FilterBankCommonSpatialPattern¶
Extract signal components across multiple bands whose variance optimally discriminates between two conditions.
FBCSP generalizes the basic CSP method to multiple bands (yielding number of bands times number of pattern pairs times 2 channels), and like CSP it can be used as an adaptive preprocessing step for a multichannel signal, such as EEG, EMG, or MEG, whose variance shall subsequently be used in a classification setup (e.g., to predict some binary target variable, for instance in order to discriminate between two possible cognitive states). The resulting components will usually yield better spectral features than the raw channels, leading to better classification accuracy. This node will calibrate itself if it receives a non-streaming (offline) chunk that has a time, space, and instance axis, and which has a target value for each instance (similarly to how machine learning nodes operate). Instances correspond to labeled trials, the space axis represents the channels which are being filtered, and time are the time points of each trial segment. FBCSP should be preceded at least by a highpass filter (e.g., FIR or IIR prior to segmentation). FBCSP only works for two classes. FBCSP is a state-of-the-art spatio-spectral filtering method and is on par with the alternative Spectrally Weighted Common Spatial Patterns (Spec-CSP) method. The main differences are that FBCSP uses predefined bands, while Spec-CSP optimizes the bands on the fly, which can work better, but can also fall short if the optimization ran into a bad local optimum. Since the method can be used with a number of standard frequency bands, it can be used in cases where the correct frequency band is not known, and as such it can be useful in settings where there is little established a priori knowledge on those bands, for instance EEG collected in non-traditional tasks. Since FBCSP will generate a relatively large number of features compared to CSP (by default on the order of 30), many of which are going to be uninformative, it is advisable to use a sparse classifier, such as sparse logistic regression on the resulting features. Tip: a continuous time series with markers can be segmented into multiple labeled trials / segments using the Assign Target Markers node followed by the Segmentation node. Version 1.0.0
Ports/Properties¶
data¶
Data to process.
nof¶
Number of pattern pairs to compute per band. This determines the number of output channels for the band ( which is 2x this value) and thus the dimensionality of the feature space. Typical values are 2-4; while one can generate more features ( up to the number of input channels), these will be increasingly less useful to the classifier.
bands¶
Frequency bands of interest. This is a list of pairs of [low, high] entries, each of which defines another frequency band of interest. Example syntax: [[10,15],[7,30],[15,25]].
shrinkage¶
Shrinkage coefficient for covariance matrix estimation.
min_fft_size¶
Minimum size of the FFT used in spectrum calculation. The chosen value is the greater of this and the next power of 2 greater than the length of the signal.
window_func¶
Type of window function to use. The data can optionally be windowed using this function, which is especially useful when multiple small overlapped windows are used.
window_param¶
Window parameter. Needed to determine the shape of the window if using kaiser, gaussian, slepian, or chebwin.
window_length¶
Length of overlapped windows in case of Welch spectral estimation. Using a smaller value (e.g., 1/4-1/8th of the chunk length) yields a smoother spectrum. The default is 1/2 of the chunk length.
window_unit¶
Unit in which the window length is given.
overlap_length¶
Amount of overlap of successive windows in Welch method. The default is half of the window length.
overlap_unit¶
Unit in which the overlap window length is given.
initialize_once¶
Do not recalibrate on subsequent offline chunks, even if they include target labels. If False, this node will recalibrate itself on any offline chunk that has data plus target labels.
cond_field¶
The name of the instance data field that contains the conditions to be discriminated. This parameter will be ignored if the packet has previously been processed by a DescribeStatisticalDesign node.
set_breakpoint¶
Set a breakpoint on this node. If this is enabled, your debugger (if one is attached) will trigger a breakpoint.
metadata¶
User-definable meta-data associated with the node. Usually reserved for technical purposes.