pySPACE (https://github.com/pyspace)
Introduction to a Signal Processing and
Classification Environment (pySPACE)
M M Krell...
Table of Contents
1 Introduction 3
2 How to install and use 7
3 Concepts and Features 15
4 Applications 21
5 Conclusion 27...
Introduction
Computation of Multiple Workflows and Datasets
. . . with applications, e.g., in robotics and brain-computer i...
Introduction
Computation of Multiple Workflows and Datasets
. . . with applications, e.g., in robotics and brain-computer i...
Introduction
Computation of Multiple Workflows and Datasets
. . . with applications, e.g., in robotics and brain-computer i...
Introduction
Short Facts
medium sized framework (> 40000 lines of code)
developed and tested on Mac OS X and Linux
develop...
Introduction
Short Facts
medium sized framework (> 40000 lines of code)
developed and tested on Mac OS X and Linux
develop...
Introduction
Selected Applications: The VI-Bot Project
M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 5 / 30
Introduction
Selected Applications
evaluation and comparison of
. . . sensor/channel selection algorithms (on EEG data) [1...
Introduction
Selected Applications
evaluation and comparison of
. . . sensor/channel selection algorithms (on EEG data) [1...
Introduction
Selected Applications
evaluation and comparison of
. . . sensor/channel selection algorithms (on EEG data) [1...
Introduction
Selected Applications
evaluation and comparison of
. . . sensor/channel selection algorithms (on EEG data) [1...
How to install and use
How to install and use pySPACE
1 installation (simple, see tutorial)
2 prepare your data for pySPAC...
How to install and use
Installation
required dependencies:
Python 2.7/Python 2.6
YAML
NumPy
SciPy
matplotlib (visualizatio...
How to install and use
Installation
required dependencies:
Python 2.7/Python 2.6
YAML
NumPy
SciPy
optional dependencies:
m...
How to install and use
Installation
required dependencies:
Python 2.7/Python 2.6
YAML
NumPy
SciPy
optional dependencies:
m...
How to install and use
prepare your data: Input Formats
feature vector: csv, arff
time series segments: csv
time series str...
How to install and use
1 installation
2 prepare your data
dataset description of banana dataset (metadata.yaml)
storage_fo...
How to install and use
1 installation
2 prepare your data
3 decide and define the processing file (examples/bench.yaml)
type...
How to install and use
1 installation
2 prepare your data for pySPACE
3 decide and define the processing file (bench.yaml)
4...
How to install and use
1 installation
2 prepare your data for pySPACE
3 decide and define the processing file (bench.yaml)
4...
How to install and use
Parallelization
parallel execution modes:
single-core: − − serial
multi-core (multiprocessing): − −...
How to install and use
Parallelization
parallel execution modes:
single-core: − − serial
multi-core (multiprocessing): − −...
Concepts and Features
General Structure Concept [12]
operation
1
operation
2
operation
3
operation
..
node
1
node
2
node
3...
Concepts and Features
General Node Chain Processing
CPU
1
Column Chart
TableLine Chart
processed Data
CPU
2
CPU
3
A
1 2 3 ...
Concepts and Features
General Node Chain Processing
CPU
1
Column Chart
TableLine Chart
processed Data
CPU
2
CPU
3
A
1 2 3 ...
Concepts and Features
More than 100 own implemented algorithms [12]Source
Sink
Preprocessing
Spatial
Filter
Classification
...
Concepts and Features
More than 100 own implemented algorithms [12]Source
Sink
Preprocessing
Spatial
Filter
Classification
...
Concepts and Features
Package Structure
environments (parallelization backends, node/operation chains,
application interfa...
Concepts and Features
Documentation
http://pyspace.github.io/pyspace/
internally generated on a daily bases
based on sphin...
Concepts and Features
Testing
core functionalities are changed very seldom with multiple checks
unit tests for internal de...
Applications
Using Optimized Processing Chains in the Application
launch live
M M Krell pySPACE (https://github.com/pyspac...
Applications
Using Optimized Processing Chains in the Application
launch live
YAML configuration file defines complete data h...
Applications
reSPACE for Processing with FPGA on Mobile Device
M M Krell pySPACE (https://github.com/pyspace) July 27, 201...
Applications
reSPACE for Processing with FPGA on Mobile Device
!"#$
%#$
&'%"("
)*+',-'./, 012'324
56'7/.6
85-'324
M M Krel...
Applications
Large Scale Evaluation: Sensor Selection
10 20 30 40 50 60
Number of EEG Electrodes
0.76
0.77
0.78
0.79
0.80
...
Applications
Data Processing Visualization
M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 26 / 30
Conclusion
Conclusion
pySPACE automatizes the signal processing and classification
workflow.
automatic parallel execution of...
Conclusion
Conclusion
pySPACE automatizes the signal processing and classification
workflow.
automatic parallel execution of...
Conclusion
Thank you for your attention! Do you have questions?
CPU
1
Column Chart
TableLine Chart
processed Data
CPU
2
CP...
Bibliography
[1] David Feess, Mario Michael Krell, and Jan Hendrik Metzen. Comparison of Sensor Selection Mechanisms for a...
Bibliography
[11] Mario Michael Krell, David Feess, and Sirko Straube. Balanced Relative Margin Machine The missing piece ...
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Introduction to the Signal Processing and Classification Environment pySPACE by Mario Michael Krell PyData Berlin 2014

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This talk will give a basic introduction to the pySPACE framework and its current applications.

pySPACE (Signal Processing And Classification Environment) is a modular software for the processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g., PCA, CSP, xDAWN) to established classifiers (e.g., SVM, LDA). pySPACE incorporates the concept of node and node chains of the Modular Toolkit for Data Processing (MDP) framework. Due to its modular architecture, the software can easily be extended with new processing nodes and more general operations. Large scale empirical investigations can be configured using simple text-configuration files in the YAML format, executed on different (distributed) computing modalities, and evaluated using an interactive graphical user interface.

pySPACE allows the user to connect nodes modularly and automatically benchmark the respective chains for different parameter settings and compare these with other node chains, e.g., by automatic evaluation of classification performances provided within the software. In addition, the pySPACElive mode of execution can be used for online processing of streamed data. The software specifically supports but is not limited to EEG data. Any kind of time series or feature vector data can be processed and analyzed.

pySPACE additionally provides interfaces to specialized signal processing libraries such as SciPy, scikit-learn, LIBSVM, the WEKA Machine Learning Framework, and the Maja Machine Learning Framework (MMLF).

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Introduction to the Signal Processing and Classification Environment pySPACE by Mario Michael Krell PyData Berlin 2014

  1. 1. pySPACE (https://github.com/pyspace) Introduction to a Signal Processing and Classification Environment (pySPACE) M M Krell, A Seeland, J H Metzen DFKI Bremen & University of Bremen Robotics Innovation Center Director: Prof. Dr. Frank Kirchner www.dfki.de/robotics robotics@dfki.de M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 1 / 30
  2. 2. Table of Contents 1 Introduction 3 2 How to install and use 7 3 Concepts and Features 15 4 Applications 21 5 Conclusion 27 M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 2 / 30
  3. 3. Introduction Computation of Multiple Workflows and Datasets . . . with applications, e.g., in robotics and brain-computer interfaces . . . with simple configuration and automatic processing of empirical evaluations (benchmarking) . . . on feature vector and time series datasets . . . where configuration requires no programming (YAML used) ⇒ useable by non-programmers . . . with execution in a distributed manner (embarrassingly parallel) . . . intuitive structure and documentation . . . choosing from more than 100 signal processing and classification algorithms (additionally interfaces to other libraries) M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 3 / 30
  4. 4. Introduction Computation of Multiple Workflows and Datasets . . . with applications, e.g., in robotics and brain-computer interfaces . . . with simple configuration and automatic processing of empirical evaluations (benchmarking) . . . on feature vector and time series datasets . . . where configuration requires no programming (YAML used) ⇒ useable by non-programmers . . . with execution in a distributed manner (embarrassingly parallel) . . . intuitive structure and documentation . . . choosing from more than 100 signal processing and classification algorithms (additionally interfaces to other libraries) M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 3 / 30
  5. 5. Introduction Computation of Multiple Workflows and Datasets . . . with applications, e.g., in robotics and brain-computer interfaces . . . with simple configuration and automatic processing of empirical evaluations (benchmarking) . . . on feature vector and time series datasets . . . where configuration requires no programming (YAML used) ⇒ useable by non-programmers . . . with execution in a distributed manner (embarrassingly parallel) . . . intuitive structure and documentation . . . choosing from more than 100 signal processing and classification algorithms (additionally interfaces to other libraries) M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 3 / 30
  6. 6. Introduction Short Facts medium sized framework (> 40000 lines of code) developed and tested on Mac OS X and Linux development started 6 years ago (open source since August 2013) core developer team of 3 − 5 people and approx. 10 in total open source software (GPL, available at https://github.com/pyspace) documentation: http://pyspace.github.io/pyspace/ M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 4 / 30
  7. 7. Introduction Short Facts medium sized framework (> 40000 lines of code) developed and tested on Mac OS X and Linux development started 6 years ago (open source since August 2013) core developer team of 3 − 5 people and approx. 10 in total open source software (GPL, available at https://github.com/pyspace) documentation: http://pyspace.github.io/pyspace/ M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 4 / 30
  8. 8. Introduction Selected Applications: The VI-Bot Project M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 5 / 30
  9. 9. Introduction Selected Applications evaluation and comparison of . . . sensor/channel selection algorithms (on EEG data) [1] . . . dimensionality reduction algorithms (ICA, PCA, xDAWN, PiSF, CSP) [2, 4, 5, 13] . . . classifiers [6, 11, 14, 15] Brain-Computer Interfaces (movement prediction, interaction error detection, detection of warning perception) [3, 7, 8, 9, 10, 16, 17] robotic applications (soil detection, parallelization of robot simulations, localization algorithms, and sensor regression) more details at the end, if there is time left M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 6 / 30
  10. 10. Introduction Selected Applications evaluation and comparison of . . . sensor/channel selection algorithms (on EEG data) [1] . . . dimensionality reduction algorithms (ICA, PCA, xDAWN, PiSF, CSP) [2, 4, 5, 13] . . . classifiers [6, 11, 14, 15] Brain-Computer Interfaces (movement prediction, interaction error detection, detection of warning perception) [3, 7, 8, 9, 10, 16, 17] robotic applications (soil detection, parallelization of robot simulations, localization algorithms, and sensor regression) more details at the end, if there is time left M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 6 / 30
  11. 11. Introduction Selected Applications evaluation and comparison of . . . sensor/channel selection algorithms (on EEG data) [1] . . . dimensionality reduction algorithms (ICA, PCA, xDAWN, PiSF, CSP) [2, 4, 5, 13] . . . classifiers [6, 11, 14, 15] Brain-Computer Interfaces (movement prediction, interaction error detection, detection of warning perception) [3, 7, 8, 9, 10, 16, 17] robotic applications (soil detection, parallelization of robot simulations, localization algorithms, and sensor regression) more details at the end, if there is time left M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 6 / 30
  12. 12. Introduction Selected Applications evaluation and comparison of . . . sensor/channel selection algorithms (on EEG data) [1] . . . dimensionality reduction algorithms (ICA, PCA, xDAWN, PiSF, CSP) [2, 4, 5, 13] . . . classifiers [6, 11, 14, 15] Brain-Computer Interfaces (movement prediction, interaction error detection, detection of warning perception) [3, 7, 8, 9, 10, 16, 17] robotic applications (soil detection, parallelization of robot simulations, localization algorithms, and sensor regression) more details at the end, if there is time left M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 6 / 30
  13. 13. How to install and use How to install and use pySPACE 1 installation (simple, see tutorial) 2 prepare your data for pySPACE 3 decide and define the processing file 4 potentially modify your config file 5 start software M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 7 / 30
  14. 14. How to install and use Installation required dependencies: Python 2.7/Python 2.6 YAML NumPy SciPy matplotlib (visualizations) scikit-learn (classifiers, transformations) PyQt4 (GUIs) WEKA, MMLF LIBSVM, LIBLINEAR, MDP, cvxopt, . . . (algorithm interfaces) M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 8 / 30
  15. 15. How to install and use Installation required dependencies: Python 2.7/Python 2.6 YAML NumPy SciPy optional dependencies: matplotlib (visualizations) scikit-learn (classifiers, transformations) PyQt4 (GUIs) WEKA, MMLF LIBSVM, LIBLINEAR, MDP, cvxopt, . . . (algorithm interfaces) M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 8 / 30
  16. 16. How to install and use Installation required dependencies: Python 2.7/Python 2.6 YAML NumPy SciPy optional dependencies: matplotlib (visualizations) scikit-learn (classifiers, transformations) PyQt4 (GUIs) WEKA, MMLF LIBSVM, LIBLINEAR, MDP, cvxopt, . . . (algorithm interfaces) download (git clone https://github.com/pyspace/pyspace.git) python setup.py ⇒ configuration folder in home directory M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 8 / 30
  17. 17. How to install and use prepare your data: Input Formats feature vector: csv, arff time series segments: csv time series stream: csv, EDF2 .set (EEGLAB), .eeg (BrainProducts GmbH) M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 9 / 30
  18. 18. How to install and use 1 installation 2 prepare your data dataset description of banana dataset (metadata.yaml) storage_format: [csvUnnamed, real] type: FEATURE_VECTOR file_name: banana_data.csv label_column: 1 ... 3 decide and define the processing file 4 potentially modify your config file 5 start software M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 10 / 30
  19. 19. How to install and use 1 installation 2 prepare your data 3 decide and define the processing file (examples/bench.yaml) type: node_chain input_path: "example_summary" runs : 3 node_chain: - node: FeatureVectorSourceNode - node: TrainTestSplitter parameters : train_ratio: 0.4 - node: __Normalization__ - node : 2SVM parameters : complexity : __C__ - node: PerformanceSinkNode parameter_ranges : __C__ : [0.01,0.1,1] __Normalization__ : [GaussianFeatureNormalization, EuclideanFeatureNormalization] 4 potentially modify your config file 5 M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 11 / 30
  20. 20. How to install and use 1 installation 2 prepare your data for pySPACE 3 decide and define the processing file (bench.yaml) 4 potentially modify your config file (config.yaml) storage: ~/pySPACEcenter/storage spec_dir: ~/pySPACEcenter/specs console_log_level : logging.WARNING file_log_level : logging.INFO python_path: - /home/user/pySPACE/external/libsvm/python/ ... 5 start software M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 12 / 30
  21. 21. How to install and use 1 installation 2 prepare your data for pySPACE 3 decide and define the processing file (bench.yaml) 4 potentially modify your config file (config.yaml) 5 start software go to pySPACEcenter on the command line and type: ./launch.py -o examples/bench.yaml --mcore M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 13 / 30
  22. 22. How to install and use Parallelization parallel execution modes: single-core: − − serial multi-core (multiprocessing): − − mcore cluster (shared file system, LoadLeveler): − − loadl cluster (shared file system, no scheduling): − − mpi possibility to add new modes (backends): e.g., − − cloud, − − gearman? creation of jobs and execution specific subtasks (e.g., parameter optimization evaluations) different processing chains in the application M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 14 / 30
  23. 23. How to install and use Parallelization parallel execution modes: single-core: − − serial multi-core (multiprocessing): − − mcore cluster (shared file system, LoadLeveler): − − loadl cluster (shared file system, no scheduling): − − mpi possibility to add new modes (backends): e.g., − − cloud, − − gearman? further parallelization: creation of jobs and execution specific subtasks (e.g., parameter optimization evaluations) different processing chains in the application M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 14 / 30
  24. 24. Concepts and Features General Structure Concept [12] operation 1 operation 2 operation 3 operation .. node 1 node 2 node 3 node .. operation chain operation node chain node node chain merge WEKA .. FIR Filter sub- sampling SVM .. offline offline offline + online summary summary dataset data sample M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 15 / 30
  25. 25. Concepts and Features General Node Chain Processing CPU 1 Column Chart TableLine Chart processed Data CPU 2 CPU 3 A 1 2 3 4a 2 1 3 4b 1 2 4a spec {1,2,3,4a} {2,1,3,4b} {1,2,4a} B M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 16 / 30
  26. 26. Concepts and Features General Node Chain Processing CPU 1 Column Chart TableLine Chart processed Data CPU 2 CPU 3 A 1 2 3 4a 2 1 3 4b 1 2 4a spec {1,2,3,4a} {2,1,3,4b} {1,2,4a} B Modularity concept of node chain based on Modular toolkit for Data Processing (MDP)! M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 16 / 30
  27. 27. Concepts and Features More than 100 own implemented algorithms [12]Source Sink Preprocessing Spatial Filter Classification Feature Generator Meta Splitter Visualization Others Postprocessing LDA QDA LIBSVM LIBLINEAR SOR SVM Naive Bayes Random FFT IIR FIR CSP ICAPCA xDAWN Coherence Correlations Moments STFT DWT Linear Fit Pattern Search Grid Search Window Function detrend z-score Resample Decimation Cross- validation FDA Sensor Selection Avg. EEG Raw Data TKEO optimalsigmoid linear Scatter HistogramSpectrum Stream TimeSeries FeatureVector Performance Score Mapping gaussian histogram Normalizations Filters Feature Normalization Sub-Chain Fusion Classifier Ensemble Scikit-learn Wrapper Gating Functions ridge regression probability voting label voting precision weighted Feature Selection DebugInstance Selection Type Conversion Amplitudes Train-Test Splitter baseline TimeSeriesFeatureVector Stream M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 17 / 30
  28. 28. Concepts and Features More than 100 own implemented algorithms [12]Source Sink Preprocessing Spatial Filter Classification Feature Generator Meta Splitter Visualization Others Postprocessing LDA QDA LIBSVM LIBLINEAR SOR SVM Naive Bayes Random FFT IIR FIR CSP ICAPCA xDAWN Coherence Correlations Moments STFT DWT Linear Fit Pattern Search Grid Search Window Function detrend z-score Resample Decimation Cross- validation FDA Sensor Selection Avg. EEG Raw Data TKEO optimalsigmoid linear Scatter HistogramSpectrum Stream TimeSeries FeatureVector Performance Score Mapping gaussian histogram Normalizations Filters Feature Normalization Sub-Chain Fusion Classifier Ensemble Scikit-learn Wrapper Gating Functions ridge regression probability voting label voting precision weighted Feature Selection DebugInstance Selection Type Conversion Amplitudes Train-Test Splitter baseline TimeSeriesFeatureVector Stream Here new algorithms/libraries can be integrated/interfaced! M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 17 / 30
  29. 29. Concepts and Features Package Structure environments (parallelization backends, node/operation chains, application interface, configuration) missions (nodes, operations, support) resources (data types, dataset definitions) run (launch, launch live, GUIs, scripts) tests tools (useful stuff for file handling, logging, debugging, . . . ) M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 18 / 30
  30. 30. Concepts and Features Documentation http://pyspace.github.io/pyspace/ internally generated on a daily bases based on sphinx (and restructured text) some modifications to improve API documentation layout and automatic generation post processing to automatically display additional information (node names, input types, examples, list(s) of available algorithms (nodes)) M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 19 / 30
  31. 31. Concepts and Features Testing core functionalities are changed very seldom with multiple checks unit tests for internal development are run and checked via a cronjob and email on a daily bases regular user feedback generic unit tests added for (all) algorithms (nodes): documentation? Exemplary call existing? Exemplary call valid? Is the respective algorithm running on example data? Simple interface to modify test and define results to define specific unit tests scripts exist for running both type of tests M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 20 / 30
  32. 32. Applications Using Optimized Processing Chains in the Application launch live M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 21 / 30
  33. 33. Applications Using Optimized Processing Chains in the Application launch live YAML configuration file defines complete data handling multiple processing flows can be executed in parallel used for: detection of warning perception, interface error potentials, movement preparation, SSVEP, . . . M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 22 / 30
  34. 34. Applications reSPACE for Processing with FPGA on Mobile Device M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 23 / 30
  35. 35. Applications reSPACE for Processing with FPGA on Mobile Device !"#$ %#$ &'%"(" )*+',-'./, 012'324 56'7/.6 85-'324 M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 24 / 30
  36. 36. Applications Large Scale Evaluation: Sensor Selection 10 20 30 40 50 60 Number of EEG Electrodes 0.76 0.77 0.78 0.79 0.80 0.81 0.82 0.83 0.84 Balancedaccuracy All SSNRV S SSNRAS 1SVM 2SVM Performance PCA xDAWN CSP Standard caps 2 4 6 8 10 0.65 0.70 0.75 0.80 M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 25 / 30
  37. 37. Applications Data Processing Visualization M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 26 / 30
  38. 38. Conclusion Conclusion pySPACE automatizes the signal processing and classification workflow. automatic parallel execution of other evaluations (WEKA, robot simulation) intuitive configuration without scripting (YAML based) ⇒ useable by non-programmers possibility to integrate other algorithms/libraries future steps more algorithms and interfaces to other libraries other data types (e.g., pictures, videos, multimodal data) further applications (e.g., clustering, regression) installation suite . . . M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 27 / 30
  39. 39. Conclusion Conclusion pySPACE automatizes the signal processing and classification workflow. automatic parallel execution of other evaluations (WEKA, robot simulation) intuitive configuration without scripting (YAML based) ⇒ useable by non-programmers possibility to integrate other algorithms/libraries future steps more algorithms and interfaces to other libraries other data types (e.g., pictures, videos, multimodal data) further applications (e.g., clustering, regression) installation suite . . . M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 27 / 30
  40. 40. Conclusion Thank you for your attention! Do you have questions? CPU 1 Column Chart TableLine Chart processed Data CPU 2 CPU 3 A 1 2 3 4a 2 1 3 4b 1 2 4a spec {1,2,3,4a} {2,1,3,4b} {1,2,4a} B Figure: Node chain processing scheme from [12] Credits: German Research Center for Artificial Intelligence, DFKI Bremen, Robotics Innovation Center and Robotics Research Group, University of Bremen M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 28 / 30
  41. 41. Bibliography [1] David Feess, Mario Michael Krell, and Jan Hendrik Metzen. Comparison of Sensor Selection Mechanisms for an ERP-Based Brain-Computer Interface. PLoS ONE, 8(7):e67543, 2013. [2] Foad Ghaderi. Joint spatial and spectral filter estimation for single trial detection of event related potentials. In IEEE International Workshop on Machine Learning for Signal Processing, (MLSP), 9 2013. [3] Foad Ghaderi, Su Kyoung Kim, and Elsa Andrea Kirchner. Effects of eye artifact removal methods on single trial P300 detection, a comparative study. Journal of Neuroscience Methods, 221(0):41–47, 2014. [4] Foad Ghaderi and Elsa Andrea Kirchner. Periodic Spatial Filter for Single Trial Classification of Event Related Brain Activity. In Proceedings of the 10th IASTED International Conference on Biomedical Engineering (BioMed-2013), February 13-15, Innsbruck, Austria. ACTA Press, 2013. [5] Foad Ghaderi and Sirko Straube. An adaptive and efficient spatial filter for event-related potentials. In Proceedings of European Signal Processing Conference, (EUSIPCO), 9 2013. [6] Yohannes Kassahun, Hendrik W¨ohrle, Alexander Fabisch, and Marc Tabie. Learning parameters of linear models in compressed parameter space. In Alessandro E. Villa, Wlodzislaw Duch, P´eter ´Erdi, Francesco Masulli, and G¨unther Palm, editors, Artificial Neural Networks and Machine Learning ICANN 2012, volume 7553 of Lecture Notes in Computer Science, pages 108–115. Springer, Lausanne, Switzerland, 2012. [7] Su Kyoung Kim and Elsa Andrea Kirchner. Classifier transferability in the detection of error related potentials from observation to interaction. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, SMC-2013, Manchester, UK, October 13-16, 2013. [8] Elsa Andrea Kirchner, Su Kyoung Kim, Sirko Straube, Anett Seeland, Hendrik W¨ohrle, Mario Michael Krell, Marc Tabie, and Manfred Fahle. On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics. PLoS ONE, 8:e81732, 2013. [9] Elsa Andrea Kirchner and Marc Tabie. Closing the gap: Combined EEG and EMG analysis for early movement prediction in exoskeleton based rehabilitation. In Proceedings of the 4th European Conference on Technically Assisted Rehabilitation - TAR 2013, Berlin, Germany, 2013. [10] Elsa Andrea Kirchner, Hendrik W¨ohrle, Constantin Bergatt, Su Kyoung Kim, Jan Hendrik Metzen, David Feess, and Frank Kirchner. Towards Operator Monitoring via Brain Reading – An EEG-based Approach for Space Applications. In Proceedings of the 10th International Symposium on Artificial Intelligence, Robotics and Automation in Space, pages 448–455, Sapporo, 2010. M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 29 / 30
  42. 42. Bibliography [11] Mario Michael Krell, David Feess, and Sirko Straube. Balanced Relative Margin Machine The missing piece between FDA and SVM classification. Pattern Recognition Letters, 41:43–52, 2014. [12] Mario Michael Krell, Sirko Straube, Anett Seeland, Hendrik W¨ohrle, Johannes Teiwes, Jan Hendrik Metzen, Elsa Andrea Kirchner, and Frank Kirchner. pySPACE - A Signal Processing and Classification Environment in Python. Frontiers in Neuroinformatics, 7(40), 2013. [13] Jan Hendrik Metzen, Su Kyoung Kim, Timo Duchrow, Elsa Andrea Kirchner, and Frank Kirchner. On Transferring Spatial Filters in a Brain Reading Scenario. In Proceedings of the 2011 IEEE Workshop on Statistical Signal Processing, pages 797–800, Nice, France, 2011. [14] Jan Hendrik Metzen, Su Kyoung Kim, and Elsa Andrea Kirchner. Minimizing Calibration Time for Brain Reading. In Rudolf Mester and Michael Felsberg, editors, Pattern Recognition, volume 6835 of Lecture Notes in Computer Science, pages 366–375. Springer Berlin Heidelberg, Frankfurt, Germany, 2011. [15] Jan Hendrik Metzen and Elsa Andrea Kirchner. Rapid Adaptation of Brain Reading Interfaces based on Threshold Adjustment. In Proceedings of the 2011 Conference of the German Classification Society, (GfKl-2011), page 138, Frankfurt, Germany, 2011. [16] Anett Seeland, Hendrik W¨ohrle, Sirko Straube, and Elsa Andrea Kirchner. Online movement prediction in a robotic application scenario. In 6th International IEEE EMBS Conference on Neural Engineering (NER), pages 41–44, San Diego, California, 2013. [17] Hendrik W¨ohrle, Johannes Teiwes, Elsa Andrea Kirchner, and Frank Kirchner. A Framework for High Performance Embedded Signal Processing and Classification of Psychophysiological Data. In APCBEE Procedia. International Conference on Biomedical Engineering and Technology (ICBET-2013), 4th, May 19-20, Kopenhagen, Denmark. Elsevier, 2013. M M Krell pySPACE (https://github.com/pyspace) July 27, 2014 30 / 30

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