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Cortical current density vs. surface EEG for event-related potential-based brain-Computer interface

Cortical current density vs. surface EEG for event-related p...

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2011 5th International IEEE/EMBS Conference on Neural Engineering, NER 2011

作者： Goel, Mohit Kumar Chavarriaga, Ricardo Del R. Millan, Jose Defitech Department of Non-Invasive Brain-Machine Interface Center for Neuroprosthetics School of Engineering CH-1015 Switzerland

ISBN: (纸本)9781424441402

In this paper we use the cortical current density based inverse solution to classify Event Related Potentials, in particular for error-related potentials elicited during a brain-Computer interface experiment. We selected discriminant cortical sources for comparing classification performance with respect to surface EEG. We found that the data from estimated cortical sources achieves higher classification accuracy for most of the subjects. In addition, the inverse method exhibits consistently discriminant activity for the sources located over the anterior cingulate cortex region for different time points. This level of neurophysiological interpretation in terms of localisation of selected cortical sources is enabled with the use of inverse solution. © 2011 IEEE.

关键词： brain computer interface

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Phase-based features for Motor Imagery brain-Computer interfaces

Phase-based features for Motor Imagery Brain-Computer Interf...

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

作者： Benjamin Hamner Robert Leeb Michele Tavella Jose del R. Millan Defitech Chair on Non-Invasive Brain-Machine Interface Center for Neuroprosthetics School of Engineering Ecole Polytechnique Federal de Lausanne

ISBN: (纸本)9781424441211

Motor imagery (MI) brain-computer interfaces (BCIs) translate a subject's motor intention to a command signal. Most MI BCIs use power features in the mu or beta rhythms, while several results have been reported using a measure of phase synchrony, the phase-locking value (PLV). In this study, we investigated the performance of various phase-based features, including instantaneous phase difference (IPD) and PLV, for control of a MI BCI. Patterns of phase synchrony differentially appear over the motor cortices and between the primary motor cortex (M1) and supplementary motor area (SMA) during MI. Offline results, along with preliminary online sessions, indicate that IPD serves as a robust control signal for differentiating between MI classes, and that the phase relations between channels are relatively stable over several months. Offline and online trial-level classification accuracies based on IPD ranged from 84% to 99%, whereas the performance for the corresponding amplitude features ranged from 70% to 100%.

关键词： BCI EEG Motor imagery Phase synchrony Instantaneous phase difference

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Detecting anomalies to improve classification performance in opportunistic sensor networks

Detecting anomalies to improve classification performance in...

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IEEE Annual Conference on Pervasive Computing and Communications Workshops (PerCom)

作者： Hesam Sagha José del R. Millán Ricardo Chavarriaga Defitech Foundation Chair in Non-Invasive Brain-Machine Interface Ecole Poly technique Federale de Lausanne (EPFL) 1015 Lausanne Switzerland

Anomalies and changes in sensor networks which are deployed for activity recognition may abate the classification performance. Detection of anomalies followed by compensatory reaction would ameliorate the performance. This paper introduces a novel approach to detect the faulty or degraded sensors in a multi-sensory environment and a way to compensate it. The approach considers the distance between each classifier output and the fusion output to decide whether a sensor (classifier) is degraded or not. Evaluation is done on two activity datasets with different configuration of sensors and different types of noise. The results show that using the method improves the classification accuracy.

关键词： Accuracy Noise measurement Manufacturing Accelerometers Additive noise Magnetic sensors

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Ensemble creation and reconfiguration for activity recognition: An information theoretic approach

Ensemble creation and reconfiguration for activity recogniti...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Ricardo Chavarriaga Hesam Sagha José del R. Millán Chair in Non-Invasive Brain-Machine Interface (CNBI) Center of Neuroprosthetics Ecole Polytechnique Fédérale de Lausanne (EPFL) Lausanne Switzerland

Advances in sensing, portable computing devices, and wireless communication has lead to an increase in the number and variety of sensing enabled devices (e.g. smartphones or sensing garments). Pervasive computing and activity recognition systems should be able to take advantage of these sensors, even if they are not always available or appear in runtime. These sensors can be integrated into an ensemble that fuse their information to obtain the final decision. There is therefore a need for mechanisms to select which sensors should compose the ensemble, as well as techniques for dynamically reconfigure the ensemble so as to integrate new sensors. Sensors can be integrated into an ensemble where information from each of them is fused to obtain the final decisions. From the machine learning point of view, this corresponds to the combination of classifiers where measures of the accuracy and diversity of the ensemble are used to select the elements that may lead to the highest performance. Recent works propose measures of accuracy and diversity based on an information theoretical approach. In this paper we study the use of these measures for selecting ensembles in activity recognition based on body sensor networks. Besides a comparison with traditional diversity measures (e.g., Q-, κ-statistics), we also present mechanisms to exploit these measures for the dynamic reconfiguration of the ensemble and detection of changes in the network.

关键词： Accuracy Sensors Mutual information Testing Training Estimation Manufacturing

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Recording a complex, multi modal activity data set for context recogntion 23

Recording a complex, multi modal activity data set for conte...

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23th International Conference on Architecture of Computing Systems 2010, ARCS 2010

作者： Lukowicz, P. Pirkl, G. Bannach, D. Wagner, F. Calatroni, A. Förster, K. Holleczek, T. Rossi, M. Roggen, D. Troester, G. Doppler, J. Holzmann, C. Riener, A. Ferscha, A. Chavarriaga, R. Embedded Systems Lab University of Passau Germany Wearable Computing Lab ETH Switzerland Institute Pervasive Computing JKU Linz Austria Defitech Foundation Chair in Non-Invasive Brain-Machine Interface EPFL Lausanne Switzerland

ISBN: (纸本)9783800732227

Publicly available data sets are increasingly becoming an important research tool in context recognition. However, due to the diversity and complexity of the domain it is difficult to provide standard recordings that cover the majority of possible applications and research questions. In this paper we describe a novel data set hat combines a number of properties, that, in this combination, are missing from existing data sets. This includes complex, overlapping and hierarchically decomposable activities, a large number of repetitions, significant number of different users and a highly multi modal sensor setup. The set contains around 25 hours of data from 12 subjects. On the low level there are around 30'000 individual annotated actions (e.g. picking up a knife, opening a drawer). On the highest level (e.g. getting up, breakfast preparation) we have around 200 context instances. Overall 72 sensors from 10 different modalities (different on body motion sensors, different sound sources, two cameras, video, object usage, device power consumption and location) were recorded. © 2010 23th International Conference on Architecture of Computing Systems 2010, ARCS 2010 - Workshop Proceedings. All rights reserved.

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Collecting complex activity datasets in highly rich networked sensor environments

Collecting complex activity datasets in highly rich networke...

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7th International Conference on Networked Sensing Systems, INSS 2010

作者： Roggen, Daniel Calatroni, Alberto Rossi, Mirco Holleczek, Thomas Förster, Kilian Tröster, Gerhard Lukowicz, Paul Bannach, David Pirkl, Gerald Ferscha, Alois Doppler, Jakob Holzmann, Clemens Kurz, Marc Holl, Gerald Chavarriaga, Ricardo Sagha, Hesam Bayati, Hamidreza Creatura, Marco Del R. Millàn, Jose Wearable Computing Laboratory ETH Zurich Switzerland Institute for Pervasive Computing Johannes Kepler University Linz Austria Institute for Pervasive Computing Johannes Kepler University Linz Austria Defitech Foundation Chair in Non-Invasive Brain-Machine Interface Ecole Polytechnique Fédérale Lausarme Switzerland Department of Informatics Systems and Telematics University of Genova Italy

ISBN: (纸本)9781424479108

We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity occurrences observed during post-processing, and estimate that over 13000 and 14000 object and environment interactions occurred. We describe the networked sensor setup and the methodology for data acquisition, synchronization and curation. We report on the challenges and outline lessons learned and best practice for similar large scale deployments of heterogeneous networked sensor systems. We evaluate data acquisition quality for on-body and object integrated wireless sensors;there is less than 2.5% packet loss after tuning. We outline our use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations. Eventually this dataset will be made public. © 2010 IEEE.

关键词： Pattern recognition

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Recording a Complex, Multi Modal Activity Data Set for Context Recognition

Recording a Complex, Multi Modal Activity Data Set for Conte...

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23th International Conference on Architecture of Computing Systems 2010

作者： P. Lukowicz G. Pirkl D. Bannach F. Wagner A. Calatroni K. Foerster T. Holleczek M. Rossi D. Roggen G. Troester J. Doppler C. Holzmann A. Riener A. Ferscha R. Chavarriaga Embedded Systems Lab University of Passau Germany Wearable Computing Lab ETH Switzerland Institute Pervasive Computing JKU Linz Austria Defitech Foundation Chair in Non-Invasive Brain-Machine Interface EPFL Lausanne Switzerland

Publicly available data sets are increasingly becoming an important research tool in context recognition. However, due to the diversity and complexity of the domain it is difficult to provide standard recordings that cover the majority of possible applications and research questions. In this paper we describe a novel data set hat combines a number of properties, that, in this combination, are missing from existing data sets. This includes complex, overlapping and hierarchically decomposable activities, a large number of repetitions, significant number of different users and a highly multi modal sensor setup. The set contains around 25 hours of data from 12 subjects. On the low level there are around 30000 individual annotated actions (e.g. picking up a knife, opening a drawer). On the highest level (e.g. getting up, breakfast preparation) we have around 200 context instances. Overall 72 sensors from 10 different modalities (different on body motion sensors, different sound sources, two cameras, video, object usage, device power consumption and location) were recorded.

关键词： Acceleration Dairy products Gyroscopes Context Legged locomotion Accuracy Microphones

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Towards natural non-invasive hand neuroprostheses for daily living

Towards natural non-invasive hand neuroprostheses for daily ...

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

作者： Michele Tavella Robert Leeb Rüdiger Rupp José del R. Millán Chair on Non-Invasive Brain-Machine Interface Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland University Hospital Spinal Cord Injury Center Heidelberg Germany

In this paper we show how healthy subjects can operate a non-invasive asynchronous BCI for controlling a FES neuroprosthesis and manipulate objects to carry out daily tasks in ecological conditions. Both, experienced and novel subjects proved to be able to deliver mental commands with high accuracy and speed. Our neuroprosthetic approach relies on a natural interaction paradigm, where subjects delivers congruent MI commands (i.e., they imagining a movement of the same hand they control through FES). Furthermore, we have tested our approach in a common daily task such as handwriting, which requires the user to split his/her attention to multitask between BCI control, reaching, and the primary handwriting task itself. Interestingly, the very low number of erroneous trials illustrates how during the experiments subjects were able to deliver commands just when they intended to do so. Similarly, the subjects could perform actions while delivering, or preparing to deliver, mental commands.

关键词： Writing Electroencephalography Grasping Muscles Neuromuscular stimulation brain computer interfaces Accuracy

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Collecting complex activity datasets in highly rich networked sensor environments

Collecting complex activity datasets in highly rich networke...

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International Conference on Networked Sensing Systems (INSS)

作者： Daniel Roggen Alberto Calatroni Mirco Rossi Thomas Holleczek Kilian Förster Gerhard Tröster Paul Lukowicz David Bannach Gerald Pirkl Alois Ferscha Jakob Doppler Clemens Holzmann Marc Kurz Gerald Holl Ricardo Chavarriaga Hesam Sagha Hamidreza Bayati Marco Creatura José del R. Millàn Wearable Computing Laboratory ETH Zürich Switerland Embedded Systems Laboratory University of Passau Germany Institute for Pervasive Computing Johannes Kepler University Linz Linz Austria Defitech Foundation Chair in Non-Invasive Brain-Machine Interface Ecole Polytechnique Fédérale de Lausanne Switzerland Ecole Polytechnique Fédérale de Lausanne Switzerland Department of Informatics Systems and Telematics University of Genova Italy

We deployed 72 sensors of 10 modalities in 15 wireless and wired networked sensor systems in the environment, in objects, and on the body to create a sensor-rich environment for the machine recognition of human activities. We acquired data from 12 subjects performing morning activities, yielding over 25 hours of sensor data. We report the number of activity occurrences observed during post-processing, and estimate that over 13000 and 14000 object and environment interactions occurred. We describe the networked sensor setup and the methodology for data acquisition, synchronization and curation. We report on the challenges and outline lessons learned and best practice for similar large scale deployments of heterogeneous networked sensor systems. We evaluate data acquisition quality for on-body and object integrated wireless sensors; there is less than 2.5% packet loss after tuning. We outline our use of the dataset to develop new sensor network self-organization principles and machine learning techniques for activity recognition in opportunistic sensor configurations. Eventually this dataset will be made public.

关键词： Microphones Bluetooth Humidity Lead Bismuth Artificial intelligence Electrocardiography

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On the road to a neuroprosthetic hand: A novel hand grasp orthosis based on functional electrical stimulation

On the road to a neuroprosthetic hand: A novel hand grasp or...

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

作者： Robert Leeb Miguel Gubler Michele Tavella Heather Miller José del. R. Millán Chair on Non-Invasive Brain-Machine Interface Ecole Polytechnique Fédérale de Lausanne Lausanne Switzerland Sensory-Motor System laboratory Swiss Federal Institute of Technology Zurich Switzerland Sensory-Motor System Lab Swiss Federal Institute of Technology Zuórich Zuórich Switzerland

To patients who have lost the functionality of their hands as a result of a severe spinal cord injury or brain stroke, the development of new techniques for grasping is indispensable for reintegration and independency in daily life. Functional Electrical Stimulation (FES) of residual muscles can reproduce the most dominant grasping tasks and can be initialized by brain signals. However, due to the very complex hand anatomy and current limitations in FES-technology with surface electrodes, these grasp patterns cannot be smoothly executed. In this paper, we present an adaptable passive hand orthosis which is capable of producing natural and smooth movements when coupled with FES. It evenly synchronizes the grasping movements and applied forces on all fingers, allowing for naturalistic gestures and functional grasps of everyday objects. The orthosis is also equipped with a lock, which allows it to remain in the desired position without the need for long-term stimulation. Furthermore, we quantify improvements offered by the orthosis compare them with natural grasps on healthy subjects.

关键词： Grasping Force Synchronization Strips Muscles Thumb Sensors

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