Emg
EMG
N/A
The Center for Translational Neurophysiology of Speech and Communication (CTNSC) @ Italian Institute of Technology (IIT), jointly with the University of Ferrara, is opening a number of PhD positions starting in November 1st, 2025. Research areas include improving performance and biocompatibility of electrode arrays for brain-computer interfaces, organic neuroelectronics for multimodal recordings and stimulation of the brain in vivo, hardware and software development for innovative exploration of brain signals, machine learning applications to multimodal brain and speech signals, investigation of sensorimotor functions in animal models, cortical recordings in human patients during awake Neurosurgery, and human non-invasive neurophysiology of speech and sensorimotor communication by means of TMS, EEG, EMG and MoCap.
In pursuit of a universal, biomimetic iBCI decoder: Exploring the manifold representations of action in the motor cortex
My group pioneered the development of a novel intracortical brain computer interface (iBCI) that decodes muscle activity (EMG) from signals recorded in the motor cortex of animals. We use these synthetic EMG signals to control Functional Electrical Stimulation (FES), which causes the muscles to contract and thereby restores rudimentary voluntary control of the paralyzed limb. In the past few years, there has been much interest in the fact that information from the millions of neurons active during movement can be reduced to a small number of “latent” signals in a low-dimensional manifold computed from the multiple neuron recordings. These signals can be used to provide a stable prediction of the animal’s behavior over many months-long periods, and they may also provide the means to implement methods of transfer learning across individuals, an application that could be of particular importance for paralyzed human users. We have begun to examine the representation within this latent space, of a broad range of behaviors, including well-learned, stereotyped movements in the lab, and more natural movements in the animal’s home cage, meant to better represent a person’s daily activities. We intend to develop an FES-based iBCI that will restore voluntary movement across a broad range of motor tasks without need for intermittent recalibration. However, the nonlinearities and context dependence within this low-dimensional manifold present significant challenges.
Change of mind in rapid free-choice picking scenarios
In a famous philosophical paradox, Buridan's ass perishes because he is equally hungry and thirsty, and cannot make up his mind whether to first drink or eat. We are faced daily with the need to pick between alternatives that are equally attractive (or not) to us. What are the processes that allow us to avoid paralysis and to rapidly select between such equal options when there are no preferences or rational reasons to rely on? One solution that was offered is that although on a higher cognitive level there is symmetry between the alternatives, on a neuronal level the symmetry does not maintain. What is the nature of this asymmetry of the neuronal level? In this talk I will present experiments addressing this important phenomenon using measures of human behavior, EEG, EMG and large scale neural network modeling, and discuss mechanisms involved in the process of intention formation and execution, in the face of alternatives to choose from. Specifically, I will show results revealing the temporal dynamics of rapid intention formation and, moreover, ‘change of intention’ in a free choice picking scenario, in which the alternatives are on a par for the participant. The results suggest that even in arbitrary choices, endogenous or exogenous biases that are present in the neural system for selecting one or another option may be implicitly overruled; thus creating an implicit and non-conscious ‘change of mind’. Finally, the question is raised: in what way do such rapid implicit ‘changes of mind’ help retain one’s self-control and free-will behavior?
A Single-Layer Neuromorphic Encoder Maps EMG Signals into Wrist Kinematics
Bernstein Conference 2024
Stochastic Process Model derived indicators of overfitting for deep architectures: Applicability to small sample recalibration of sEMG decoders
Bernstein Conference 2024
EMG monitoring of central neuroplastic changes after nerve transfer procedures in brachial plexus injury
FENS Forum 2024
Myoelectric gesture recognition in patients with spinal cord injury using a medium-density EMG system
FENS Forum 2024
Psychophysiological biomarkers to assess the effectiveness of surface EMG biofeedback as an alternative therapy to reduce chronic low back pain
FENS Forum 2024