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Prediction of Command Delivery Time for BCI

Prediction of Command Delivery Time for BCI

引用

IEEE International Conference on Systems, Man, and Cybernetics

作者： S. Saeedi R. Chavarriaga I. Iturrate J. d. R. Millan T. Carlson Chair in Non-Invasive Brain-Machine Interface (CNBI) Center for Neuroprosthetics School of Engineering Aspire Create Institute of Orthopaedics and Musculoskeletal Science University College London

ISBN: (纸本)9781479938414

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 discriminative information over time. Therefore, the users are often unable to maintain the same accuracy and time efficiency in delivering BCI commands. In this paper, we tackle the issue of variation in BCI command delivery time for a motor imagery task with the aim of providing assistance through adaptive shared control. This is important mainly because having long delivery of mental commands leads to uncertainty in the user's intent classification and limits the responsiveness of the system. In order to address this issue, we separate the trials into long and short groups so that we have the same number of trials in each group. We demonstrate that using only a few samples at the beginning of the trial, we are able to predict whether the current trial will be short or long with high accuracies (70% - 86%). Eventually, this prediction enables us to tune the shared control parameters to overcome the issue of uncertainty.

关键词： brain computer interface (BCI) EEG Adaptive shared controls

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BCI and motion capture technologies for rehabilitation based on videogames

BCI and motion capture technologies for rehabilitation based...

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IEEE Global Humanitarian Technology Conference (GHTC)

作者： John E. Muñoz Ricardo Chavarriaga Julian F. Villada David SebastianLopez Universidad Tecnologica de Pereira Pereira CO Defitech Chair in Non-Invasive Brain Machine Interface EcolePolytechniqueFédérale de Lausanne Lausanne Switzerland Human Computer Interaction Group Universidad Tecnológica de Pereira Pereira Colombia

This paper presents a cost-effective rehabilitation system based on videogames and multimodal recordings of physiological signals. The system targets patients with sensory-motor impairments resulting from lesions of the central nervous system (e.g., due to stroke or traumatic injuries). It relies on a wireless low-cost hybrid interface combining a consumer-level electroencephalographic (EEG) device and the Kinect sensor to record the motion capture information. Thus providing quantitative physiological measures to support medical evaluations and improve the personalization of health service. Furthermore, through the design of specialized videogames for rehabilitation, this approach aim at increasing the patient's motivation, potentially improving the service quality and the recovery process. The system is currently being used in a rehabilitation center in Colombia by patients with upper limb paralysis and balance disorders after stroke or traumaticuries. Initial results show significant improvements in the mobility of affected joints, improved adherence to treatments by patients, and high acceptability by therapists and end-users.

关键词： Biomechanics Physiology Medical treatment Biomedical imaging

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EEG correlates of active visual search during simulated driving: An exploratory study

EEG correlates of active visual search during simulated driv...

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IEEE International Conference on Systems, Man, and Cybernetics

作者： Hadrien Renold Ricardo Chavarriaga Lucian Gheorghe Jose del R. Millan Chair on Non-Invasive Brain-Machine Interface (CNBI) Center for Neuroprosthetics School of Engineering Ecole Polytechnique Federale de Lausanne Mobility Services Laboratory Nissan Research Center Nissan Motor Co. LTD.

ISBN: (纸本)9781479938414

brain responses to visual stimuli can provide information about visual recognition processes. Several studies have shown stimulus-dependent modulation of the evoked neural responses after gaze shifts (i.e. eye fixation related potentials, EFRP) depending on the relevance of the fixated object. However these studies are typically performed on still images under constrained conditions. Here we extend this approach to study overt visual attention during a simulated driving task. Simultaneous analysis of eye-tracking and electroencephalography data revealed similar patterns than those previously reported. However, natural visual exploration yielded shorter fixations, which imposes constraints in the analysis of the elicited brain responses. Nevertheless, we found significant differences between EFRPs corresponding to target objects or non-object stimuli. These results suggest the possibility of decoding such information during driving, allowing better understanding of how drivers process the environmental information.

关键词： Visualization Electroencephalography Dispersion Vehicles Tracking Protocols Electrodes

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Steering Timing Prediction in a Driving Simulator Task

Steering Timing Prediction in a Driving Simulator Task

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Lucian Gheorghe Ricardo Chavarriaga Jose del R. Millan Defitech Foundation Chair in Non-Invasive Brain-Machine Interface Center for Neuroprosthetics

ISBN: (纸本)9781457702150

In this paper we present the preliminary results of a pioneering attempt to predict the timing of steering actions in a driving task from non-invasive EEG measurements. The experiment took place with the subjects driving a car at a constant speed on a simulated highway in a driving simulator. The EEG activity was analyzed during periods of straight driving and during lane change actions. Classifiers were built on the signals recorded over the motor areas for straight and pre-steering periods. The onset of the steering actions was detected on average 811ms before the action with a 74.6% true positive rate.

关键词： Driving simulator Steering fixed speed timing sequence Cars Action Straight

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Inferring Driver's Turning Direction through Detection of Error Related brain Activity

Inferring Driver's Turning Direction through Detection of Er...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Huaijian Zhang Ricardo Chavarriaga Lucian Gheorghe Jose del R. Millan Defitech Foundation Chair in Non-Invasive Brain-Machine Interface Center for Neuroprosthetics

ISBN: (纸本)9781457702150

This work presents EEG-based brain-computer interface (BCI) that uses error related brain activity to improve the prediction of driver's intended turning direction. In experiments while subjects drive in a realistic car simulator, we show a directional cue before reaching intersection, and analyze error related EEG potential to infer if the presented direction coincides with the driver's intention. In this protocol, the directional cue provides an initial estimation of the driving direction (based on EEG, environmental or previous driving habits), and we focus on the recognition of error-potentials it may elicit. Experiments with 7 healthy human subjects yield an average classification 0.69±0.16, which confirms the feasibility of decoding these signals to help estimating driver's turning direction. This study can be further exploited by intelligent cars to tune their driving assistant systems to improve their performance and enhance the driving experience.

关键词： brain activity driver circuits driving direction Driving experience human subject Cars driving assistant

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Offline Decoding of Upper Limb Muscle Synergies from EEG Slow Cortical Potentials

Offline Decoding of Upper Limb Muscle Synergies from EEG Slo...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Nicolas J. Beuchat Ricardo Chavarriaga Sarah Degallier Jose del R. Millan Defitech Chair on Non-Invasive Brain-Machine Interface Center for Neu-roprosthetics Ecole

ISBN: (纸本)9781457702150

Muscle synergies are thought to be the building blocks used by the central nervous system to control the underdetermined problem of muscles activation. Decoding these synergies from EEG could provide useful tools for BCI-controlled orthotic devices. In this paper, we assess the possibility of decoding muscle synergies from EEG slow cortical potentials in two healthy subjects and two stroke patients performing a center-out reaching task. We were able to successfully decode the extracted muscle synergies in both healthy subject and one patient.

关键词： DECODING Healthy Volunteers Muscle of upper limb stroke patient muscles Building blocks OFF LINE Cortex of kidney Central Nervous System

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Three-dimensional upper limb movement decoding from EEG signals

Three-dimensional upper limb movement decoding from EEG sign...

引用

International Winter Workshop on brain-Computer interface (BCI)

作者： Jeong-Hun Kim Ricardo Chavarriaga José del R. Millán Seong-Whan Lee Department of Brain and Cognitive Engineering Korea University Seoul South Korea Defitech Chair in Non-Invasive Brain-Machine Interface EPFL Lausanne Switzerland

A brain-computer interface (BCI) can be used to control a limb neuroprosthesis in patients. In particular, decoding trajectory of upper limb with motor imagery (MI) can support motor rehabilitation using a wearable robotic arm. Recent research shows the possibility of decoding hand movement trajectory from electroencephalography (EEG) signals. However, such studies are insufficient to apply motor rehabilitation, which are only considered hand movement trajectory. Although disabilities patients take correct hand movement, sometimes wrong elbow movement can be taken in motor rehabilitation. In this study, we explore to decode velocity of both hand and elbow at the same time from EEG signals when subjects move upper limb. The result shows feasibility toward controlling robotic arm.

关键词： Decoding Electroencephalography Elbow Trajectory Accuracy Mobile robots

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Design of a robotic wheelchair with a motor imagery based brain-computer interface

Design of a robotic wheelchair with a motor imagery based br...

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International Winter Workshop on brain-Computer interface (BCI)

作者： Keun-Tae Kim Tom Carlson Seong-Whan Lee Department of Brain and Cognitive Engineering Korea University Seoul South Korea Defitech Chair in Non-Invasive Brain-Machine Interface EPFL Lausanne Switzerland

This paper presents a prototype for an electro-encephalogram (EEG) based brain-actuated wheelchair system using motor imagery. To overcome some of the limitations of other previous works, such as gaze dependence and unnecessary stops, five commands (left, left-diagonal, right, right-diagonal, and forward) were decoded based on the motor imagery correlates in EEG signals. Also, the system was modularized into three components: BCI control, and network. On the basis of the conclusions, we can expect a robust brain-actuated wheelchair system, which can allow the user's intention to control the wheelchair in multi-directional movements, thereby increasing the user's authority compared with many of the alternative approaches.

关键词： Wheelchairs Electroencephalography Mobile robots Foot Training Permanent magnet motors

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Making the most of context-awareness in brain-computer interfaces

Making the most of context-awareness in brain-computer inter...

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IEEE International Conference on Cybernetics

作者： Sareh Saeedi Tom Carlson Ricardo Chavarriaga José R. del Millán Chair in Non-Invasive Brain-Machine Interface (CNBI) EPFL Lausanne Switzerland

In order for brain-computer interfaces (BCIs) to be used reliably for extended periods of time, they must be able to adapt to the users evolving needs. This adaptation should not only be a function of the environmental (external) context, but should also consider the internal context, such as cognitive states and brain signal reliability. In this work, we propose three different shared control frameworks that have been used for BCI applications: contextual fusion, contextual gating, and contextual regulation. We review recently published results in the light of these three context-awareness frameworks. Then, we discuss important issues to consider when designing a shared controller for BCI.

关键词： Context Control systems Reliability Navigation Wheelchairs Mobile robots

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A Hybrid BCI for Enhanced Control of a Telepresence Robot

A Hybrid BCI for Enhanced Control of a Telepresence Robot

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Tom Carlson Luca Tonin Serafeim Perdikis Robert Leeb Jose del R. Millan Chair in Non-Invasive Brain-Machine Interface Center for Neuropros-thetics School of Engineering

ISBN: (纸本)9781457702150

Motor-disabled end users have successfully driven a telepresence robot in a complex environment using a brain-Computer interface (BCI). However, to facilitate the interaction aspect that underpins the notion of telepresence, users must be able to voluntarily and reliably stop the robot at any moment, not just drive from point to point. In this work, we propose to exploit the user's residual muscular activity to provide a fast and reliable control channel, which can start/stop the telepresence robot at any moment. Our preliminary results show that not only does this hybrid approach increase the accuracy, but it also helps to reduce the workload and was the preferred control paradigm of all the participants.

关键词： REMOTE CONTROL Robot Moment of force End users Workload

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