Clemonsabbott8357

Neurological disorders can lead to significant impairments in speech communication and, in severe cases, cause the complete loss of the ability to speak. Brain-Computer Interfaces have shown promise as an alternative communication modality by directly transforming neural activity of speech processes into a textual or audible representations. Previous studies investigating such speech neuroprostheses relied on electrocorticography (ECoG) or microelectrode arrays that acquire neural signals from superficial areas on the cortex. While both measurement methods have demonstrated successful speech decoding, they do not capture activity from deeper brain structures and this activity has therefore not been harnessed for speech-related BCIs. In this study, we bridge this gap by adapting a previously presented decoding pipeline for speech synthesis based on ECoG signals to implanted depth electrodes (sEEG). For this purpose, we propose a multi-input convolutional neural network that extracts speech-related activity separately for each electrode shaft and estimates spectral coefficients to reconstruct an audible waveform. We evaluate our approach on open-loop data from 5 patients who conducted a recitation task of Dutch utterances. We achieve correlations of up to 0.80 between original and reconstructed speech spectrograms, which are significantly above chance level for all patients (p less then 0.001). Our results indicate that sEEG can yield similar speech decoding performance to prior ECoG studies and is a promising modality for speech BCIs.Adaptive deep brain stimulation (aDBS) promises a significant improvement in patient outcomes, compared to existing deep brain stimulation devices. Fully implanted systems represent the next step to the clinical adoption of aDBS. We take advantage of a unique longitudinal data set formed as part of an effort to investigate aDBS for essential tremor to verify the long term reliability of electrocorticography strips over the motor cortex as a source of bio-markers for control of adaptive stimulation. We show that beta band event related de-synchronization, a promising bio-marker for movement, is robust even when used to trigger aDBS. Over the course of several months we show a minor increase in beta band event related de-synchronization in patients with active deep brain stimulation confirming that it could be used in chronically implanted systems.Clinical relevance - We show the promise and practicality of cortical electrocorticography strips for use in fully implanted, clinically translatable, aDBS systems.Rehabilitation promoting "assistance-as-needed" is considered a promising scheme of active rehabilitation, since it can promote neuroplasticity faster and thus reduce the time needed until restoration. To implement such schemes using robotic devices, it is crucial to be able to predict accurately and in real-time the intention of motion of the patient. In this study, we present an intention-of-motion model trained on healthy volunteers. The model is trained using kinematics and muscle activation time series data, and returns future predicted values for the kinematics. β-Glycerophosphate We also present the results of an analysis of the sensitivity of the accuracy of the model for different amount of training datasets and varying lengths of the prediction horizon. We demonstrate that the model is able to predict reliably the kinematics of volunteers that were not involved in its training. The model is tested with three types of motion inspired by rehabilibation tasks. In all cases, the model is predicting the arm kinematics with a Root Mean Square Error (RMSE) below 0.12m. Being a non person-specific model, it could be used to predict kinematics even for patients that are not able to perform any motion without assistance. The resulting kinematics, even if not fully representative of the specific patient, might be a preferable input for a robotic rehabilitator than predefined trajectories currently in use.We have investigated selective electrical stimulation of myelinated nerve fibers using a computational model of temporal interfering (TI) fields. The model consists of two groups of electrodes placed on the outer bundle surface, each group stimulated at a different frequency. We manipulated the stimulus waveform, magnitude and frequency of short-duration stimuli (70ms), and investigated fiber-specific stimulus-elicited compound action potentials. Results show that under 100Hz & 200Hz TI stimulation with 0.6mA total current shared by the electrodes, continuous action potentials were generated in deeper nerve fibers, and that the firing region was steerable by changing individual electrode currents. This study provides a promising platform for non-invasive nerve bundle stimulation by TI fields.EEG-EMG based hybrid Brain Computer Interface (hBCI) utilizes the brain-muscle physiological system to interpret and identify motor behaviors, and transmit human intelligence to automated machines in AI applications such as neurorehabilitations and brain-like intelligence. The study introduces a hBCI method for motor behaviors, where multiple time series of the brain neuromuscular network are introduced to indicate brain-muscle causal interactions, and features are extracted based on Relative Causal Strengths (RCSs) derived by Noise-assisted Multivariate Empirical Mode Decomposition (NA-MEMD) based Causal Decomposition. The complex process in brain neuromuscular interactions is specifically investigated towards a monitoring task of upper limb movement, whose 63-channel EEGs and 2-channel EMGs are composed of data inputs. The energy and frequency factors counted from RCSs were extracted as Core Features (CFs). Results showed accuracies of 91.4% and 81.4% with CFs for identifying cascaded (No Movement and Movement Execution) and 3-class (No Movement, Right Movement, and Left Movement) using Naive Bayes classifier, respectively. Moreover, those reached 100% and 94.3% when employing CFs combined with eigenvalues processed by Common Spatial Pattern (CSP). This initial work implies a novel causality inference based hBCI solution for the detection of human upper limb movement.Transcranial electrical stimulation (tES), which modulates cortical excitability via electric currents, has attracted increasing attention because of its application in treating neurologic and psychiatric disorders. To obtain a better understanding of the brain areas affected and stimulation's cellular effects, a multi-scale model was proposed that combines multi-compartmental neuronal models and a head model. While one multi-scale model of tES that used straight axons reported that the direction of electric field (EF) is a determining factor in a neuronal response, another model of transcranial magnetic stimulation (TMS) that used arborized axons reported that EF magnitude is more crucial than EF direction because of arborized axons' reduced sensitivity to the latter. Our goal was to investigate whether EF magnitude remains a crucial factor in the neuronal response in a multi-scale model of tES into which an arborized axon is integrated. To achieve this goal, we constructed a multi-scale model that integrated three types of neurons and a realistic head model, and then simulated the neuronal response to realistic EF. link2 We found that EF magnitude was correlated with excitation threshold, and thus, it may be one of the determining factors in cortical neurons' response to tES.Clinical Relevance-This multi-scale model based on biophysical and morphological properties and realistic brain geometry may help elucidate tES's neural mechanisms. Moreover, given its clinical applications, this model may help predict a patient's neuronal response to brain stimulation effectively.Exoskeleton-assisted gait rehabilitation is a promising complement to traditional motion rehabilitation programs for afflictions such as stroke or spinal cord injury. However, some challenges persist that hinder the translation of this approach to the clinical practice. One of these aspects is the objective assessment of patients' progress from information collected during exoskeleton-assisted therapy sessions with minimal hardware setup. In order to carry out an objective assessment with the data collected during the sessions, in this work (1) we implement and compute a set of metrics (Harmonic Ratio, Joint Trajectory Correlation, and Intralimb Coordination) from data provided by the exoskeleton and two inertial motion units (IMUs) while subjects walked during their rehabilitation sessions, (2) we evaluate the capacity of the metrics to discriminate between the different patients' physical conditions, and (3) assess the correspondence of the patient evaluations using the mentioned metrics and traditional clinical scores. Our results show that Intralimb Coordination has the greatest capacity to discriminate between different physical states of the patients and presents the best correlation with their clinical assessment.Clinical relevance- This work could guide clinicians and researchers to formulate a more objective assessment of progress of patients who have experienced a spinal cord in- jury using data collected during exoskeleton-assisted therapy sessions.Post-stroke hemiparesis often impairs gait and increases the risks of falls. Low and variable Minimum Toe Clearance (MTC) from the ground during the swing phase of the gait cycle has been identified as a major cause of such falls. In this paper, we study MTC characteristics in 30 chronic stroke patients, extracted from gait patterns during treadmill walking, using infrared sensors and motion analysis camera units. We propose objective measures to quantify MTC asymmetry between the paretic and non-paretic limbs using Poincaré analysis. We show that these subject independent Gait Asymmetry Indices (GAIs) represent temporal variations of relative MTC differences between the two limbs and can distinguish between healthy and stroke participants. Compared to traditional measures of cross-correlation between the MTC of the two limbs, these measures are better suited to automate gait monitoring during stroke rehabilitation. Further, we explore possible clusters within the stroke data by analysing temporal dispersion of MTC features, which reveals that the proposed GAIs can also be potentially used to quantify the severity of lower limb hemiparesis in chronic stroke.In this paper, we propose a deep learning-based algorithm to improve the performance of automatic speech recognition (ASR) systems for aphasia, apraxia, and dysarthria speech by utilizing electroencephalography (EEG) features recorded synchronously with aphasia, apraxia, and dysarthria speech. We demonstrate a significant decoding performance improvement by more than 50% during test time for isolated speech recognition task and we also provide preliminary results indicating performance improvement for the more challenging continuous speech recognition task by utilizing EEG features. link3 The results presented in this paper show the first step towards demonstrating the possibility of utilizing non-invasive neural signals to design a real-time robust speech prosthetic for stroke survivors recovering from aphasia, apraxia, and dysarthria. Our aphasia, apraxia, and dysarthria speech-EEG data set will be released to the public to help further advance this interesting and crucial research.