Advances in Neurotechnology, Electronics and Informatics

Studying and modeling the brain as a whole is a real challenge. For such systemic models (in contrast to models of one brain area or aspect), there is a real need for new tools designed to perform complex numerical experiments, beyond usual tools distributed in the computer science and neuroscience communities. Here, we describe an effective solution, freely available on line and already in use, to validate such models of the brain functions. We explain why this is the best choice, as a complement to robotic setup, and what are the general requirements for such a benchmarking platform. In this experimental setup, the brainy-bot implementing the model to study is embedded in a simplified but realistic controlled environment. From visual, tactile and olfactory input, to body, arm and eye motor command, in addition to vital interoceptive cues, complex survival behaviors can be experimented. We also discuss here algorithmic high-level cognitive modules, making the job of building biologically plausible bots easier. The key point is to possibly alternate the use of symbolic representation and of complementary and usual neural coding. As a consequence, algorithmic principles have to be considered at higher abstract level, beyond a given data representation, which is an interesting challenge.

This paper presents the work done in the framework of the Si elegans project to develop the physics engine for the simulation of the roundworm Caenorhabditis elegans and the interfaces to define and visualize behavioural experiments of the worm. The physically-based simulation of the locomotion of the worm is guided by a biomechanical model, based on anatomically matched biphasic springs. The simulation is presented via an experiment visualization web, using a 3D motion reproduction obtained through animation bones. This web also displays information about the activation in muscles and neurons, on additional information panels. Finally, an experiment definition portal has been developed where, by means of a timeline, the user can easily design complex experimental assays.

The mammalian nervous system is very efficient at processing, integrating and making sense of different sensory information from the outside world. When compared to the processing speed of modern computers the mammalian nervous system is very slow but is compensated for by the dense parallel nature of the brain. Understanding and harnessing the computational power of such systems has long been the goal of computational neuroscientists. However, elucidating the most basic cognitive behaviour has been difficult due to the vast complexity of such a system. Through understanding and emulating simpler nervous systems, such as the C. elegans nematode, it is hoped that new insights into nervous system behaviour can be achieved. The Si elegans EU FP7 project aims to develop a Hardware Neural Network (HNN) to accurately replicate the C. elegans nervous system which has been widely studied in recent years and there now exists a vast wealth of knowledge about its nervous function and connectivity. To fully replicate the C. elegans nervous system requires powerful computing technologies, based on parallel processing, for real-time computation and therefore will use Field Programmable Gate Arrays (FPGAs) to achieve this. The project will also deliver an open-access framework via a Web Portal to neuroscientists, biologists, clinicians and engineers and will enable a global network of scientists to gain a better understanding of neural function. In this paper an overview of the complete hardware system required to fully realise Si elegans is presented along with an early small scale implementation of the hardware system.

The approach of using deterministic methods to trace white-matter fiber tracts through the brain and map brain connectivity is pervasive in currently followed tractographic methodologies. However, using deterministic procedures to support fiber mapping jeopardizes rigorous fiber tractography and may originate deficient maps of white matter fiber networks. We propose a new probabilistic framework for modeling fiber-orientation uncertainty and improve probabilistic tractography. A probabilistic methodology is proposed for estimating intravoxel principal fiber directions, based on clustering directional data arising from orientation distribution function profiles. Mixtures of von Mises-Fisher (vMF) distributions are used to support the probabilistic estimation of intravoxel fiber directions. The fitted parameters of the clustered vMF mixture at each voxel are then used to estimate white-matter pathways using particle filtering techniques. The proposed method is validated on synthetic simulations, as well as on real data experiments. The method holds promise to support robust tractographic methodologies, and build realistic models of white matter tracts in the human brain.

Research findings indicate that intensive therapy is essential for achieving better outcome in post-stroke rehabilitation. However, with the increasing number of stroke patients and limited healthcare resources, it is difficult to provide the needed amount of therapy. Robot-assisted rehabilitation based on serious games may offer the solution for providing a more autonomous and scalable training that can be transferred out of the clinic and into home environments. Robots offer precision and repeatability of movements that can be used to provide safe and intensive rehabilitation exercises, both at home and in the clinic. Serious games can provide an enjoyable and effective way to motivate patients to increase both the quality and quantity of therapy. In addition, these technologies can also be used to remotely assess the motor performance of patients and the therapy progress. The ArmAssist is a recent example of a low-cost robotic system designed specifically for post-stroke upper-limb telerehabilitation. The system incorporates a set of serious games for assessment and training, further described in this paper. Preliminary results from the ongoing clinical study reveal very positive responses from 10 patients and 2 therapists about the usability and integration of the system in the clinical setting. Training with this system is shown to be beneficial and enjoyable, and highly motivates patients to continue and endure longer durations of training. More data and analysis is required to extract further conclusions.

With exactly 302 neurons and about 8000 connections, the hermaphrodite of the soil-dwelling ringworm Caenorhabditis elegans features one of the simplest nervous systems in nature. The Si elegans project will provide a reverse-engineerable model of this nematode by emulating its nervous system and embodying it in a virtual world. The hardware will consist of 302 individual FPGAs, each carrying a neuron-specific neural response model. The FPGA neurons will be interconnected by an electro-optical connectome to distribute the signal at the axonal output or gap-junction pin of an FPGA neuron onto the respective synaptic input or gap-junction pins of postsynaptic FPGA neurons. This technology will replicate the known connectome of the nematode to allow for a biomimetic parallel information flow between neurons. This chapter focuses on the comparison of different electro-optical connectome concepts and on the required implementation steps with their advantages and disadvantages being explained.

Electroencephalography (EEG) is a widely used brain signal recording technique with many uses. The information conveyed in these recordings is a useful tool in the diagnosis of some diseases and disturbances, basic science, as well as in the development of non-invasive Brain-Machine Interfaces (BMI). However, the electrical recording setup comes with two major downsides, a. poor signal-to-noise ratio and b. the vulnerability to any external and internal noise sources. One of the main sources of artefacts is eye movements due to the electric dipole between the cornea and the retina. We have previously proposed that monitoring eye-movements provides a complementary signal for BMIs. Here we propose a novel technique to remove eye-related artefacts from the EEG recordings. We coupled Eye Tracking with EEG allowing us to independently measure when ocular artefact events occur through the eye tracker and thus clean them up in a targeted “supervised” manner instead of using a “blind” artefact clean up correction technique. Three standard methods of artefact correction were applied in an event-driven, supervised manner: 1. Independent Components Analysis (ICA), 2. Wiener Filter and 3. Wavelet Decomposition and compared to “blind” unsupervised ICA clean up. These are standard artefact correction approaches implemented in many toolboxes and experimental EEG systems and could easily be applied by their users in an event-driven manner. Already the qualitative inspection of the clean up traces shows that the simple targeted artefact event-driven clean up outperforms the traditional “blind” clean up approaches. We conclude that this justifies the small extra effort of performing simultaneous eye tracking with any EEG recording to enable simple, but targeted, automatic artefact removal that preserves more of the original signal.

Fusing naturalistic motor psychophysics with neuroimaging remains a key challenge in neuroscience, given that the former requires advanced motion tracking and the latter commonly entails certain technical compatibility constrains. Here we designed and developed fMOVE, a novel 3DOF fMRI-compatible motion tracking system to support realistic object manipulation (haptic) tasks during a neuroimaging session. fMOVE constitutes an ultra-low-cost technology, based on a standardized zoom-lens camera and ARToolkit, a software library for augmented reality applications. Motion tracking occurs with a 120 Hz frequency, that lies within the range of established fMRI-incompatible motion tracking methods. It captures the real-time movement of a marked hand-held object and provides online feedback of motor performance to subjects, thereby enabling closed-loop motor control and learning experiments. Tracking accuracy was tested against the performance levels of a commercial electromagnetic motion tracker. fMOVE thus constitutes a promising methodological platform to pursue the real-time, closed-loop study of motor behavior in real-world tasks and decipher its underlying neural mechanisms.

Decoding the neural activity based on ECoG signals is widely used in the field of Brain-Computer Interfaces (BCIs) to predict movement trajectories or control a prosthetic device. However, there are only few reports of using ECoG in stroke patients. In this paper, we compare different methods for predicting contralateral movement trajectories from epidural ECoG signals recorded over the lesioned hemisphere in three chronic stroke patients. The results show that movement trajectories can be predicted with correlation coefficients ranging from 0.24 to 0.64. Depending on the intended application, either the use of Support Vector Regression (SVR) or Canonical Correlation Analysis (CCA) obtained the best results. By investigating how ECoG based decoding performs in comparison with EMG based decoding it becomes visible that abnormal muscle activation patterns affect the prediction and that using activity of only the forearm muscles, there is no significant difference between ECoG and EMG for predicting wrist movement trajectory.

In this paper, an experiment designed to detect the will to perform several steps forward (as gait initiation) before it occurs using the electroencephalographic (EEG) signals collected from the scalp is presented. In order to detect this movement intention, the Event-Related Desynchronization phenomenon is detected using a SVM-based classifier. The preliminary results from seven users have been presented. In this work, the results obtained in a previous paper are enhance obtaining similar true positive rates (around 66 % in average) but reducing the false positive rates (with an average around 20 %). In the future, this improved Brain-Computer Interface will be used as part of the control system of an exoskeleton attached to the lower limb of people with incomplete and complete spinal cord injury to initiate their gait cycle.