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| '''Thit toolbox allows you to do support vector machine (SVM) and linear discriminant analysis (LDA) classification on the MEG timeseries. ''' | = Tutorial 13: Decoding Conditions = ''Author: Seyed-Mahdi Khaligh-Razavi'' |
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| These set of functions allow you to do support vector machine (SVM) and linear discriminant analysis (LDA) classification on your MEG data across time. Input: the input is channel data from two conidtions (e.g. condA and condB) across time. Number of samples per condition should be the same for both condA and condB. Each of them should be at least contain two samples. Output: the output is a decoding curve across time, showing your decoding accuracy (decoding condA vs. condB) at timepoint 't'. |
Tutorial 13: Decoding Conditions
Author: Seyed-Mahdi Khaligh-Razavi
These set of functions allow you to do support vector machine (SVM) and linear discriminant analysis (LDA) classification on your MEG data across time.
Input: the input is channel data from two conidtions (e.g. condA and condB) across time. Number of samples per condition should be the same for both condA and condB. Each of them should be at least contain two samples.
Output: the output is a decoding curve across time, showing your decoding accuracy (decoding condA vs. condB) at timepoint 't'.