Multiclass Classification of EEG Motor Imagery Signals Based on Transfer Learning

Multiclass Classification of EEG signal is essential for brain computer interface (BCI) applications but extremely time consuming. We proposed a subject-weighted adaptive transfer learning method in conjunction with MLP and CNN classifiers for fast classification of multiclass EEG dataset.Analytic results show that CNN generally outperforms MLP in this multi-class classification. The use of transfer learning is efficient for building the predictive model without decreasing the accuracy and 2D CNN is more robust to between-subject variabilities.