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

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

doi:10.3389/fnins.2023.1180471

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

Journal Article · Tue Aug 29 00:00:00 EDT 2023 · Frontiers in Neuroscience (Online)

DOI:https://doi.org/10.3389/fnins.2023.1180471· OSTI ID:1997293

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

Objective

In recent years, motor imagery-based brain–computer interfaces (MI-BCIs) have developed rapidly due to their 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.

Approach

Ten subjects participated in the offline experiment. During the experiment, they were required to press a key sequentially [i.e., Left→Left (LL), Right→Right (RR), Left→Right (LR), and Right→Left (RL)] using the left or right index finger at about 1 s intervals under an 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.

Main results

As a result, both the MRCP and ERD features showed the specific 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.

Significance

This paper demonstrated the feasibility of the proposed sequential finger movement paradigm through offline and online experiments. This study would be helpful for optimizing the encoding method of motor-related EEG information and providing a promising approach to extending the instruction set of the movement intention-based BCIs.

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Sponsoring Organization:: USDOE Office of Electricity (OE), Advanced Grid Research & Development. Power Systems Engineering Research

OSTI ID:: 1997293

Journal Information:: Frontiers in Neuroscience (Online), Journal Name: Frontiers in Neuroscience (Online) Vol. 17; ISSN 1662-453X

Publisher:: Frontiers Media SACopyright Statement

Country of Publication:: Switzerland

Language:: English

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