Abstract
Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via recognizing brain activities without manual operation. However, for most current BCI systems, conventional electroencephalography (EEG) machines and computers are usually required to acquire EEG signal and translate them into control commands, respectively. The sizes of the above machines are usually large, and this increases the limitation for daily applications. Moreover, conventional EEG electrodes also require conductive gels to improve the EEG signal quality. This causes discomfort and inconvenience of use, while the conductive gels may also encounter the problem of drying out during prolonged measurements. In order to improve the above issues, a wearable headset with steady-state visually evoked potential (SSVEP)-based BCI is proposed in this study. Active dry electrodes were designed and implemented to acquire a good EEG signal quality without conductive gels from the hairy site. The SSVEP BCI algorithm was also implemented into the designed field-programmable gate array (FPGA)-based BCI module to translate SSVEP signals into control commands in real time. Moreover, a commercial tablet was used as the visual stimulus device to provide graphic control icons. The whole system was designed as a wearable device to improve convenience of use in daily life, and it could acquire and translate EEG signal directly in the front-end headset. Finally, the performance of the proposed system was validated, and the results showed that it had excellent performance (information transfer rate = 36.08 bits/min).
Highlights
Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via brain activity signals [1,2,3,4]
The state visually evoked potential (SSVEP) BCI algorithm was implemented in the field-programmable gate array (FPGA)-based BCI module to process SSVEP signal in real time
The FPGA architecture could provide the advantages of parallel signal processing to perform complex calculations in a few cycle times, and its small size allowed it to be embedded into a wearable device
Summary
Brain–computer interface (BCI) is a system that allows people to communicate directly with external machines via brain activity signals [1,2,3,4]. Most of the above SSVEP-based BCI systems require a conventional EEG machine and EEG electrodes with conductive gels to acquire SSVEP signals. Conventional EEG electrodes with conductive gels were still used to acquire SSVEP They indicated that the ITR of the phase coding method was relatively lower than that of the frequency coding method. A novel active dry electrode was designed to acquire SSVEP in hairy site without conductive gels. The BCI algorithm was implemented in FPGA to reduce the size of the back-end BCI platform Both the active dry electrodes and the BCI platform were embedded in a wearable mechanical design to acquire and translate EEG signals directly. The experimental results showed that the proposed wearable SSVEP-based BCI could effectively acquire SSVEP in hairy site without conductive gels and provide a good ITR. The proposed system can be viewed as a good prototype of wearable BCI, and it may be applied to many daily life applications in the future
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