On the use of interaction error potentials for adaptive brain computer interfaces

Llera, Alberto; Gerven, Marcel A. J. van; Gómez, Vicenç; Jensen, Ole; Kappen, Hilbert J.

doi:10.1016/j.neunet.2011.05.006

Cited by 59 publications

(45 citation statements)

References 21 publications

(36 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is very likely that in progressive neurological disorders, the parameters selected in global multimodal plots will have to be modified or adapted accordingly. This is in agreement with adaptive methods which are being developed currently with the goal of improving the classifiers 5 . Also, our approach will have to be tested in a large sample of patients in the future in order to demonstrate its real clinical usefulness in daily practice.…”

Section: Resultssupporting

confidence: 88%

See 1 more Smart Citation

Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs

2013

View full text Add to dashboard Cite

Tremor is the most common movement disorder encountered during daily neurological practice. Tremor in the upper limbs causes functional disability and social inconvenience, impairing daily life activities. The response of tremor to pharmacotherapy is variable. Therefore, a combination of drugs is often required. Surgery is considered when the response to medications is not sufficient. However, about one third of patients are refractory to current treatments. New bioengineering therapies are emerging as possible alternatives. Our study was carried out in the framework of the European project “Tremor” (ICT-2007-224051). The main purpose of this challenging project was to develop and validate a new treatment for upper limb tremor based on the combination of functional electrical stimulation (FES; which has been shown to reduce upper limb tremor) with a brain-computer interface (BCI). A BCI-driven detection of voluntary movement is used to trigger FES in a closed-loop approach. Neurological tremor is detected using a matrix of EMG electrodes and inertial sensors embedded in a wearable textile. The identification of the intentionality of movement is a critical aspect to optimize this complex system. We propose a multimodal detection of the intentionality of movement by fusing signals from EEG, EMG and kinematic sensors (gyroscopes and accelerometry). Parameters of prediction of movement are extracted in order to provide global prediction plots and trigger FES properly. In particular, quality parameters (QPs) for the EEG signals, corticomuscular coherence and event-related desynchronization/synchronization (ERD/ERS) parameters are combined in an original algorithm which takes into account the refractoriness/responsiveness of tremor. A simulation study of the relationship between the threshold of ERD/ERS of artificial EEG traces and the QPs is also provided. Very interestingly, values of QPs were much greater than those obtained for the corticomuscular module alone.

show abstract

Section: Resultssupporting

confidence: 88%

“…Why use a multimodal detection of the intentionality of movement? Although the potential for BCIs in neurological disorders is huge, the applicability of current BCI systems has been limited by several factors 5 . One of them is the poor performance of BCIs based on conventional EEG analysis.…”

Section: Introductionmentioning

confidence: 99%

Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs

2013

View full text Add to dashboard Cite

show abstract

“…This finding has created attractive opportunities for engineering applications, specifically in the field of Brain-Computer Interfaces (BCI), which allow a person to communicate solely via brain activity. It has been shown that ErrPs can be automatically detected when a BCI delivers erroneous feedback [6] and that this information can be used for correction or adaptation of BCIs [7]. Typical ErrP single-trial classification accuracies are around 70-80% and relatively stable across recording sessions; ErrP classifiers have shown to still perform well up to 600 days after initial calibration [8] and can be applied across different task sets [9].…”

Section: Related Workmentioning

confidence: 99%

A neuro-based method for detecting context-dependent erroneous robot action

Ehrlich

Cheng

2016

2016 IEEE-RAS 16th International Conference on Humanoid Robots (Humanoids)

View full text Add to dashboard Cite

Abstract-Validating appropriateness and naturalness of human-robot interaction (HRI) is commonly performed by taking subjective measures from human interaction partners, e.g. questionnaire ratings. Although these measures can be of high value for robot designers, they are very sensitive and can be inaccurate and/or biased. In this paper we propose and validate a neuro-based method for objectively validating robot behavior in HRI. We propose to detect from the electronencephalogram (EEG) of a human interaction partner, the perception of inappropriate / unexpected / erroneous robot behavior. To validate this method, we conducted an EEG experiment with a simplified HRI protocol in which a humanoid robot displayed context-dependent erroneous behavior from time to time. The EEG data taken from 13 participants revealed biologically plausible error-related potentials (ErrP) whose spatio-temporal distributions match well with related neuroscientific research. We further demonstrate that perceived erroneous robot action can reliably be modeled and detected from human EEG signals with classification accuracies on avg. 69.7±9.1%. These findings confirm principal feasibility of the proposed method and suggest that EEG-based ErrP detection can be used for quantitative evaluation and thus improvement of robot behavior.

show abstract

“…Furthermore, these modulations, termed error-related potentials, ErrP, can be reliably decoded in single-trial. This provides information about user perception of errors during brain-machine interaction bringing the possibility of correcting such errors [2], [3] or adapting the system to prevent these errors to reocurr [4], [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Decoding fast-paced error-related potentials in monitoring protocols

Chavarriaga

Iturrate

Wannebroucq

et al. 2015

2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

View full text Add to dashboard Cite

Abstract-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 interfacing system. Multiple studies have shown the feasibility of decoding these potentials in single-trial using different types of experimental protocols and feedback modalities. However, previously reported approaches are limited by the use of long inter-stimulus intervals (ISI > 2 s). In this work we assess if it is possible to overcome this limitation. Our results show that it is possible to decode error-related potentials elicited by stimuli presented with ISIs lower than 1 s without decrease in performance. Furthermore, the increase in the presentation rate did not increase the subject workload. This suggests that the presentation rate for ErrP-based BMI protocols using serial monitoring paradigms can be substantially increased with respect to previous works.

show abstract

On the use of interaction error potentials for adaptive brain computer interfaces

Cited by 59 publications

References 21 publications

Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs

Quality parameters for a multimodal EEG/EMG/kinematic brain-computer interface (BCI) aiming to suppress neurological tremor in upper limbs

A neuro-based method for detecting context-dependent erroneous robot action

Decoding fast-paced error-related potentials in monitoring protocols

Contact Info

Product

Resources

About