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

Chang Liu1, Jia You2, Kun Wang1

  • 1Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.

Frontiers in Neuroscience
|September 14, 2023
PubMed
Summary

This study introduces a new brain-computer interface method using sequential finger movements to expand control options. This approach shows promise for improving brain-computer interfaces in neurological rehabilitation.

Keywords:
brain-computer interfaceelectroencephalographyevent-related desynchronizationmovement related cortical potentialssequential finger movements

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Motor imagery-based brain-computer interfaces (MI-BCIs) show potential for neurological rehabilitation.
  • Current MI-BCIs have limited controllable instruction sets, restricting real-world applications.
  • There is a need for novel encoding paradigms to expand the functionality of BCIs.

Purpose of the Study:

  • To propose and validate a novel movement-intention encoding paradigm based on sequential finger movements.
  • To extend the instruction set capabilities of motor imagery-based brain-computer interfaces.
  • To investigate the feasibility of using sequential finger movements for BCI control.

Main Methods:

  • An offline experiment involved 10 subjects performing sequential finger key presses (LL, RR, LR, RL) under auditory prompts.
  • Movement-related cortical potentials (MRCP) and event-related desynchronization (ERD) features were analyzed from EEG data.
  • An online experiment with 12 subjects verified the paradigm's practical application.

Main Results:

  • Both MRCP and ERD features exhibited distinct spatio-temporal EEG patterns corresponding to different sequential finger movement tasks.
  • The offline experiment achieved an average classification accuracy of 71.69% for the four tasks.
  • The online experiment demonstrated high average accuracies of 83.33% for LL-vs-RR and 82.71% for LR-vs-RL.

Conclusions:

  • The proposed sequential finger movement paradigm is feasible for BCI applications, as confirmed by offline and online experiments.
  • This approach offers a promising method for optimizing the encoding of motor-related EEG information.
  • The study contributes to extending the instruction set for movement intention-based BCIs, enhancing their utility.