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

作者： L. Ferrero V. Quiles P. Soriano-Segura M. Ortiz E. Iáñez J.L. Contreras-Vidal J. M. Azorín Brain-Machine Interface Systems Lab and at the International Affiliate NSF IUCRC BRAIN Site Miguel Hernández University of Elche Elche Spain Instituto de Investigación en Ingeniería de Elche-I3E Miguel Hernández University of Elche Elche Spain Laboratory for Non-Invasive Brain Machine Interface Systems and Director of the NSF IUCRC BRAIN University of Houston Houston TX United States of America Valencian Graduate School and Research Network of Artificial Intelligence – valgrAI Spain

This study evaluates the performance of two convolutional neural networks (CNNs) in a brain-machine interface (BMI) based on motor imagery (MI) by using a small dataset collected from five participants wearing a lower-limb exoskeleton. To address the issue of limited data availability, transfer learning was employed by training models on EEG signals from other subjects and subsequently fine-tuning them to specific users. A combination of common spatial patterns (CSP) and linear discriminant analysis (LDA) was used as a benchmark for comparison. The study's primary aim is to examine the potential of CNNs and transfer learning in the development of an automatic neural classification system for a BMI based on MI to command a lower-limb exoskeleton that can be used by individuals without specialized *** Relevance— BMI can be used in rehabilitation for patients with motor impairment by using mental simulation of movement to activate robotic exoskeletons. This can promote neural plasticity and aid in recovery.

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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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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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Applications of brain–machine interface systems in Stroke Recovery and Rehabilitation

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Current Physical Medicine and Rehabilitation Reports 2014年第2期2卷 93-105页

作者： Venkatakrishnan, Anusha Francisco, Gerard E. L. Contreras-Vidal, Jose Laboratory for Non-invasive Brain–Machine Interface Systems Department of Electrical and Computer Engineering University of Houston Houston TX United States Department of Physical Medicine and Rehabilitation University of Texas Health Science Center Houston TX United States NeuroRecovery Research Center TIRR Memorial Hermann Houston Houston TX United States

Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients. Recent engineering and technological advances such as brain–machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neurorehabilitation, to accelerate functional recovery and improve QOL. This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation. Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation. © 2014, Springer Science + Business Media New York.

关键词： brain–machine interfaces Clinical trials Functional recovery Neuroplasticity Neurorehabilitation Robotic exoskeletons Robotic-assisted rehabilitation Stroke

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Decoding of Intentional Actions from Scalp Electroencephalography (EEG) in Freely-behaving Infants

Decoding of Intentional Actions from Scalp Electroencephalog...

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

作者： Zachery R. Hernandez Jesus Cruz-Garza Teresa Tse Jose L. Contreras-Vidal Non-Invasive Brain-Machine Interface Systems Laboratory at the Department of Electrical and Computer Engineering University of Houston (UH) Center for Robotics and Intelligent Systems Tecnologico de Monterrey

ISBN: (纸本)9781424479276

The mirror neuron system (MNS) in humans is thought to enable an individual's understanding of the meaning of actions performed by others and the potential imitation and learning of those actions. In humans, electroencephalographic (EEG) changes in sensorimotor a-band at central electrodes, which desynchronizes both during execution and observation of goal-directed actions (i.e., μ suppression), have been considered an analog to MNS function. However, methodological and developmental issues, as well as the nature of generalized μ suppression to imagined, observed, and performed actions, have yet to provide a mechanistic relationship between EEG μ-rhythm and MNS function, and the extent to which EEG can be used to infer intent during MNS tasks remains unknown. In this study we present a novel methodology using active EEG and inertial sensors to record brain activity and behavioral actions from freely-behaving infants during exploration, imitation, attentive rest, pointing, reaching and grasping, and interaction with an actor. We used δ-band (1-4Hz) EEG as input to a dimensionality reduction algorithm (locality-preserving Fisher's discriminant analysis, LFDA) followed by a neural classifier (Gaussian mixture models, GMMs) to decode the each MNS task performed by freely-behaving 6-24 month old infants during interaction with an adult actor. Here, we present results from a 20-month male infant to illustrate our approach and show the feasibility of EEG-based classification of freely occurring MNS behaviors displayed by an infant. These results, which provide an alternative to the μ-rhythm theory of MNS function, indicate the informative nature of EEG in relation to intentionality (goal) for MNS tasks which may support action-understanding and thus bear implications for advancing the understanding of MNS function.

关键词： Electroencephalography Scalp Acceleration Training Coherence Electrodes Testing

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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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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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Modular organization of reaching and grasping movements investigated using EEG microstates

Modular organization of reaching and grasping movements inve...

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

作者： J. Minguillon E. Pirondini M. Coscia R. Leeb J.d.R. Millán D. Van De Ville S. Micera Translational Neural Engineering Laboratory Ecole Polytechnique Federale de Lausanne (EPFL) Lausanne Switzerland Instituto di Neuroscienze del Consiglio Nazionale delle Ricerche (CNR) Pisa Italy Chair of Non-Invasive Brain-Machine Interface Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland Department of Radiology and Medical Informatics University of Geneva Lausanne Switzerland Biorobotics Institute Scuola Superiore Sant'Anna Pisa Italy

How movements are generated and controlled by the central nervous system (CNS) is still not well understood. In this work, we tested the hypothesis of a modular organization of the brain activity during the execution of voluntary movements. In particular, we extracted meta-stable topographies as a measure for global brain state, so-called microstates, from electroencephalography (EEG) data during pure planar reaching movements as well as reaching and grasping of different objects, and we compared them with those extracted during resting-state. The results showed the emergence of specific EEG microstates related to movement execution. Our results provide evidence about the benefits of EEG microstate analysis for motor control studies and their importance to better understand brain reorganization in neurological pathologies.

关键词： Electroencephalography Grasping brain Fingers Organizations Timing Educational institutions

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Freeing the visual channel by exploiting vibrotactile BCI feedback

Freeing the visual channel by exploiting vibrotactile BCI fe...

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

作者： Robert Leeb Kiuk Gwak Dae-Shik Kim Jose del. R. Millan Chair on Non-Invasive Brain-Machine Interface Center for Neuroprosthetics Institute of Bioengineering School of Engineering Ecole Polytechnique Federale de Lausanne Brain Reverse Engineering and Imaging Laboratory Korea Advanced Institute of Science and Technology

ISBN: (纸本)9781457702150

Controlling a brain-actuated device requires the participant to look at and to split his attention between the interaction of the device with its environment and the status information of the brain-Computer interface (BCI). Such parallel visual tasks are partly contradictory, with the goal of achieving a good and natural device control. Is there a possibility to free the visual channel from one of these tasks? To address this, a stimulation system based on 6 coin-motors is developed, which provides a spatially continuous tactile illusion as BCI feedback, so that the visual channel can be devoted to the device. Several experiments are conducted in this work, to optimize the tactile illusion patterns and to investigate the influence on the electroencephalogram (EEG). Finally, 6 healthy BCI participants compare visual with tactile feedback in online BCI recordings. The developed stimulator can be used without interfering with the EEG. All subjects are able to perceive this type of tactile feedback well, and no statistical degradation in the online BCI performance could be identified between visual and tactile feedback.

关键词： BCI EEG Tactile Feedback Vibration Motor Imagery

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Determination of trajectories using non-invasive bci techniques in 3d environments

Determination of trajectories using non-invasive bci techniq...

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16th International Conference on Climbing and Walking Robots and the Support Technologies for Mobile machines, CLAWAR 2013

作者： García-Egea, Teodoro Díaz-Hernández, Carlos Alberto López-Coronado, Juan Contreras-Vidal, José L. Department of Automation and Systems Engineering Campus Muralla del Mar Polytechnical University of Cartagena Cartagena Spain Department of Polytechnic Sciences Polytechnic Faculty UCAM Catholic University San-Anthony of Murcia Spain Fischell Department of Bioengineering University of Maryland College Park MD United States Department of Kinesiology University of Maryland College Park MD United States Department of Electrical and Computer Engineering Laboratory for Non-invasive Brain-Machine Interface Systems University of Houston Houston TX 77204 United States

ISBN: (纸本)9789814525527

The feasibility of continuous decoding of self-initiated, self-selected hand movements to three-dimensional (3D) spatial targets from scalp electroencephalography (EEG) using linear decoders has been recently demonstrated. In this paper, we show that it is possible to train linear classifiers to decode hand movement direction to eight 3D spatial targets, in both planning and movement windows, using only the fluctuations in the amplitude of smoothed low-frequency signals from high-density scalp EEG. Taken together these results support the design of brain-computer interfaces (BCI) based on non-invasive scalp EEG signals and suggest that the current perception of the limits of EEG as a source signal for BCI applications merits further examination. Copyright © 2013 by World Scientific Publishing Co. Pte. Ltd.

关键词： Electroencephalography

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