Exemple #1
0
 def __init__(self, n_components=4, reg=None, log=None,
              transform_into='csp_space', norm_trace=False, name="CSP"):
     super(CSP, self).__init__(name)
     self.n_components = n_components
     self.reg = reg
     self.log = log
     self.transform_into = transform_into
     self.norm_trace = norm_trace
     self.model = mne_CSP(n_components, reg, log, 'epoch', transform_into, norm_trace)
Exemple #2
0
                                    s_id[s],
                                    npp_params,
                                    clean=False,
                                    physical=True,
                                    downsample=False)

            data_size = y_train.shape[0]
            shuffle_index = utils.shuffle_data(data_size)
            x_train = x_train[shuffle_index]
            x_train = np.squeeze(x_train)
            y_train = y_train[shuffle_index]

            print(x_train.shape)

            csp = mne_CSP(n_components=6,
                          transform_into='average_power',
                          log=False,
                          cov_est='epoch')

            # build model
            lr = LogisticRegression(solver='sag', max_iter=200, C=0.01)
            model = Pipeline([('csp_power', csp), ('LR', lr)])
            model.fit(x_train, y_train)

            # Test Model
            y_pred = np.argmax(model.predict_proba(np.squeeze(x_test)), axis=1)
            bca = utils.bca(y_test, y_pred)
            acc = np.sum(y_pred == y_test).astype(np.float32) / len(y_pred)
            print('{}: acc-{} bca-{}'.format(data_name, acc, bca))
            acc_mean += acc
            bca_mean += bca