A Wireless Multifunctional SSVEP-Based Brain–Computer Interface Assistive System

Abstract

Several kinds of brain-computer interface (BCI) systems have been proposed to compensate for the lack of medical technology for assisting patients who lose the ability to use motor functions to communicate with the outside world. However, most of the proposed systems are limited by their non-portability, impracticality and inconvenience because of the adoption of wired or invasive electroencephalography (EEG) acquisition devices. Another common limitation is the shortage of functions provided because of the difficulty of integrating multiple functions into one BCI system. In this study, we propose a wireless, non-invasive and multifunctional assistive system which integrates steady state visually evoked potential (SSVEP)-based BCI and a robotic arm to assist patients to feed themselves. Patients are able to control the robotic arm via the BCI to serve themselves food. Three other functions: video entertainment, video calling, and active interaction are also integrated. This is achieved by designing a functional menu and integrating multiple subsystems. A refinement decision-making mechanism is incorporated to ensure the accuracy and applicability of the system. Fifteen participants were recruited to validate the usability and performance of the system. The averaged accuracy and information transfer rate (ITR) achieved is 90.91% and 24.94 bit per min respectively. The feedback from the participants demonstrates that this assistive system is able to significantly improve the quality of daily life.

Brain Computer Interface-Based Action Observation Game Enhances Mu Suppression in Patients with Stroke

“…SSVEPs are natural responses to visual stimuli at specific frequencies, with the strongest response at frequencies approximately 15 Hz or 20 Hz [27]. This technique involves a blinking stimulus presented at a specific frequency that results in brain waves of the same frequency synchronizing in the occipital lobe, which can be used to control various devices and develop BCI applications [25,[27][28][29]. In a preliminary study, we demonstrated that mu suppression and SSVEP expression were simultaneously observed during AO training with a flickering action video and it was possible to confirm whether the user was actually watching the flickering action video [30].…”

Fully portable and wireless universal brain–machine interfaces enabled by flexible scalp electronics and deep learning algorithm

“…There is also an argument for the use of lightweight sensors and shorter leads in order to prevent dragging or movement artifacts that may occur with massy cap EEG systems 29 . However, the available mobile EEG systems [30][31][32][33] are still quite bulky and use rigid electronic and structural components with a large number of electrodes, which are not comfortable for daily use and real-world applications. Here, we introduce the first example of a fully portable, wireless, flexible, skin-like hybrid scalp electronics (referred to as 'SKINTRONICS') that includes a low-profile, flexible circuit, an ultrathin aerosol-jet printed skin electrode, and three flexible conductive polymer electrodes for mounting on the hairy scalp (occipital lobe).…”

“…The fully flexible, wearable system enables real-time long-range wireless data acquisition and accurate classification of SSVEP with a high information transfer rate from only two recording channels. Due to extreme mechanical compliance and small form factor, SKINTRONICS exhibits significant reduction of noise and electromagnetic interference, compared to the existing portable EEG systems with rigid electronic components 33,[35][36][37] . Additionally, the use of conformal electronic components allows for easy wearability on the back of the neck or other bare skin locations.…”

Towards Bridging the Gap Between Computational Intelligence and Neuroscience in Brain-Computer Interfaces With a Common Description of Systems and Data

“…Such a varying level of information coming from various researchers has created many hurdles and significant gaps in sharing, understanding, comparing, and importantly expanding knowledge in the BCI communities. For example, when researchers work on steady-state visually evoked potential (SSVEP) -based BCIs (Lin et al, 2018 ), it would be ideal for assuring reproducibility to provide, besides the original acquired data, information like the number of unique flickering stimuli that are presented to the user, the flickering rate, and time distribution (i.e., uniform or not), among others. Additionally, descriptions should add details from the hardware and signal processing perspectives such as the impedance of electrodes, type of reference used, applied signal filters, enable/disable DC-offset flag, and even appropriate labeled data produced by peaks detection algorithms, etc.…”