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Classification of upper limb center-out reaching tasks by means of EEG-based continuous decoding techniques

Related publications (78)

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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

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Objective. Recent results have shown the potentials of neural interfaces to provide sensory feedback to subjects with limb amputation increasing prosthesis usability. However, their advantages for decoding motor control signals over current methods based o ...

IOP Publishing Ltd2021

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

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Real-time fMRI neurofeedback is an increasingly popular neuroimaging technique that allows an individual to gain control over his/her own brain signals, which can lead to improvements in behavior in healthy participants as well as to improvements of clinic ...

2021

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 INC2021

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

Silvestro Micera, Fiorenzo Artoni, Stefano Tortora

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

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

Cortical reactive balance responses to unexpected slippages while walking: a pilot study

Silvestro Micera, Daniela De Venuto

Understanding how the human brain cortex behaves when the dynamical balance is unexpectedly challenged can be useful to enable fall prevention strategies during daily activities. In this respect, we designed and tested a novel methodological approach to ea ...

IEEE2019

First Biopotential Recordings from a Liquid Crystal Optrode

Han Wang

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

Analysing Vibrotactually Stimulated EEG Signals to Comprehend Object Shapes

Anwesha Khasnobish

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