An ECoG-Based Binary Classification of BCI Using Optimized Extreme Learning Machine
Algorithm 1
Given training dataset train_data, which consists of samples and their corresponding target labels. For binary classification, target labels can be only −1 or 1. Set activation function (x).
(1)
Let , which denotes the sample of training data; , which denotes the given target label of training data sample.
(2)
Preprocessing the whole data of classification.
(3)
Processing targets of training and targets of testing.
(4)
Calculate the singular value decomposition of .
(5)
Set number of hidden neurons .
(6)
Set input weights. Let .
(7)
Calculate hidden neuron output matrix under specific activation function .
(8)
Calculate output weights .
(9)
Input the testing data test_data. Then calculate hidden neuron output matrix H_test.
(10)
Calculate the actual output of testing data .
(11)
Calculate CPU time (seconds) spent by OELM predicting the whole testing data.
