xdawn¶
Module: missions.nodes.spatial_filtering.xdawn¶
xDAWN and variants for enhancing event-related potentials
Inheritance diagram for pySPACE.missions.nodes.spatial_filtering.xdawn:
Class Summary¶
XDAWNNode([erp_class_label, ...]) |
xDAWN spatial filter for enhancing event-related potentials. |
SparseXDAWNNode(lambda_[, erp_class_label, ...]) |
Sparse xDAWN spatial filter for enhancing event-related potentials. |
AXDAWNNode([comp_type, lambda_signal, ...]) |
Adaptive xDAWN spatial filter for enhancing event-related potentials. |
Classes¶
XDAWNNode¶
-
class
pySPACE.missions.nodes.spatial_filtering.xdawn.XDAWNNode(erp_class_label=None, retained_channels=None, load_filter_path=None, visualize_pattern=False, **kwargs)[source]¶ Bases:
pySPACE.missions.nodes.spatial_filtering.spatial_filtering.SpatialFilteringNodexDAWN spatial filter for enhancing event-related potentials.
xDAWN tries to construct spatial filters such that the signal-to-signal plus noise ratio is maximized. This spatial filter is particularly suited for paradigms where classification is based on event-related potentials.
For more details on xDAWN, please refer to http://www.icp.inpg.fr/~rivetber/Publications/references/Rivet2009a.pdf
References
main source: xDAWN author Rivet, B. and Souloumiac, A. and Attina, V. and Gibert, G. journal Biomedical Engineering, IEEE Transactions on title xDAWN Algorithm to Enhance Evoked Potentials: Application to Brain-Computer Interface year 2009 month aug. volume 56 number 8 pages 2035 -2043 doi 10.1109/TBME.2009.2012869 ISSN 0018-9294 minor source: adaptive xDAWN author Woehrle, H. and Krell, M. M. and Straube, S. and Kim, S. U., Kirchner, E. A. and Kirchner, F. title An Adaptive Spatial Filter for User-Independent Single Trial Detection of Event-Related Potentials journal IEEE Transactions on Biomedical Engineering publisher IEEE doi 10.1109/TBME.2015.2402252 volume 62 issue 7 pages 1696 - 1705 year 2015 - Parameters
erp_class_label: Label of the class for which an ERP should be evoked. For instance “Target” for a P300 oddball paradigm.
(recommended, default: ‘Target’)
retained_channels: Determines how many of the pseudo channels are retained. Default is None which means “all channels”.
(optional, default: None)
load_filter_path: An absolute path from which the spatial filters can be loaded. If not specified, these filters are learned from the training data.
(optional, default: None)
visualize_pattern: If value is true, a visualization of the learned spatial filters is stored.
The visualisation is divided into two components. First of all each transformation is visualized separately. Since the visualization itself may not be so meaningful, there exists another combined visualization, which shows the filter (u_i) with the underlying spatial distribution (w_i, parameter names taken from paper). The number of filters equals the number of original channels. Normally only the first channels matter and the rest corresponds to different noise components.
To avoid storing to many pictures, the retained_channels parameter is used to restrict the number.
(optional, default: False)
Exemplary Call
- node : xDAWN parameters: erp_class_label : "Target" retained_channels : 32 store : True
Author: Jan Hendrik Metzen (jhm@informatik.uni-bremen.de)
Created: 2011/07/05
POSSIBLE NODE NAMES: - XDAWN
- XDAWNNode
- xDAWN
POSSIBLE INPUT TYPES: - TimeSeries
Class Components Summary
_execute(data)Apply the learned spatial filters to the given data point _plotTimeSeriesInEC(values[, vmin, vmax, ...])_stop_training([debug])_train(data, label)Train node on given example data for class label. is_supervised()Returns whether this node requires supervised training is_trainable()Returns whether this node is trainable. store_state(result_dir[, index])Stores this node in the given directory result_dir
SparseXDAWNNode¶
-
class
pySPACE.missions.nodes.spatial_filtering.xdawn.SparseXDAWNNode(lambda_, erp_class_label='Target', num_selected_electrodes=None, **kwargs)[source]¶ Bases:
pySPACE.missions.nodes.spatial_filtering.xdawn.XDAWNNodeSparse xDAWN spatial filter for enhancing event-related potentials.
xDAWN tries to construct spatial filters such that the signal-to-signal plus noise ratio (SSNR) is maximized. This spatial filter is particularly suited for paradigms where classification is based on event-related potentials. In contrast to the standard xDAWN algorithm, this node tries to minimize the electrodes that have non-zero weights in the spatial filters while at the same time trying to maximize the signal-to-signal plus noise ratio. This property is used for electrode selection, i.e. only those electrodes need to be set that obtained non-zero weights.
For more details on Sparse xDAWN, please refer to http://www.gipsa-lab.inpg.fr/~bertrand.rivet/references/RivetEMBC10.pdf
Parameters
lambda_: Determines the relative influence of the two objectives (maximization of SSNR and minimization of electrodes with non-zero weights). If lambda_ is 0, only the SSNR is relevant (like in standard xDAWN). The larger lambda_, the weaker is the influence of the SSNR.
erp_class_label: Label of the class for which an ERP should be evoked. For instance “Target” for a P300 oddball paradigm.
(recommended, default:’Target’)
num_selected_electrodes: Determines how many electrodes keep a non-zero weight.
Exemplary Call
- node : Sparse_xDAWN parameters : lambda_ : 0.1 erp_class_label : "Target" num_selected_electrodes : 2 store : True
Author: Jan Hendrik Metzen (jhm@informatik.uni-bremen.de)
Created: 2011/08/22
POSSIBLE NODE NAMES: - SparseXDAWN
- Sparse_xDAWN
- SparseXDAWNNode
POSSIBLE INPUT TYPES: - TimeSeries
Class Components Summary
_execute(data)Project the data onto the selected channels. _gradient_optimization(objective_function, ...)_stop_training([debug])get_filters()store_state(result_dir[, index])Stores this node in the given directory result_dir
AXDAWNNode¶
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class
pySPACE.missions.nodes.spatial_filtering.xdawn.AXDAWNNode(comp_type='rls', lambda_signal=1.0, lambda_noise=1.0, delta=0.25, w_ini=0.01, regularization=False, lambda_reg=100, **kwargs)[source]¶ Bases:
pySPACE.missions.nodes.spatial_filtering.xdawn.XDAWNNodeAdaptive xDAWN spatial filter for enhancing event-related potentials.
In general, the adaptive xDAWN algorithm works as the conventional xDAWN algorithm, but is adapted to be able to evolve over time.
Therefore, instead of using the QR and SV decomposition, this node uses the generalized Eigendecomposition to find the optimal filters.
The methods are based on iteratively computing the generalized eigendecomposition (GED) with the algorithm from “Fast RLS-like algorithm for generalized eigendecomposition and its applications” (2004) by Yadunandana N. Rao , Jose , C. Principe , Tan F. Wong
In general, this works as follows:
- The noise and signal autocorrelation matrices are adapted with more incoming samples.
- The inverse noise autocorrelation is updated.
- The weight vectors (i.e. general eigenvectors) are updated.
- These are used to get the actual filters.
Optionally, update coefficients can be used for adapting the filter estimate.
For using regularization techniques, the noise autocorrelation is initialized with the regularization matrix instead of using zeros.
References
main source: axDAWN author Woehrle, H. and Krell, M. M. and Straube, S. and Kim, S. U. and Kirchner, E. A. and Kirchner, F. title An Adaptive Spatial Filter for User-Independent Single Trial Detection of Event-Related Potentials journal IEEE Transactions on Biomedical Engineering publisher IEEE doi 10.1109/TBME.2015.2402252 volume 62 issue 7 pages 1696 - 1705 year 2015 main source: raxDAWN author Krell, M. M. and Seeland, A. and Woehrle, H. title raxDAWN: Circumventing Overfitting of the Adaptive xDAWN book Proceedings of the International Congress on Neurotechnology, Electronics and Informatics publisher SciTePress doi 10.5220/0005657500680075 year 2015 - Parameters
lambda_signal: - update coefficient for weighting
old samples of the signal.
(optional, default: 1.0)
lambda_noise: forgetting factor for weighting old samples of the noise.
(optional, default: 1.0)
comp_type: Type of computation
Either use iterative GED (rls) or the eigh function from scipy (eig). eig will not enable an iterative procedure and is just integrated for comparison with the original method and for testing the incremental approach. Depending on the scipy version, the
scipy.linalg.eigh()function might raise an error or deliver unexpected results.(optional, default: rls)
delta: Factor for identity matrix in initialization of inverse correlation matrix.
(optional, default: 0.25)
w_ini: Factor for random filter initialization
(optional, default: 0.01)
regularization: Currently only Tikhonov regularization is implemented. By default no regularization is active using False. For the regularization, the lambda_reg parameter should be optimized.
(optional, default: False)
lambda_reg: Positive regularization constant to weight between signal-plus-noise energy and chosen regularization term (see also the regularization parameter). Values between 100 and 1000 seem to be appropriate. Values below 1 won’t have a real effect. This parameter should be roughly optimized, when used.
(optional, default: 100)
Exemplary Call
- node : axDAWN parameters: erp_class_label : "Target" retained_channels : 32 store : True lambda_signal : 0.99 lambda_noise : 0.99 lambda_reg : 100
Author: Hendrik Woehrle (hendrik.woehrle@dfki.de)
Created: 2012/05/25
POSSIBLE NODE NAMES: - axDAWN
- AXDAWNNode
- AXDAWN
POSSIBLE INPUT TYPES: - TimeSeries
Class Components Summary
_execute(data)Apply the learned spatial filters to the given data point _inc_train(data, label)_stop_training([debug])_train(data, class_label)Incremental update procedure adapt_inverse_noise_correlation(data)initialize_filters(data)Filter initialization which requires the first data sample store_state(result_dir[, index])Stores this node in the given directory result_dir -
__init__(comp_type='rls', lambda_signal=1.0, lambda_noise=1.0, delta=0.25, w_ini=0.01, regularization=False, lambda_reg=100, **kwargs)[source]¶