Neural signatures of humans’ movement intention can be exploited by future neuroprosthesis. We propose a method for detecting self-paced upper limb movement intention from brain signals acquired with both invasive an...
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ISBN:
(纸本)9781424441198
Neural signatures of humans’ movement intention can be exploited by future neuroprosthesis. We propose a method for detecting self-paced upper limb movement intention from brain signals acquired with both invasive and noninvasive methods. In the first study with scalp electroencephalograph (EEG) signals from healthy controls, we report single trial detection of movement intention using movementrelated potentials (MRPs) in a frequency range between 0.1 to 1 Hz. Movement intention can be detected above chance level (p<0.05) on average 460 ms before the movement onset with low detection rate during the non-movement intention period. Using intracranial EEG (iEEG) from one epileptic subject, we detect movement intention as early as 1500 ms before movement onset with accuracy above 90% using electrodes implanted in the bilateral supplementary motor area (SMA). The coherent results obtained with non-invasive and invasive method and its generalization capabilities across different days of recording, strengthened the theory that self-paced movement intention can be detected before movement initiation for the advancement in robot-assisted neurorehabilitation.
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....
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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.
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 us...
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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%.
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 ...
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 activi...
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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 ...
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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.
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 activi...
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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.
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