Abstract
Recently, the robotic arm control system based on a brain-computer interface (BCI) has been employed to help the disabilities to improve their interaction abilities without body movement. However, it's the main challenge to implement the desired task by a robotic arm in a three-dimensional (3D) space because of the instability of electroencephalogram (EEG) signals and the interference by the spontaneous EEG activities. Moreover, the free motion control of a manipulator in 3D space is a complicated operation that requires more output commands and higher accuracy for brain activity recognition. Based on the above, a steady-state visual evoked potential (SSVEP)-based synchronous BCI system with six stimulus targets was designed to realize the motion control function of the seven degrees of freedom (7-DOF) robotic arm. Meanwhile, a novel template-based method, which builds the optimized common templates (OCTs) from various subjects and learns spatial filters from the common templates and the multichannel EEG signal, was applied to enhance the SSVEP recognition accuracy, called OCT-based canonical correlation analysis (OCT-CCA). The comparison results of offline experimental based on a public benchmark dataset indicated that the proposed OCT-CCA method achieved significant improvement of detection accuracy in contrast to CCA and individual template-based CCA (IT-CCA), especially using a short data length. In the end, online experiments with five healthy subjects were implemented for achieving the manipulator real-time control system. The results showed that all five subjects can accomplish the tasks of controlling the manipulator to reach the designated position in the 3D space independently.
Highlights
How to realize the information interaction between people and external equipment and conveniently has always been the goal of human beings, and the brain-computer interface (BCI) provides this possibility
This study focuses on the state visual evoked potential (SSVEP)-based robotic arm control system to provide multiple command options, the subjects can elicit the evoked potentials to obtain EEG signals by gazing at visual flickers and the commands of the 3D motion of the robotic arm are generated from the result of the SSVEP signals recognition
The average classification accuracy of the optimized common templates (OCTs)-canonical correlation analysis (CCA) method is higher than CCA and individual template-based CCA (IT-CCA) in every time window and a significant improvement is achieved when the time window was less than 3 s
Summary
How to realize the information interaction between people and external equipment and conveniently has always been the goal of human beings, and the brain-computer interface (BCI) provides this possibility. Compared to ordinary input interactive devices, the BCI input is the brain signals recorded by electrodes on the head, and the output applications can be controlled directly from the brain, Optimized Common Template-Based CCA such as robotic arms (Aljalal et al, 2020; Zhu et al, 2020), wheelchairs (Li et al, 2016; Deng et al, 2019; Bonci et al, 2021), character speller systems (Rezeika et al, 2018; Podmore et al, 2019), and other devices (Gao et al, 2019). Compared to the EEG paradigms of P300 and MI, the SSVEP-based BCI system is preferable in robotic arms control owing to the little training and relatively high recognition accuracy (Ge et al, 2019; Chen et al, 2020; Zhang et al, 2020). It can be accepted that how to improve the SSVEP recognition accuracy is a key factor in determining the performance of the entire SSVEP-based robotic arm system
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