A portable EEG signal acquisition system and a limited-electrode channel classification network for SSVEP

• Published in: Frontiers in neurorobotics Vol. 18; p. 1502560
• Main Authors: Ma, Yunxiao, Huang, Jinming, Liu, Chuan, Shi, Meiyu
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 15.01.2025; Frontiers Media S.A
• Subjects: Attention; BCI systems; Brain research; Brain-Computer Interfaces (BCIs); Classification; Communication; Data collection; EEG; Electrodes; Electroencephalography; Experiments; limited channels; Neuroscience; Robot Operating System (ROS); Robotics; Robots; Signal processing; steady-state visual evoked potential (SSVEP); User interface; Visual evoked potentials; Robot Operating System (ROS); BCI systems; limited channels; Brain-Computer Interfaces (BCIs); steady-state visual evoked potential (SSVEP)
• ISSN: 1662-5218; 1662-5218
• DOI: 10.3389/fnbot.2024.1502560

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.