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Analysing Vibrotactually Stimulated EEG Signals to Comprehend Object Shapes

Publications associées (61)

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Predictors of real-time fMRI neurofeedback performance and improvement-A machine learning mega-analysis

Dimitri Nestor Alice Van De Ville, Frank Scharnowski, Yury Koush, Kirsten Emmert

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

A Multifaceted Approach to Covert Attention Brain-Computer Interfaces

Christoph Schneider

Over the last years, brain-computer interfaces (BCIs) have shown their value for assistive technology and neurorehabilitation. Recently, a BCI-approach for the rehabilitation of hemispatial neglect has been proposed on the basis of covert visuospatial atte ...

EPFL2018

Background: One of the current challenges in brain-machine interfacing is to characterize and decode upper limb kinematics from brain signals, e.g. to control a prosthetic device. Recent research work states that it is possible to do so based on low freque ...

BioMed Central2017

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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

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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

Quantifying the role of motor imagery in brain-machine interfaces

Olaf Blanke, Hannes Bleuler, Andrea Serino, Michela Bassolino, Silvia Marchesotti

Despite technical advances in brain machine interfaces (BMI), for as-yet unknown reasons the ability to control a BMI remains limited to a subset of users. We investigate whether individual differences in BMI control based on motor imagery (MI) are related ...

2016

Asynchronous Decoding of Error Potentials During the Monitoring of a Reaching Task

Ricardo Andres Chavarriaga Lozano, Inaki Asier Iturrate Gil

Brain-machine interfaces (BMIs) have demonstrated how they can be used for reaching tasks with both invasive and non-invasive signal recording methods. Despite the constant improvements in this field, there still exist diverse factors to overcome before ac ...

2015

Structured Sampling and Recovery of iEEG Signals

Volkan Cevher, Mahsa Shoaran, Cosimo Aprile, Yusuf Leblebici, Luca Baldassarre

Wireless implantable devices capable of monitoring the electrical activity of the brain are becoming an important tool for understanding, and potentially treating, mental diseases such as epilepsy and depression. Compressive sensing (CS) is emerging as a p ...

2015

Calibration-Free BCI Based Control

Inaki Asier Iturrate Gil

Recent works have explored the use of brain signals to directly control virtual and robotic agents in sequential tasks. So far in such brain-computer interfaces (BCI), an explicit calibration phase was required to build a decoder that translates raw electr ...

2014

Neurotechnology for Brain Repair

Andrea Biasiucci

Neurotechnology is the application of scientific knowledge to the practical purpose of understanding, interacting and/or repairing the brain or, in a broader sense, the nervous system. The development of novel approaches to decode functional information fr ...

EPFL2014