TY - JOUR
T1 - VR-enabled portable brain-computer interfaces via wireless soft bioelectronics
AU - Mahmood, Musa
AU - Kim, Noah
AU - Mahmood, Muhammad
AU - Kim, Hojoong
AU - Kim, Hyeonseok
AU - Rodeheaver, Nathan
AU - Sang, Mingyu
AU - Yu, Ki Jun
AU - Yeo, Woon Hong
N1 - Publisher Copyright:
© 2022 Elsevier B.V.
PY - 2022/8/15
Y1 - 2022/8/15
N2 - Noninvasive, wearable brain-computer interfaces (BCI) find limited use due to their obtrusive nature and low information. Currently available portable BCI systems are limited by device rigidity, bulky form factors, and gel-based skin-contact electrodes – and therefore more prone to noise and motion artifacts. Here, we introduce virtual reality (VR)-enabled split-eye asynchronous stimulus (SEAS) allowing a target to present different stimuli to either eye. This results in unique asynchronous stimulus patterns measurable with as few as four EEG electrodes, as demonstrated with improved wireless soft electronics for portable BCI. This VR-embedded SEAS paradigm demonstrates potential for improved throughput with a greater number of unique stimuli. A wearable soft platform featuring dry needle electrodes and shielded stretchable interconnects enables high throughput decoding of steady-state visually evoked potentials (SSVEP) for a text spelling interface. A combination of skin-conformal electrodes and soft materials offers high-quality recordings of SSVEP with minimal motion artifacts, validated by comparing the performance with a conventional wearable system. A deep-learning algorithm provides real-time classification, with an accuracy of 78.93% for 0.8 s and 91.73% for 2 s with 33 classes from nine human subjects, allowing for a successful demonstration of VR text spelling and navigation of a real-world environment. With as few as only four data recording channels, the system demonstrates a highly competitive information transfer rate (243.6 bit/min). Collectively, the VR-enabled soft system offers unique advantages in wireless, real-time monitoring of brain signals for portable BCI, neurological rehabilitation, and disease diagnosis.
AB - Noninvasive, wearable brain-computer interfaces (BCI) find limited use due to their obtrusive nature and low information. Currently available portable BCI systems are limited by device rigidity, bulky form factors, and gel-based skin-contact electrodes – and therefore more prone to noise and motion artifacts. Here, we introduce virtual reality (VR)-enabled split-eye asynchronous stimulus (SEAS) allowing a target to present different stimuli to either eye. This results in unique asynchronous stimulus patterns measurable with as few as four EEG electrodes, as demonstrated with improved wireless soft electronics for portable BCI. This VR-embedded SEAS paradigm demonstrates potential for improved throughput with a greater number of unique stimuli. A wearable soft platform featuring dry needle electrodes and shielded stretchable interconnects enables high throughput decoding of steady-state visually evoked potentials (SSVEP) for a text spelling interface. A combination of skin-conformal electrodes and soft materials offers high-quality recordings of SSVEP with minimal motion artifacts, validated by comparing the performance with a conventional wearable system. A deep-learning algorithm provides real-time classification, with an accuracy of 78.93% for 0.8 s and 91.73% for 2 s with 33 classes from nine human subjects, allowing for a successful demonstration of VR text spelling and navigation of a real-world environment. With as few as only four data recording channels, the system demonstrates a highly competitive information transfer rate (243.6 bit/min). Collectively, the VR-enabled soft system offers unique advantages in wireless, real-time monitoring of brain signals for portable BCI, neurological rehabilitation, and disease diagnosis.
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U2 - 10.1016/j.bios.2022.114333
DO - 10.1016/j.bios.2022.114333
M3 - Article
C2 - 35525171
AN - SCOPUS:85129362985
SN - 0956-5663
VL - 210
JO - Biosensors and Bioelectronics
JF - Biosensors and Bioelectronics
M1 - 114333
ER -