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ORIGINAL RESEARCH article

Front. Neurorobot.
Volume 18 - 2024 | doi: 10.3389/fnbot.2024.1502560

A Portable EEG Signal Acquisition System and a Limited-Electrode Channel Classification Network for SSVEP

Provisionally accepted
Yunxiao Ma Yunxiao Ma Jinming Huang Jinming Huang *Chuan Liu Chuan Liu Meiyu Shi Meiyu Shi
  • Qufu Normal University, Qufu, China

The final, formatted version of the article will be published soon.

    Brain-computer interfaces (BCIs) have garnered significant research attention, yet their complexity has hindered widespread adoption in daily life. Most current electroencephalography (EEG) systems rely on wet electrodes and numerous electrodes to enhance signal quality, making them impractical for everyday use. Portable and wearable devices offer a promising solution, but the limited number of electrodes in specific regions can lead to missing channels and reduced BCI performance. To overcome these challenges and enable better integration of BCI systems with external devices, this study developed an EEG signal acquisition platform (Gaitech BCI) based on the Robot Operating System (ROS) using a 10-channel dry electrode EEG device. Additionally, a multi-scale channel attention selection network based on the Squeeze-and-Excitation (SE) module (SEMSCS) is proposed to improve the classification performance of portable BCI devices with limited channels. Steady-state visual evoked potential (SSVEP) data were collected using the developed BCI system to evaluate both the system and network performance. Offline data from ten subjects were analyzed using within-subject and cross-subject experiments, along with ablation studies. The results demonstrated that the SEMSCS model achieved better classification performance than the comparative reference model, even with a limited number of channels.Additionally, the implementation of online experiments offers a rational solution for controlling external devices via BCI.

    Keywords: Brain-computer interfaces (BCIs), Robot Operating System (ROS), BCI systems, steady-state visual evoked potential (SSVEP), limited channels

    Received: 27 Sep 2024; Accepted: 30 Dec 2024.

    Copyright: © 2024 Ma, Huang, Liu and Shi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jinming Huang, Qufu Normal University, Qufu, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.