Multiscale Domain Gradient Boosting Models for the Automated Recognition of Imagined Vowels Using Multichannel EEG Signals

This letter proposes the multiscale domain gradient boosting-based approach for the automated recognition of imagined vowels using the multichannel electroencephalogram (MCEEG) signals. The multiscale analysis of the MCEEG signals is performed using multivariate automatic singular spectrum analysis and multivariate fast and adaptive empirical mode decomposition methods. The features such as bubble entropy, energy, slope domain entropy, sample entropy, and L1-norm are evaluated from the multiscale domain modes of the MCEEG signals. The extreme gradient boosting and light gradient boosting machine models are employed for imagined vowel recognition task as //a// versus //e// versus //i// versus //o// versus //u// using the multiscale domain features of the MCEEG signals. A publicly available MCEEG database has been used to test the performance of the proposed approach. The results demonstrate that the proposed approach has achieved an overall accuracy of 51.47%, which is higher as compared to other imagined vowel recognition methods using the same database comprising the MCEEG signals.