Evidence Accumulation in asynchronous BCI

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How I became myself after merging with a computer: Does human-machine symbiosis raise human rights issues?

Marcello Ienca

Novel usages of brain stimulation combined with artificially intelligent (AI) systems promise to address a large range of diseases. These new conjoined technologies, such as brain-computer interfaces (BCI), are increasingly used in experimental and clinica ...

ELSEVIER SCIENCE INC2023

Sensory-Motor Neurostimulation to Enhance Exosuit Performance

Silvestro Micera, Andrea Crema, Stanisa Raspopovic, Giacomo Valle

Exosuits typically provide limited mechanical support and rely on a user's residual functional ability. However, people with neurological impairments often suffer from both motor and sensory deficits that limit the assistance an exosuit can provide. To ove ...

IEEE2023

Neuroengineering of hallucinations through fMRI neurofeedback: Understanding and modulating neural dynamics for the control of hallucinations in health and disease

Herberto Dhanis Pedro De Barros Camacho

Recent years have seen considerable progress in understanding brain function, and in modulating it through real-time fMRI neurofeedback (NF). Inspired by these advances, paired with the unique possibility of inducing the clinically relevant Presence Halluc ...

EPFL2023

Inferring individual evaluation criteria for reaching trajectories with obstacle avoidance from EEG signals

Aude Billard, José del Rocio Millán Ruiz, Fumiaki Iwane

During reaching actions, the human central nerve system (CNS) generates the trajectories that optimize effort and time. When there is an obstacle in the path, we make sure that our arm passes the obstacle with a sufficient margin. This comfort margin varie ...

Nature Portfolio2023

Neurophysiological underpinnings of an intensive protocol for upper limb motor recovery in subacute and chronic stroke patients

Silvestro Micera, Matteo Vissani, Michael Lassi

ABS T R A C T BACKGROUND: Upper limb (UL) motor impairment following stroke is a leading cause of functional limitations in activities of daily living. Robot-assisted therapy supports rehabilitation, but how its efficacy and the underlying neural mechanism ...

Edizioni Minerva Medica2023

Motor imagery training to improve language processing: What are the arguments?

Sylvain Jean-François Harquel

Studies showed that motor expertise was found to induce improvement in language processing. Grounded and situated approaches attributed this effect to an underlying automatic simulation of the motor experience elicited by action words, similar to motor ima ...

FRONTIERS MEDIA SA2023

Closed-Loop Control of Epidural Electrical Stimulation in Patients with Spinal Cord Injury and Parkinson's Disease

Nicolò Macellari

Over the past two decades, epidural electrical stimulation (EES) has transitioned from a treatment for chronic pain to a promising paradigm for ameliorating deficits associated with neurological disorders like spinal cord injury (SCI) and Parkinson's Disea ...

EPFL2023

Walking naturally after spinal cord injury using a brain-spine interface

Grégoire Courtine, Léonie Asboth, Jocelyne Bloch, Robin Jonathan Demesmaeker, Henri Charles Alexandre Lorach, Thibault Jean Etienne Collin, Anne Marie Lucienne Watrin, Icare Sakr, Sergio Daniel Hernandez, Salif Axel Komi

A spinal cord injury interrupts the communication between the brain and the region of the spinal cord that produces walking, leading to paralysis(1,2). Here, we restored this communication with a digital bridge between the brain and spinal cord that enable ...

NATURE PORTFOLIO2023

Inter-Task Transfer of Prism Adaptation through Motor Imagery

Lisa Aïcha Mireille Julie Fleury

Prism adaptation (PA) is a useful method to investigate short-term sensorimotor plasticity. Following active exposure to prisms, individuals show consistent after-effects, probing that they have adapted to the perturbation. Whether after-effects are transf ...

MDPI2023

An attention-based deep learning approach for the classification of subjective cognitive decline and mild cognitive impairment using resting-state EEG

Silvestro Micera, Michael Lassi

Objective. This study aims to design and implement the first deep learning (DL) model to classify subjects in the prodromic states of Alzheimer's disease (AD) based on resting-state electroencephalographic (EEG) signals. Approach. EEG recordings of 17 heal ...

IOP Publishing Ltd2023

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