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Concept

Brain-reading

Publications associées (27)

Task matters: Individual MEG signatures from naturalistic and neurophysiological brain states

Alessandra Griffa, Enrico Amico, Ekansh Sareen

The discovery that human brain connectivity data can be used as a "fingerprint " to identify a given individual from a population, has become a burgeoning research area in the neuroscience field. Recent studies have iden-tified the possibility to extract t ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

Sense of agency for intracortical brain-machine interfaces

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

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

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

Towards everyday brain-computer interfacing through decoding visual cognition

Ruslan Aydarkhanov

Decoding visual cognition from non-invasive measurements of brain activity has shown valuable applications. Vision-based Brain-Computer Interfaces (BCI) systems extend from spellers to database search and spatial navigation. Despite the high performance of ...

EPFL2020

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network

Silvestro Micera, Fiorenzo Artoni

Objective.Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation and during locomotion. However, the decoding of gait patterns from brain signals remains an open challenge. ...

IOP PUBLISHING LTD2020

First Biopotential Recordings from a Liquid Crystal Optrode

Emilie Cornelia Maria Revol

One of the goals of the Neural Engineering System Design (NESD) program in the United States and of similar programs around the world is to develop an interface able to read from one million neurons in parallel. This is well beyond the capabilities of trad ...

SPIE-INT SOC OPTICAL ENGINEERING2019

Inferring subjective preferences on robot trajectories using EEG signals

Aude Billard, José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil, Iason Batzianoulis, Fumiaki Iwane

Cognitive information has been exploited in non-invasive Brain Computer Interface (BCI) scenarios to provide autonomous external agents with additional information. In this context, Error-related potentials (ErrPs), temporal deflections in electroencephalo ...

IEEE2019

The Connection Between the Nervous System and Machines: Commentary

Giacomo Valle

Decades of technological developments have populated the field of brain-machine interfaces and neuroprosthetics with several replacement strategies, neural modulation treatments, and rehabilitation techniques to improve the quality of life for patients aff ...

2019

Neuroprosthetics to support impaired hand functions: A multidisciplinary approach combining brain-machine interfaces and wearable exoskeletons

Luca Randazzo

Neuroprosthetics, the discipline that aims at interfacing neural systems to artificially engineered devices, has witnessed in recent years important advancements towards the ultimate goal of augmenting and restoring human functions through technology and a ...

EPFL2018

Analysing Vibrotactually Stimulated EEG Signals to Comprehend Object Shapes

Tactile feedback has the capability of reducing the workload on the visual channel, during visual feedback in brain-computer interfaces (BCIs). It is requisite to analyse the brain signals corresponding to the tactile stimulations. This work is aimed at an ...

IEEE2018

Brain-actuated functional electrical stimulation elicits lasting arm motor recovery after stroke

Hua Zhang, José del Rocio Millán Ruiz, Robert Leeb, Inaki Asier Iturrate Gil, Serafeim Perdikis, Andrea Biasiucci, Tiffany Corbet, Thomas Schmidlin, Dragana Viceic

Brain-computer interfaces (BCI) are used in stroke rehabilitation to translate brain signals into intended movements of the paralyzed limb. However, the efficacy and mechanisms of BCI-based therapies remain unclear. Here we show that BCI coupled to functio ...

Nature Research2018

Closed-loop electrical neurostimulation: Challenges and opportunities

José del Rocio Millán Ruiz, Inaki Asier Iturrate Gil, Michael Eric Anthony Pereira

Non-invasive and invasive electrical neurostimulation are promising tools to better understand brain function and ultimately treat its malfunction. In current open-loop approaches, a clinician chooses a fixed set of stimulation parameters, informed by obse ...

2018

The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users

José del Rocio Millán Ruiz, Luca Tonin, Serafeim Perdikis, Sareh Saeedi, Christoph Schneider

This work aims at corroborating the importance and efficacy of mutual learning in motor imagery (MI) brain–computer interface (BCI) by leveraging the insights obtained through our participation in the BCI race of the Cybathlon event. We hypothesized that, ...

2018

Analysis of EEG Correlates of Perceived Difficulty in Dynamically Changing Flying Tasks

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Ping-Keng Jao

Real-time workload estimation can be an important tool to improve human-machine interaction. Despite multiple efforts in this sense, most studies focused on tasks designed to have extreme conditions of workload (high vs low), and these levels remain rather ...

IEEE2018

Brain-Computer Interface meets ROS: A robotic approach to mentally drive telepresence robots

Luca Tonin

This paper shows and evaluates a novel approach to integrate a non-invasive Brain-Computer Interface (BCI) with the Robot Operating System (ROS) to mentally drive a telepresence robot. Controlling a mobile device by using human brain signals might improve ...

IEEE COMPUTER SOC2018

EEG-based lower-limb movement onset decoding: Continuous classification and asynchronous detection

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Mohamed Bouri, Dong Liu, Kyuhwa Lee, Fumiaki Iwane

Brain-machine interfaces (BMIs) have been used to incorporate the user intention to trigger robotic devices by decoding movement onset from electroencephalography (EEG). Active neural participation is crucial to promote brain plasticity thus to enhance the ...

2018

Inverse solutions for brain-computer interfaces: Effects of regularisation on localisation and classification

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Mohit Kumar Goel

Estimation of intracranial sources, using inverse solutions methods, has been proposed as a mean to improve performance in non-invasive brain-computer interfaces. These methods estimate the activity of a large number of neural sources from a smaller number ...

2017

An Approach to a Phase Model for Steady State Visually Evoked Potentials

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano

One of the most used signals in Brain Machine Interfaces (BMI) is the Steady State Visually Evoked Potentials (SSVEP). In a SSVEP-based BMI, a visual stimulus that flickers in a constant frequency is presented to the user, and the system has to detect if t ...

Springer International Publishing Ag2017

An EEG-based brain-computer interface for gait training

José del Rocio Millán Ruiz, Dong Liu, Kyuhwa Lee

This work presents an electroencephalography (EEG)-based Brain-computer Interface (BCI) that decodes cerebral activities to control a lower-limb gait training exoskeleton. Motor imagery (MI) of flexion and extension of both legs was distinguished from the ...

2017

Brain-actuated gait trainer with visual and proprioceptive feedback

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Mohamed Bouri, Dong Liu, Kyuhwa Lee

Objective. Brain-machine interfaces (BMIs) have been proposed in closed-loop applications for neuromodulation and neurorehabilitation. This study describes the impact of different feedback modalities on the performance of an EEG-based BMI that decodes moto ...

Institute of Physics2017