Quantitative Kinematic Characterization of Reaching Impairments in Mice After a Stroke

Background and Objective. Kinematic analysis of reaching movements is increasingly used to evaluate upper extremity function after cerebrovascular insults in humans and has also been applied to rodent models. Such analyses can require time-consuming frame-by-frame inspections and are affected by the experimenter's bias. In this study, we introduce a semi-automated algorithm for tracking forepaw movements in mice. This methodology allows us to calculate several kinematic measures for the quantitative assessment of performance in a skilled reaching task before and after a focal cortical stroke. Methods. Mice were trained to reach for food pellets with their preferred paw until asymptotic performance was achieved. Photothrombosis was then applied to induce a focal ischemic injury in the motor cortex, contralateral to the trained limb. Mice were tested again once a week for 30 days. A high frame rate camera was used to record the movements of the paw, which was painted with a nontoxic dye. An algorithm was then applied off-line to track the trajectories and to compute kinematic measures for motor performance evaluation. Results. The tracking algorithm proved to be fast, accurate, and robust. A number of kinematic measures were identified as sensitive indicators of poststroke modifications. Based on end-point measures, ischemic mice appeared to improve their motor performance after 2 weeks. However, kinematic analysis revealed the persistence of specific trajectory adjustments up to 30 days poststroke, indicating the use of compensatory strategies. Conclusions. These results support the use of kinematic analysis in mice as a tool for both detection of poststroke functional impairments and tracking of motor improvements following rehabilitation. Similar studies could be performed in parallel with human studies to exploit the translational value of this skilled reaching analysis

Enhanced Low-Latency Detection of Motor Intention From EEG for Closed-Loop Brain-Computer Interface Applications

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Brain-Switches for Asynchronous Brain−Computer Interfaces: A Systematic Review

A brain–computer interface (BCI) has been extensively studied to develop a novel communication system for disabled people using their brain activities. An asynchronous BCI system is more realistic and practical than a synchronous BCI system, in that, BCI commands can be generated whenever the user wants. However, the relatively low performance of an asynchronous BCI system is problematic because redundant BCI commands are required to correct false-positive operations. To significantly reduce the number of false-positive operations of an asynchronous BCI system, a two-step approach has been proposed using a brain-switch that first determines whether the user wants to use an asynchronous BCI system before the operation of the asynchronous BCI system. This study presents a systematic review of the state-of-the-art brain-switch techniques and future research directions. To this end, we reviewed brain-switch research articles published from 2000 to 2019 in terms of their (a) neuroimaging modality, (b) paradigm, (c) operation algorithm, and (d) performance

Non-linear optimized spatial filter for single-trial identification of movement related cortical potential

To investigate the optimal filter settings for pre-processing of Movement Related Cortical Potentials (MRCP) for the detection through EEG in single trial, we have proposed a novel Non-Linear Optimized Spatial Filter (NL-SF) and compared it to the Optimized Spatial Filtering (OSF) used in literature. MRCPs from EEG recordings are emphasized, calculating the optimal non-linear combination of channels which isolates the signal of interest. The method is applied to EEG data recorded from 16 healthy patients either executing or imagining 50 self-paced upper limb movements (palmar grasp). MRCPs have been identified from the outputs of the two filters by matching with a template built by averaging responses to movement intentions in the training set. NL-SF had a median accuracy on the overall dataset of 84.6%, which is significantly better than that of OSF (i.e., 76.9%). Being a filter and feasible for self-paced applications, it could be of interest in online BCI system design

Attention-controlled assistive wrist rehabilitation using a low-cost EEG sensor

It is essential to make sure patients be actively involved in motor training using robot-assisted rehabilitation to achieve better rehabilitation outcomes. This paper introduces an attention-controlled wrist rehabilitation method using a low-cost EEG sensor. Active rehabilitation training is realized using a threshold of the attention level measured by the low-cost EEG sensor as a switch for a flexible wrist exoskeleton assisting wrist flexion/extension and radial/ulnar deviation. We present a prototype implementation of this active training method and provide a preliminary evaluation. The feasibility of the attention-based control was proven with the overall actuation success rate of 95%. The experimental results also proved that the visual guidance was helpful for the users to concentrate on the wrist rehabilitation training: two types of visual guidance, namely, looking at the hand motion shown on a video and looking at the user's own hand had no significant performance difference. A general threshold of a certain group of users can be utilized in the wrist robot control rather than a customized threshold to simplify the procedure

Digital neural circuits : from ions to networks

PhD ThesisThe biological neural computational mechanism is always fascinating to human beings since it shows several state-of-the-art characteristics: strong fault tolerance, high power efficiency and self-learning capability. These behaviours lead the developing trend of designing the next-generation digital computation platform. Thus investigating and understanding how the neurons talk with each other is the key to replicating these calculation features. In this work I emphasize using tailor-designed digital circuits for exactly implementing bio-realistic neural network behaviours, which can be considered a novel approach to cognitive neural computation. The first advance is that biological real-time computing performances allow the presented circuits to be readily adapted for real-time closed-loop in vitro or in vivo experiments, and the second one is a transistor-based circuit that can be directly translated into an impalpable chip for high-level neurologic disorder rehabilitations. In terms of the methodology, first I focus on designing a heterogeneous or multiple-layer-based architecture for reproducing the finest neuron activities both in voltage-and calcium-dependent ion channels. In particular, a digital optoelectronic neuron is developed as a case study. Second, I focus on designing a network-on-chip architecture for implementing a very large-scale neural network (e.g. more than 100,000) with human cognitive functions (e.g. timing control mechanism). Finally, I present a reliable hybrid bio-silicon closed-loop system for central pattern generator prosthetics, which can be considered as a framework for digital neural circuit-based neuro-prosthesis implications. At the end, I present the general digital neural circuit design principles and the long-term social impacts of the presented work

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