[Mne_analysis] CSP for Multi-Classification

Xi Zh timsasah at gmail.com
Sun Feb 24 16:32:09 EST 2019
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        External Email - Use Caution        

Hi Clemens,
Thank you very much for your quesitons. I tried 2,3,4,6, and 18 classes. I
found out that the best accuracy is in case of 2 classes and that by
increasing the number of classes the accuracy decreases.
I used cross_validation. for all cases (2,3,4,6, or 18 classes) Following
is part of my code as I copied some from

from mne.decoding import CSP
from sklearn.cross_validation import cross_val_score

X_train, X_test, y_train, y_test = train_test_split(my_array, y,
csp = CSP(n_components=15, reg=None, log=True, norm_trace=False)
log_reg = LogisticRegression()
clf = Pipeline([('CSP', csp), ('Reg', log_reg)])
my_score = cross_val_score(clf, X_train, y_train, cv=10, n_jobs=1,

Is it right this way for multi-classification? What would you suggest? What
do you mean by  CSP is a spatial filter, and you can train filters e.g. in
a 1-vs-rest scheme

Many thanks for your kind help

El sáb., 23 feb. 2019 a las 23:20, Brunner, Clemens (
clemens.brunner at uni-graz.at) (<clemens.brunner at uni-graz.at>) escribió:

>         External Email - Use Caution
> Hi!
> Could you please be more specific? What have you tried for the multi-class
> case (how many classes?) and which method yielded better results? Which
> performance metric did you consider? Which data (cross-validation)? CSP is
> a spatial filter, and you can train filters e.g. in a 1-vs-rest scheme.
> Clemens
> *From:* mne_analysis-bounces at nmr.mgh.harvard.edu <
> mne_analysis-bounces at nmr.mgh.harvard.edu> *On Behalf Of *tr rt
> *Sent:* Saturday, 23 February 2019 11:59
> *To:* mne_analysis at nmr.mgh.harvard.edu
> *Subject:* [Mne_analysis] CSP for Multi-Classification
> *        External Email - Use Caution        *
> Hi everyone,
> CSP (from mne.decoding import CSP) (Common Spatial Patterns) for
> Multi-Classification gives clearly worse accuracy than the binary
> classification. Would you suggest a better solution for the case of more
> than two classes? Maybe another kind of CSP? Another kind of Machine
> Learning methods? or even Deep Learning?
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