An Incremental Version of L-MVU for the Feature Extraction of MI-EEG

Abstract EN

Due to the nonlinear and high-dimensional characteristics of motor imagery electroencephalography (MI-EEG), it can be challenging to get high online accuracy.

As a nonlinear dimension reduction method, landmark maximum variance unfolding (L-MVU) can completely retain the nonlinear features of MI-EEG.

However, L-MVU still requires considerable computation costs for out-of-sample data.

An incremental version of L-MVU (denoted as IL-MVU) is proposed in this paper.

The low-dimensional representation of the training data is generated by L-MVU.

For each out-of-sample data, its nearest neighbors will be found in the high-dimensional training samples and the corresponding reconstruction weight matrix be calculated to generate its low-dimensional representation as well.

IL-MVU is further combined with the dual-tree complex wavelet transform (DTCWT), which develops a hybrid feature extraction method (named as IL-MD).

IL-MVU is applied to extract the nonlinear features of the specific subband signals, which are reconstructed by DTCWT and have the obvious event-related synchronization/event-related desynchronization phenomenon.

The average energy features of α and β waves are calculated simultaneously.

The two types of features are fused and are evaluated by a linear discriminant analysis classifier.

Based on the two public datasets with 12 subjects, extensive experiments were conducted.

The average recognition accuracies of 10-fold cross-validation are 92.50% on Dataset 3b and 88.13% on Dataset 2b, and they gain at least 1.43% and 3.45% improvement, respectively, compared to existing methods.

The experimental results show that IL-MD can extract more accurate features with relatively lower consumption cost, and it also has better feature visualization and self-adaptive characteristics to subjects.

The t-test results and Kappa values suggest the proposed feature extraction method reaches statistical significance and has high consistency in classification.