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Objective. When humans perceive an erroneous action, an EEG error-related potential (ErrP) is elicited as a neural response. ErrPs have been largely investigated in discrete feedback protocols, where actions are executed at discrete steps, to enable seamle ...

People that cannot communicate due to neurological disorders would benefit from an internal speech decoder. Here, we showed the ability to classify individual words during imagined speech from electrocorticographic signals. In a word imagery task, we used ...

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 ...

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 ...

Hand sensorimotor impairments are among the most common consequences of injuries affecting the central and peripheral nervous systems, leading to a drastic reduction in the quality of life for affected individuals. Combining wearable robotic exoskeletons a ...

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 ...

Error-related EEG potentials (ErrP) can be used for brain-machine interfacing (BMI). Decoding of these signals, indicating subject's perception of erroneous system decisions or actions can be used to correct these actions or to improve the overall interfac ...

Recent developments in brain-machine interfaces (BMIs) have proposed the use of errorrelated potentials as cognitive signal that can provide feedback to control devices or to teach them how to solve a task. Due to the nature of these signals, all the propo ...

The brain strengthens memories through consolidation, defined as resistance to interference (stabilization) or performance improvements between the end of a practice session and the beginning of the next (offline gains) [1]. Typically, consolidation has be ...

One of the main problems of both synchronous and asynchronous EEG-based BCIs is the need of an initial calibration phase before the system can be used. This phase is necessary due to the high non-stationarity of the EEG, since it changes between sessions a ...

Error-related potentials (ErrP) have been increasingly studied in psychophysical experiments as well as for brain-machine interfacing. In the latter case, the generalisation capabilities of ErrP decoders is a crucial element to avoid frequent recalibration ...

Brain-machine interfaces (BMI) usually decode movement parameters from cortical activity to control neuroprostheses. This requires subjects to learn to modulate their brain activity to convey all necessary information, thus imposing natural limits on the c ...

Motor imagery (MI) has been largely studied as a way to enhance motor learning and to restore motor functions. Although it is agreed that users should emphasize kinesthetic imagery during MI, recordings of MI brain patterns are not sufficiently reliable fo ...

One of the challenges in using brain computer interfaces over extended periods of time is the uncertainty in the system. This uncertainty can be due to the user's internal states, the non stationarity of the brain signals, or the variation of the class dis ...

Brain–computer interfaces (BCI) (also referred to as brain–machine interfaces; BMI) are, by definition, an interface between the human brain and a technological application. Brain activity for interpretation by the BCI can be acquired with either invasive ...

This paper presents a new approach for self-calibration BCI for reaching tasks using error-related potentials. The proposed method exploits task constraints to simultaneously calibrate the decoder and control the device, by using a robust likelihood functi ...

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 ...

Interactive learning deals with the problem of learning and solving tasks using human instructions. It is common in human-robot interaction, tutoring systems, and in human-computer interfaces such as brain-computer ones. In most cases, learning these tasks ...

Brain-computer interfaces (BCI) have been shown to be a promising tool in rehabilitation and assistive scenarios. Within these contexts, brain signals can be decoded and used as commands for a robotic device, allowing to translate user’s intentions into mo ...

Error-related potentials (ErrP) have been recently incorporated in brain-machine interfaces (BMIs) due to its ability to adapt and correct both the output of the BMI or the behavior of the machine. Most of these applications rely on synchronous tasks with ...

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Invariability of EEG error-related potentials during continuous feedback protocols elicited by erroneous actions at predicted or unpredicted states

Word pair classification during imagined speech using direct brain recordings

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

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

mano: A Wearable Hand Exoskeleton for Activities of Daily Living and Neurorehabilitation

Calibration-Free BCI Based Control

Decoding Fast-Paced Error-Related Potentials in Monitoring Protocols

Asynchronous detection of error potentials

A Rapid Form of Offline Consolidation in Skill Learning

Minimizing calibration time of single-trial recognition of error potentials in brain-computer interfaces

Spatial filters yield stable features for error-related potentials across conditions

Teaching brain-machine interfaces as an alternative paradigm to neuroprosthetics control

Sensory threshold neuromuscular electrical stimulation fosters motor imagery performance

Prediction of Command Delivery Time for BCI

Workshops of the Sixth International Brain–Computer Interface Meeting: brain–computer interfaces past, present, and future

Exploiting task constraints for self-calibrated brain-machine interface control using error-related potentials

Inferring subjective preferences on robot trajectories using EEG signals

Interactive Learning from Unlabeled Instructions

Detecting intention to grasp during reaching movements from EEG

Brain connectivity in continuous error tasks