AUTHOR=Liu Chang , You Jia , Wang Kun , Zhang Shanshan , Huang Yining , Xu Minpeng , Ming Dong 

TITLE=Decoding the EEG patterns induced by sequential finger movement for brain-computer interfaces

JOURNAL=Frontiers in Neuroscience

VOLUME=Volume 17 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2023.1180471

DOI=10.3389/fnins.2023.1180471

ISSN=1662-453X

ABSTRACT=In recent years, motor imagery-based BCI (MI-BCI) has developed rapidly due to its great potential in neurological rehabilitation. However, the controllable instruction set limits its application in daily life. To extend the instruction set, we proposed a novel movement intention encoding paradigm based on sequential finger movement. Ten subjects participated in the offline experiment. During the experiment, they were required to press the key sequentially (i.e. Left→Left (LL), Right→Right (RR), Left→Right (LR), Right→Left (RL)) using the left or right index finger at about 1s intervals under the auditory prompt of 1 Hz. The movement-related cortical potential (MRCP) and event-related desynchronization (ERD) features were used to investigate the electroencephalography (EEG) variation induced by the sequential finger movement tasks. Twelve subjects participated in an online experiment to verify the feasibility of the proposed paradigm. As a result, both the MRCP and ERD feature showed the special temporal-spatial EEG patterns of different sequential finger movement tasks. For the offline experiment, the average classification accuracy of the four tasks was 71.69%, with the highest accuracy of 79.26%. For the online experiment, the average accuracies were 83.33% and 82.71%, for LL-versus-RR and LR-versus-RL respectively. This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful to optimize the encoding method of motor-related EEG information and provide a promising approach to extend the instruction set of the movement intention-based BCIs.