Towards Improving Motor Imagery Brain–Computer Interface Using Multimodal Speech Imagery

Abstract Purpose The brain–computer interface (BCI) based on motor imagery (MI) has attracted extensive interest due to its spontaneity and convenience. However, the traditional MI paradigm is limited by weak features in evoked EEG signal, which often leads lower classification performance. Methods In this paper, a novel proposed improve BCI performance, speech imaginary combined with silent reading (SR) writing (WI), instead of imagining body movements. multimodal (imaginary voices movements) paradigm, subjects silently read Chinese Pinyin (pronunciation) imaginarily write characters, according cue. Results Eight participated binary tasks, carrying out different experiments for comparison. 77.03% average accuracy was obtained new versus 68.96% paradigm. Conclusion results show that evokes stronger features, benefits classification. This work opens view evoking activities/stimuli using specific paradigms BCI.