complications of non-degradable implants and open surgery. These devices and their concepts have the potential one day to be new tools of the bioelectronic medicine toolbox, contributing to the advancement of innovative treatments for neurodegenerative disorders.

EPFL2022

Towards a Robust BCI: Error Potentials and Online Learning

Anna Buttfield, Pierre Ferrez

Recent advances in the field of Brain-Computer Interfaces (BCIs) have shown that BCIs have the potential to provide a powerful new channel of communication, completely independent of muscular and nervous systems. However, while there have been successful laboratory demonstrations, there are still issues that need to be addressed before BCIs can be used by non-experts outside the laboratory. At IDIAP we have been investigating several areas that we believe will allow us to improve the robustness, flexibility and reliability of BCIs. One area is recognition of cognitive error states, that is, identifying errors through the brain's reaction to mistakes. The production of these error potentials (ErrP) in reaction to an error made by the user is well established. We have extended this work by identifying a similar but distinct ErrP that is generated in response to an error made by the interface, (a misinterpretation of a command that the user has given). This ErrP can be satisfactorily identified in single trials and can be demonstrated to improve the theoretical performance of a BCI. A second area of research is online adaptation of the classifier. BCI signals change over time, both between sessions and within a single session, due to a number of factors. This means that a classifier trained on data from a previous session will probably not be optimal for a new session. In this paper we present preliminary results from our investigations into supervised online learning that can be applied in the initial training phase. We also discuss the future direction of this research, including the combination of these two currently separate issues to create a potentially very powerful BCI.

2006

Bayesian machine learning applied in a brain-computer interface for disabled users

Ulrich Hoffmann

A brain-computer interface (BCI) is a system that enables control of devices or communication with other persons, only through cerebral activity, without using muscles. The main application for BCIs is assistive technology for disabled persons. Examples for devices that can be controlled by BCIs are artificial limbs, spelling devices, or environment control systems. BCI research has seen renewed interest in recent years, and it has been convincingly shown that communication via a BCI is in principle feasible. However, present day systems still have shortcomings that prevent their widespread application. In part, these shortcomings are caused by limitations in the functionality of the pattern recognition algorithms used for discriminating brain signals in BCIs. Moreover, BCIs are often tested exclusively with able-bodied persons instead of conducting tests with the target user group, namely disabled persons. The goal of this thesis is to extend the functionality of pattern recognition algorithms for BCI systems and to move towards systems that are helpful for disabled users. We discuss extensions of linear discriminant analysis (LDA), which is a simple but efficient method for pattern recognition. In particular, a framework from Bayesian machine learning, the so-called evidence framework, is applied to LDA. An algorithm is obtained that learns classifiers quickly, robustly, and fully automatically. An extension of this algorithm allows to automatically reduce the number of sensors needed for acquisition of brain signals. More specifically, the algorithm allows to perform electrode selection. The algorithm for electrode selection is based on a concept known as automatic relevance determination (ARD) in Bayesian machine learning. The last part of the algorithmic development in this thesis concerns methods for computing accurate estimates of class probabilities in LDA-like classifiers. These probabilities are used to build a BCI that dynamically adapts the amount of acquired data, so that a preset, approximate bound on the probability of misclassifications is not exceeded. To test the algorithms described in this thesis, a BCI specifically tailored for disabled persons is introduced. The system uses electroencephalogram (EEG) signals and is based on the P300 evoked potential. Datasets recorded from five disabled and four able-bodied subjects are used to show that the Bayesian version of LDA outperforms plain LDA in terms of classification accuracy. Also, the impact of different static electrode configurations on classification accuracy is tested. In addition, experiments with the same datasets demonstrate that the algorithm for electrode selection is computationally efficient, yields physiologically plausible results, and improves classification accuracy over static electrode configurations. The classification accuracy is further improved by dynamically adapting the amount of acquired data. Besides the datasets recorded from disabled and able-bodied subjects, benchmark datasets from BCI competitions are used to show that the algorithms discussed in this thesis are competitive with state-of-the-art electroencephalogram (EEG) classification algorithms. While the experiments in this thesis are uniquely performed with P300 datasets, the presented algorithms might also be useful for other types of BCI systems based on the EEG. This is the case because functionalities such as robust and automatic computation of classifiers, electrode selection, and estimation of class probabilities are useful in many BCI systems. Seen from a more general point of view, many applications that rely on the classification of cerebral activity could possibly benefit from the methods developed in this thesis. Among the potential applications are interrogative polygraphy ("lie detection") and clinical applications, for example coma outcome prognosis and depth of anesthesia monitoring.