Integrating EEG and MEG Signals to Improve Motor Imagery Classification in Brain-Computer Interface

Int J Neural Syst. 2019 Feb;29(1):1850014. doi: 10.1142/S0129065718500144. Epub 2018 Apr 2.

Abstract

We adopted a fusion approach that combines features from simultaneously recorded electroencephalogram (EEG) and magnetoencephalogram (MEG) signals to improve classification performances in motor imagery-based brain-computer interfaces (BCIs). We applied our approach to a group of 15 healthy subjects and found a significant classification performance enhancement as compared to standard single-modality approaches in the alpha and beta bands. Taken together, our findings demonstrate the advantage of considering multimodal approaches as complementary tools for improving the impact of noninvasive BCIs.

Keywords: Classifier fusion; EEG; MEG; brain–computer interface; motor imagery.

MeSH terms

  • Adult
  • Alpha Rhythm / physiology
  • Beta Rhythm / physiology
  • Brain-Computer Interfaces / standards*
  • Cerebral Cortex / physiology*
  • Electroencephalography / methods*
  • Humans
  • Imagination / physiology*
  • Magnetoencephalography / methods*
  • Motor Activity / physiology*
  • Signal Processing, Computer-Assisted*
  • Young Adult