Publications associées à EEG error-related potentials encode magnitude of errors and individual perceptual thresholds

Publications associées (21)

MEP and TEP features variability: is it just the brain-state?

Friedhelm Christoph Hummel, Estelle Emeline Raffin, Sylvain Jean-François Harquel, Andéol Geoffroy Cadic-Melchior, Michele Bevilacqua, Claudia Bigoni

Objective. The literature investigating the effects of alpha oscillations on corticospinal excitability is divergent. We believe inconsistency in the findings may arise, among others, from the electroencephalography (EEG) processing for brain-state determi ...

Iop Publishing Ltd2024

Sex modulation of faces prediction error in the autistic brain

Sylvain Jean-François Harquel

Recent research suggests that autistic females may have superior socio-cognitive abilities compared to autistic males, potentially contributing to underdiagnosis in females. However, it remains unclear whether these differences arise from distinct neurophy ...

Nature Portfolio2024

Predicting Visual Stimuli From Cortical Response Recorded With Wide-Field Imaging in a Mouse

Silvestro Micera

Neural decoding of the visual system is a subject of research interest, both to understand how the visual system works and to be able to use this knowledge in areas, such as computer vision or brain-computer interfaces. Spike-based decoding is often used, ...

Ieee-Inst Electrical Electronics Engineers Inc2024

Human-machine interface for two-dimensional steering control with the auricular muscles

Daniel Jose Lins Leal Pinheiro, Silvestro Micera, Solaiman Shokur

Human-machine interfaces (HMIs) can be used to decode a user's motor intention to control an external device. People that suffer from motor disabilities, such as spinal cord injury, can benefit from the uses of these interfaces. While many solutions can be ...

FRONTIERS MEDIA SA2023

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

Editorial: Getting Neuroprosthetics Out of the Lab: Improving the Human-Machine Interactions to Restore Sensory-Motor Functions

Solaiman Shokur, Ivo Strauss

FRONTIERS MEDIA SA2022

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

José del Rocio Millán Ruiz, Serafeim Perdikis, Luca Tonin, Kyuhwa Lee, Bastien Orset

Mind-controlled wheelchairs are an intriguing assistive mobility solution applicable in complete paralysis. Despite progress in brain-machine interface (BMI) technology, its translation remains elusive. The primary objective of this study is to probe the h ...

Cell Press2022

Does one size fit all? - Towards the optimization and personalization of non-invasive brain stimulation paradigms to enhance motor learning

Laurijn Rianne Draaisma

The acquisition and re-acquisition of motor skills is an important aspect of daily life and in the recovery after a stroke. Non-invasive brain stimulation (NIBS) is a technique that is used to improve motor learning and enhance motor recovery in stroke sur ...

EPFL2022

EpilepsyGAN: Synthetic Epileptic Brain Activities with Privacy Preservation

David Atienza Alonso, Amir Aminifar, Alireza Amirshahi, Damián Pascual Ortiz

Epilepsy is a chronic neurological disorder affecting more than 65 million people worldwide and manifested by recurrent unprovoked seizures. The unpredictability of seizures not only degrades the quality of life of the patients, but it can also be life-thr ...

2021

Effective connectivity in brain networks estimated using EEG signals is altered in children with ADHD

Amir Hossein Omidvarnia

This study presents a methodology developed for estimating effective connectivity in brain networks (BNs) using multichannel scalp EEG recordings. The methodology uses transfer entropy as an information transfer measure to detect pair-wise directed informa ...

PERGAMON-ELSEVIER SCIENCE LTD2021

Probing regional cortical excitability via input-output properties using transcranial magnetic stimulation and electroencephalography coupling

Estelle Emeline Raffin

The modular organization of the cortex refers to subsets of highly interconnected nodes, sharing specific cytoarchitectural and dynamical properties. These properties condition the level of excitability of local pools of neurons. In this study, we describe ...

Wiley2020

The Role of the Control Framework for Continuous Teleoperation of a Brain-Machine Interface-Driven Mobile Robot

Felix Christian Bauer, José del Rocio Millán Ruiz, Luca Tonin

Despite the growing interest in brain-machine interface (BMI)-driven neuroprostheses, the translation of the BMI output into a suitable control signal for the robotic device is often neglected. In this article, we propose a novel control approach based on ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

Modulation of epileptic networks by transient interictal epileptic activity: A dynamic approach to simultaneous EEG-fMRI

Dimitri Nestor Alice Van De Ville, Maria Giulia Preti

Epileptic networks, defined as brain regions involved in epileptic brain activity, have been mapped by functional connectivity in simultaneous electroencephalography and functional magnetic resonance imaging (EEG-fMRI) recordings. This technique allows to ...

2020

An area and power efficient on-the-fly LBCS transformation for implantable neuronal signal acquisition systems

Yusuf Leblebici, Volkan Cevher, Cosimo Aprile, Luca Baldassarre

A power and area efficient hardware encoding system tailored for wireless implantable applications is presented. Constant medical monitoring allowed by implantable devices is the most relevant alternative to current bulky monitoring systems, which, in case ...

ASSOC COMPUTING MACHINERY2018

A reconfigurable interactive interface for controlling robotic origami in virtual environments

Jamie Paik, Harshal Arun Sonar, Zhenishbek Zhakypov, Jian-Lin Huang

Origami shape transformation is dictated by predefined folding patterns and their folding sequence. The working principle of robotic origami is based on the same principle: we design quasi-two-dimensional tiles and connecting hinges and define and program ...

SAGE Publications2018

Decoding of Self-paced Lower-Limb Movement Intention: A Case Study on the Influence Factors

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Dong Liu

Brain-machine interfaces (BMIs) have been applied as new rehabilitation tools for motor disabled individuals. Active involvement of cerebral activity has been shown to enhance neuroplasticity and thus to restore mobility. Various studies have focused on th ...

Frontiers Research Foundation2017

Neural Correlates of Sequential Decision Making

Michael Herzog, He Xu

In chess, a series of moves is made until a delayed sparse feedback (win, loss) is issued, which makes it impossible to evaluate the value of a single move. There are powerful reinforcement learning (RL) algorithms, which can cope with these sequential dec ...

2017

EEG-based decoding of error-related brain activity in a real-world driving task

José del Rocio Millán Ruiz, Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil, Huaijian Zhang, Lucian Andrei Gheorghe, Zahra Khaliliardali

Objectives. Recent studies have started to explore the implementation of brain–computer interfaces (BCI) as part of driving assistant systems. The current study presents an EEG-based BCI that decodes error-related brain activity. Such information can be us ...

Institute of Physics2015

EEG frequency tagging dissociates target and flanker processing in crowding

Michael Herzog, Vitaly Chicherov

Flankers can strongly deteriorate performance on a target (crowding). The neural mechanisms of crowding are largely unknown. We have recently shown that the N1 component of the EEG is suppressed during crowding. Because it is difficult to disentangle the n ...

ASSOCIATION FOR RESEARCH IN VISION & OPHTHALMOLOGY2014

Classification of stance and swing gait states during treadmill walking from non-invasive scalp electroencephalographic (EEG) signals

Pierre-André Farine, Sara Grassi Pauletti

In Contreras-Vidal and colleagues have shown the feasibility of inferring the linear and angular kinematics of treadmill walking from scalp EEG. Here, we apply a discrete approach to the same problem of decoding the human gait. By reducing the gait process ...

Springer2013