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BMI: Lessons from tests with impaired users

BMI: Lessons from tests with impaired users

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International Winter Workshop on brain-Computer interface (BCI)

作者： José del R Millán Defitech Foundation Chair in Non-Invasive Brain-Machine Interface Center for Neuroprosthetics & Institute of Bioengineering Switzerland

brain-machine interfaces (BMI) have largely been demonstrated in laboratory conditions involving, mainly, healthy users. We have recently carried out a series of studies with a substantial number of motor-disabled end-users operating different brain-controlled devices in ecological conditions and without the assistance of BMI experts.

关键词： Electroencephalography Europe Laboratories System-on-chip Mobile robots Biomedical engineering

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Rewards-Driven Control of Robot Arm by Decoding EEG Signals

Rewards-Driven Control of Robot Arm by Decoding EEG Signals

引用

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

作者： Ajay Kumar Tanwani Jose del. R. Millan Aude Billard Learning Algorithms and Systems Laboratory (LASA) Ecole Polytechnique Federale de Lausanne (EPFL) Defitech Chair on Non-Invasive Brain-Machine Interface (CNBI) Center for Neuropros-thetics in EPFL

ISBN: (纸本)9781424479276

Decoding the user intention from non-invasive EEG signals is a challenging problem. In this paper, we study the feasibility of predicting the goal for controlling the robot arm in self-paced reaching movements, i.e., spontaneous movements that do not require an external cue. Our proposed system continuously estimates the goal throughout a trial starting before the movement onset by online classification and generates optimal trajectories for driving the robot arm to the estimated goal. Experiments using EEG signals of one healthy subject (right arm) yield smooth reaching movements of the simulated 7 degrees of freedom KUKA robot arm in planar center-out reaching task with approximately 80% accuracy of reaching the actual goal.

关键词： Electroencephalography Robots Decoding Trajectory Accuracy Correlation Training

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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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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...

引用

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...

引用

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...

引用

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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Improved Recognition of Error Related Potentials through the use of brain Connectivity Features

Improved Recognition of Error Related Potentials through the...

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

作者： Huaijian Zhang Ricardo Chavarriaga Mohit Kumar Goel Lucian Gheorghe José del R. Millán Defitech Foundation Chair in Non-Invasive Brain-Machine Interface Center for Neuroprosthetics école Polytechnique Fédérale de Lausanne (EPFL) Switzerland. Mobility Services Laboratory Nissan Research Center Nissan Motor Co. Japan

ISBN: (纸本)9781424441198

brain error processing plays a key role in goaldirected behavior and learning in human brain. Directed transfer function (DTF) on EEG signal brings unique features for discrimination between correct and error cases in braincomputer interface (BCI) system. We describe the first application of brain connectivity features for recognizing error-related signals in non-invasive BCI. EEG signal were recorded from 16 human subjects when they monitored stimuli moving in either correct or erroneous direction. Classification performance using waveform features, brain connectivity features and their combination were compared. The result of combined features yielded highest classification accuracy, 0:85. In addition, we also show that brain connectivity at theta band around 200ms after stimuli carry highly discriminant information between error and correct trials. This paper provides evidence that the use of connectivity features improve the performance of an EEG based BCI.

关键词： Improved Recognition Error Related Potentials through

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Detection of Anticipatory brain Potentials during Car Driving

Detection of Anticipatory Brain Potentials during Car Drivin...

引用

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

作者： Zahra Khaliliardali Ricardo Chavarriaga Lucian Andrei Gheorghe José del R. Millán Defitech Chair in Non-Invasive Brain-Machine Interface School of Engineering école Polytechnique Fédérale de Lausanne (EPFL) Switzerland CH-1015 Nissan Motor Co. LTD. Nissan Research Center Mobility Services Laboratory

ISBN: (纸本)9781424441198

Recognition of driver’s intention from electroencephalogram (EEG) can be helpful in developing an in-car brain computer interface (BCI) systems for intelligent cars. This could be beneficial in enhancing the quality of interaction between the driver and the car to provide the response of the intelligent cars in line with driver’s intention. We proposed investigating anticipation as the cognitive state leading to specific actions during car driving. An experimental protocol is designed for recording EEG from 6 subjects while driving the virtual reality driving simulator. The experimental protocol is a variant of the contingent negative variation (CNV) paradigm with Go and No-go conditions in driving framework. The results presented in this study support the presence of the slow cortical anticipatory potentials in EEG grand averages and also confirm the discriminability of these potentials in offline single trial classification with the average of 0:76 ± 0:12 in area under the curve (AUC).

关键词： driver’s intention electroencephalogram (EEG) specific actions during

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