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arXiv 2019年

作者： Tortora, Stefano Beraldo, Gloria Tonin, Luca Menegatti, Emanuele University of Padova Italy Chair in Non-Invasive Brain-Machine Interface Center for Neuroprosthetics School of Engineering Ecole Polytechnique Fdral de Lausanne Lausanne Switzerland

The identification of intentionally delivered commands is a challenge in brain Computer interfaces (BCIs) based on Sensory-Motor Rhythms (SMR). It is of fundamental importance that BCI systems controlling a robotic device (i.e., upper limb prosthesis) are capable of detecting if the user is in the so called Intentional non-Control (INC) state (i.e., holding the prosthesis in a given position). In this work, we propose a novel approach based on the entropy of the Electroencephalogram (EEG) signals to provide a continuous identification of motion intention. Results from ten healthy subjects suggest that the proposed system can be used for reliably predicting motion in real-time at a framerate of 8 Hz with 80% ±5% of accuracy. Moreover, motion intention can be detected more than 1 second before muscular activation with an average accuracy of 76% ± 11%. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography

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Entropy-based Motion Intention Identification for brain-Computer interface

Entropy-based Motion Intention Identification for Brain-Comp...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Stefano Tortora Gloria Beraldo Luca Tonin Emanuele Menegatti Intelligent Autonomous Systems Lab (IAS-Lab) University of Padova Italy Chair in Non-Invasive Brain-Machine Interface Center for Neuroprosthetics School of Engineering Ecole Polytechnique Fdral de Lausanne Lausanne Switzerland

The identification of intentionally delivered commands is a challenge in brain Computer interfaces (BCIs) based on Sensory-Motor Rhythms (SMR). It is of fundamental importance that BCI systems controlling a robotic device (i.e., upper limb prosthesis) are capable of detecting if the user is in the so called Intentional non-Control (INC) state (i.e., holding the prosthesis in a given position). In this work, we propose a novel approach based on the entropy of the Electroencephalogram (EEG) signals to provide a continuous identification of motion intention. Results from ten healthy subjects suggest that the proposed system can be used for reliably predicting motion in real-time at a framerate of 8 Hz with 80%±5% of accuracy. Moreover, motion intention can be detected more than 1 second before muscular activation with an average accuracy of 76%±11%.

关键词： Entropy Electroencephalography Electromyography Integrated circuits Games Robot sensing systems

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Modulation of the inter-hemispheric asymmetry of motor-related brain activity using brain-computer interfaces

Modulation of the inter-hemispheric asymmetry of motor-relat...

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

作者： Michael Pereira Aleksander Sobolewski José del R. Millán Chair in Non-invasive Brain-machine Interface Ecole Polytechnique Fédérale de Lausanne 1015 Lausanne Switzerland

ISBN: (纸本)9781424492695

non-invasive brain stimulation has shown promising results in neurorehabilitation for motor-impaired stroke patients, by rebalancing the relative involvement of each hemisphere in movement generation. Similarly, brain-computer interfaces have been used to successfully facilitate movementrelated brain activity spared by the infarct. We propose to merge both approaches by using BCI to train stroke patients to rebalance their motor-related brain activity during motor tasks, through the use of online feedback. In this pilot study, we report results showing that some healthy subjects were able to learn to spontaneously up- and/or down-regulate their ipsilateral brain activity during a single session.

关键词： rebalancing relative involvement spontaneously

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

Asynchronous Decoding of Error Potentials during the Monitor...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jason Omedes Iñaki Iturrate Ricardo Chavarriaga Luis Montesano University of Zaragoza I3A Zaragoza DIIS Spain Ecole Polytechnique Fédérale de Lausanne Chair in Non-Invasive Brain-Machine Interface (CNBI) Switzerland

ISBN: (纸本)9781479986989

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 achieving a natural control. In particular, the high variability of the brain signals often leads to the incorrect decoding of the subject intentions, producing unreliable behaviors in the controlled device. A possible solution to this problem would be that of correcting this erroneous decoding using a feedback signal from the user. In this work, we evaluate the possibility of decoding neural signals associated to performance monitoring (EEG-recorded error-related potentials) during a reaching task. Compared to previous works where these error potentials were recorded under scenarios with discrete movements performed by the cursor, under real conditions the cursor is moving continuously and thus the system is required to asynchronously detect any possible error. To this end, we simulated two different erroneous events during the monitoring of a reaching task: errors at the beginning of the movement, and errors happening in the middle of the trajectory being executed. Through the analysis of the recorded EEG of three subjects, we demonstrate the existence of neural correlates for the two types of elicited error potentials, and we are able to asynchronously detect them with high accuracies.

关键词： Electroencephalography Performance evaluation Decoding Trajectory Feature extraction Monitoring Visualization

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Towards a synchronised Grammars framework for adaptive musical human-robot collaboration

Towards a synchronised Grammars framework for adaptive music...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Miguel Sarabia Kyuhwa Lee Yiannis Demiris Department of Electrical and Electronic Engineering Personal Robotics Lab United Kingdom Ecole Poly technique Fédérale de Lausanne Chair in Non-invasive Brain-machine Interface Lab Switzerland

We present an adaptive musical collaboration framework for interaction between a human and a robot. The aim of our work is to develop a system that receives feedback from the user in real time and learns the music progression style of the user over time. To tackle this problem, we represent a song as a hierarchically structured sequence of music primitives. By exploiting the sequential constraints of these primitives inferred from the structural information combined with user feedback, we show that a robot can play music in accordance with the user's anticipated actions. We use Stochastic Context-Free Grammars augmented with the knowledge of the learnt user's preferences. We provide synthetic experiments as well as a pilot study with a Baxter robot and a tangible music table. The synthetic results show the synchronisation and adaptivity features of our framework and the pilot study suggest these are applicable to create an effective musical collaboration experience.

关键词： Robots Grammar Probability distribution Collaboration Synchronization Real-time systems Prediction algorithms

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brain Correlates of Lane Changing Reaction Time in Simulated Driving

Brain Correlates of Lane Changing Reaction Time in Simulated...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Huaijian Zhang Ricardo Chavarriaga Lucian Gheorghe José Del R. Millán Chair on Non-Invasive Brain-Machine Interface (CNBI) Ecole Polytechnique Federale de Lausanne Switzerland Mobility Services Laboratory Nissan Motor Co Japan Ecole Polytechnique Federale de Lausanne Lausanne VD CH

ISBN: (纸本)9781479986989

Psychophysical studies have reported correlation between neural activity in frontal and parietal areas and subject's reaction time in simple tasks. Here we study whether similar correlates can also be identified in driver's electroencephalography (EEG) activity when they perform steering actions triggered by exogenous stimuli (e.g. Obstacles along the road). We report analysis of the EEG signals of fifteen subjects while they drive in a realistic car simulator. We found that the peak latency of the event-related potentials in frontal and parietal areas significantly correlates with the onset of the steering behavior. Similarly, modulations of the power in the theta band (4-8 Hz) prior to the action also correlates with the reaction times. These results provide evidence of reliable neural markers of the driver's response variability.

关键词： Correlation Electroencephalography Electrodes Protocols Modulation Roads Vehicles

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BNCI Horizon 2020: towards a roadmap for the BCI community

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brain-COMPUTER interfaceS 2015年第1期2卷 1-10页

作者： Brunner, Clemens Birbaumer, Niels Blankertz, Benjamin Guger, Christoph Kuebler, Andrea Mattia, Donatella Millan, Jose del R. Miralles, Felip Nijholt, Anton Opisso, Eloy Ramsey, Nick Salomon, Patric Mueller-Putz, Gernot R. Graz Univ Technol Inst Knowledge Discovery Graz Austria Eberhard Karls Univ Tubingen Inst Med Psychol & Behav Neurobiol Tubingen Germany Tech Univ Berlin Chair Neurotechnol Berlin Germany G Tec Guger Technol Schiedelberg Austria Univ Wurzburg Inst Psychol Clin Psychol & Psychotherapy Wurzburg Germany Fondazione Santa Lucia Neuroelectr Imaging & BCI Lab Rome Italy Ecole Polytechn Federale Lausanne Defitech Chair Non Invas Brain Machine Interface Lausanne Switzerland Barcelona Digital Technol Ctr EHealth R& D Barcelona Spain Univ Twente Dept EWI Res Grp Human Media Interact Twente Netherlands Inst Neurorehabilitacio Guttmann Barcelona Spain Univ Med Ctr Utrecht Utrecht Netherlands EnablingMNT GmbH Berlin Germany

The brain-computer interface (BCI) field has grown dramatically over the past few years, but there are still no coordinated efforts to ensure efficient communication and collaboration among key stakeholders. The European Commission (EC) has recently renewed their efforts to establish such a coordination effort by funding a coordination and support action for the BCI community called 'BNCI Horizon 2020' after the ` Future BNCI' project. Major goals of this new project include developing a roadmap for the next decade and beyond, encouraging discussion and collaboration within the BCI community, fostering communication with the general public, and the foundation of an international BCI Society. We present a short overview of current and past EU-funded BCI projects and provide evidence of a growing research and industrial community. Efficient communication also entails the establishment of clear terminology, which is a major goal of BNCI Horizon 2020. To this end, we give a brief overview of current BCI-related terms and definitions. A major networking activity in the project was the BNCI Horizon 2020 Retreat in Hallstatt, Austria. Over 60 experts participated in this event to discuss the future of the BCI field in a series of plenary talks, targeted discussions, and parallel focus sessions. A follow-up event was the EU BCI Day at the 6th International brain-Computer interface Conference in Graz, Austria. This networking event included plenary talks by eight companies and representatives from all seven ongoing EU research projects, poster presentations, demos, and discussions. Another goal of BNCI Horizon 2020 is the foundation of an official BCI Society. In this article, we summarize the current status of this process. Finally, we present visions for future BCI applications developed within BNCI Horizon 2020 using input from external BCI experts as well. We identify common themes and conclude with six exemplary use cases.

关键词： BCI coordination collaboration society

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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

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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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Subject-oriented training for motor imagery brain-computer interfaces

Subject-oriented training for motor imagery brain-computer i...

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

作者： Serafeim Perdikis Robet Leeb Jose del R. Millan Chair in Non-Invasive Brain-Machine Interface Center for Neuroprosthetics School of Engineering Ecole Polytechnique Federale de Lausanne

ISBN: (纸本)9781424479276

Successful operation of motor imagery (MI)-based brain-computer interfaces (BCI) requires mutual adaptation between the human subject and the BCI. Traditional training methods, as well as more recent ones based on co-adaptation, have mainly focused on the machine-learning aspects of BCI training. This work presents a novel co-adaptive training protocol shifting the focus on subject-related performances and the optimal accommodation of the interactions between the two learning agents of the BCI loop. Preliminary results with 8 able-bodied individuals demonstrate that the proposed method has been able to bring 3 naive users into control of a MI BCI within a few runs and to improve the BCI performances of 3 experienced BCI users by an average of 0.36 bits/sec.

关键词： Training Protocols Feature extraction brain-computer interfaces Integrated circuits Measurement Indexes

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