pySPACE—a signal processing and classification environment in Python

Krell, Mario Michael; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Teiwes, Johannes; Metzen, Jan Hendrik; Kirchner, Elsa Andrea; Kirchner, Frank

doi:10.3389/fninf.2013.00040

Cited by 39 publications

(20 citation statements)

References 36 publications

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“…We used our software pySPACE (Signal Processing And Classification Environment) (Krell et al, 2013a;Krell et al, 2013b) 1 for online and pseudo-online data analysis.…”

Section: Processing Methodsmentioning

confidence: 99%

Online Detection of P300 related Target Recognition Processes During a Demanding Teleoperation Task - Classifier Transfer for the Detection of Missed Targets

Woehrle¹,

Kirchner²

2014

Proceedings of the International Conference on Physiological Computing Systems

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The detection of event related potentials and their usage for innovative tasks became a mature research topic in the last couple of years for brain computer interfaces. However, the typical experimental setups are usually highly controlled and designed to actively evoke specific brain activity like the P300 event related potential. In this paper, we show that the detection and passive usage of the P300 related brain activity is possible in highly uncontrolled and noisy application scenarios where the subjects are performing demanding senso-motor task, i.e., telemanipulation of a real robotic arm. In the application scenario, the subject wears an exoskeleton to control a robotic arm, which is presented to him in a virtual scenario. While performing the telemanipulation task he has to respond to important messages. By online analysis of the subject's electroencephalogram we detect P300 related target recognition processes to infer on upcoming response behavior or missing of response behavior in case a target was not recognized. We show that a classifier that is trained to distinguish between brain activity evoked by recognized task relevant stimuli and ignored frequent task irrelevant stimuli can be applied to classify between brain activity evoked by recognized task relevant stimuli and brain activity that is evoked in case that task relevant stimuli are not recognized.

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“…We used our software pySPACE (Signal Processing And Classification Environment) (Krell et al, 2013a;Krell et al, 2013b) 1 for online and pseudo-online data analysis.…”

Section: Processing Methodsmentioning

confidence: 99%

Online Detection of P300 related Target Recognition Processes During a Demanding Teleoperation Task - Classifier Transfer for the Detection of Missed Targets

Woehrle¹,

Kirchner²

2014

Proceedings of the International Conference on Physiological Computing Systems

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“…This brings along some challenges in data storage, processing, selection, and handling. Thus, new software and hardware solutions, such as the open source software framework pySPACE [Krell et al 2013] or the specialized software framework reSPACE [Wöhrle and Kirchner 2015] which can run on embedded systems, must be developed that allow for flexible usage of multimodal data. Optimization during runtime will require reconfiguration of hardware at runtime to allow optimal, i.e., time, resource, and energy-efficient interpretation of potentially changing data sets.…”

Section: Software Framework and Hardware Solutions For Deep System Imentioning

confidence: 99%

Embedded multimodal interfaces in robotics: applications, future trends, and societal implications

Kirchner¹,

Fairclough²,

Kirchner³

2019

The Handbook of Multimodal-Multisensor Interfaces: Language Processing, Software, Commercialization, and Emerging Directions -

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“…Signal processing and classification of the EEG data were performed with the open source software framework pySPACE (Krell et al, 2013).…”

Section: Eeg Processingmentioning

confidence: 99%

Spatio-temporal Comparison between ERD/ERS and MRCP-based Movement Prediction

Seeland

Manca

Kirchner

et al. 2015

Proceedings of the International Conference on Bio-Inspired Systems and Signal Processing

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In brain-computer interfaces (BCIs) based on electroencephalography (EEG), two distinct types of EEG patterns related to movement have been used for detecting the brain's preparation for voluntary movements: a) event-related patterns in the time domain named movement related cortical potentials (MRCPs) and b) patterns in the frequency domain named event-related desynchronization/synchronization (ERD/ERS). The applicability of those patterns in BCIs is often evaluated by the classification performance. To this end, the known spatio-temporal differences in EEG activity can be of interest, since they might influence the classification performance of the two different patterns. In this paper, we compared the classification performance based on ERD/ERS and MRCP while varying the time point of prediction as well as the used electrode sites. Empirical results were obtained from eight subjects performing voluntary right arm movements. Results show: a) classification based on MRCP is superior compared to ERD/ERS close to the movement onset whereas the opposite results farther away from the movement onset, b) the performance maximum of MRCP is located at central electrodes whereas it is at fronto-central electrodes for ERD/ERS. In summary, the results contribute to a better insight into the spatial and temporal differences between ERD/ERS and MRCP in terms of prediction performance. 219Seeland A., Manca L., Kirchner F. and Kirchner E.. Spatio-temporal Comparison between ERD/ERS and MRCP-based Movement Prediction.

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pySPACE—a signal processing and classification environment in Python

Cited by 39 publications

References 36 publications

Online Detection of P300 related Target Recognition Processes During a Demanding Teleoperation Task - Classifier Transfer for the Detection of Missed Targets

Online Detection of P300 related Target Recognition Processes During a Demanding Teleoperation Task - Classifier Transfer for the Detection of Missed Targets

Embedded multimodal interfaces in robotics: applications, future trends, and societal implications

Spatio-temporal Comparison between ERD/ERS and MRCP-based Movement Prediction

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