Recherche

Afficher tous les résultats pour

Accueil

Publication

Learning to control a BMI-driven wheelchair for people with severe tetraplegia

Publications associées (30)

À propos
Confidentialité
Mentions légales

Graph Chatbot

Sense of agency for intracortical brain-machine interfaces

Olaf Blanke, Andrea Serino, Tommaso Enrico Bertoni, Nathan Quentin Faivre, Marco Solcà, Pavo Orepic, Gaurav Sharma

Intracortical brain-machine interfaces decode motor commands from neural signals and translate them into actions, enabling movement for paralysed individuals. The subjective sense of agency associated with actions generated via intracortical brain-machine ...

NATURE PORTFOLIO2022

The present and future of neural interfaces

Marcello Ienca

The 2020's decade will likely witness an unprecedented development and deployment of neurotechnologies for human rehabilitation, personalized use, and cognitive or other enhancement. New materials and algorithms are already enabling active brain monitoring ...

FRONTIERS MEDIA SA2022

Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques

Mahsa Shoaran, Bingzhao Zhu

Objective. Accurate decoding of individual finger movements is crucial for advanced prosthetic control. In this work, we introduce the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine ...

IOP Publishing Ltd2022

Decoding of error-related potentials in continuous feedback protocols for personalized human computer interaction

Fumiaki Iwane

The ability to notice erroneous behavior is crucial for effective training. Within the framework of neuroprosthetics, numerous studies in electroencephalography (EEG) confirm the existence of neural correlates when humans perceive the erroneous actions of ...

EPFL2021

Adaptive Wireless Power Transfer and Backscatter Communication for Perpetual Operation of Wireless Brain-Computer Interfaces

James Daniel Rosenthal

Brain-computer interfaces (BCIs) are neural prosthetics that enable closed-loop electrophysiology procedures. These devices are currently used in fundamental neurophysiology research, and they are moving toward clinical viability for neural rehabilitation. ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Neural interface systems with on-device computing: machine learning and neuromorphic architectures

Mahsa Shoaran

Development of neural interface and brain-machine interface (BMI) systems enables the treatment of neurological disorders including cognitive, sensory, and motor dysfunctions. While neural interfaces have steadily decreased in form factor, recent developme ...

CURRENT BIOLOGY LTD2021

Does spontaneous motion lead to intuitive Body-Machine Interfaces? A fitness study of different body segments for wearable telerobotics

Dario Floreano, Fabrizio Schiano, Matteo Macchini

Human-Robot Interfaces (HRIs) represent a crucial component in telerobotic systems. Body-Machine Interfaces (BoMIs) based on body motion can feel more intuitive than standard HRIs for naive users as they leverage humans’ natural control capability over the ...

2021

Motor state transitions and Breathing in Brain Machine Interfaces

Bastien Orset

Brain-Machine interfaces aim to create a direct neural link between user's brain and machines. This goal has pushed scientists to investigate a large spectrum of applications in the realm of assistive and rehabilitation technologies. However, despite great ...

EPFL2021

Personalized Body-Machine Interfaces for Advanced Human-Robot Interaction

Matteo Macchini

Robotic systems are becoming more and more pervasive in modern industrial, scientific and personal activities through recent years and will play a fundamental role in the future society. Despite their increasing level of automation, teleoperation is still ...

EPFL2021

Learning strategies and representations for intuitive robot learning from demonstration

Thibaut Antoine Kulak

Robots are becoming more and more present around us, both in industries and in our homes. One key capability of robots is their adaptability to various situations that might appear in the real world. Robot skill learning is therefore a crucial aspect of ro ...

EPFL2021

Page 2 sur 3

Fast and accurate decoding of finger movements from ECoG through Riemannian features and modern machine learning techniques

Mahsa Shoaran, Bingzhao Zhu

IOP Publishing Ltd2022

Decoding of error-related potentials in continuous feedback protocols for personalized human computer interaction

Fumiaki Iwane

EPFL2021

Adaptive Wireless Power Transfer and Backscatter Communication for Perpetual Operation of Wireless Brain-Computer Interfaces

James Daniel Rosenthal

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022

Does spontaneous motion lead to intuitive Body-Machine Interfaces? A fitness study of different body segments for wearable telerobotics

Dario Floreano, Fabrizio Schiano, Matteo Macchini

2021