scholarly journals Assessing the effect of aging on orientation selectivity of visual mechanisms with the steady state visually evoked potential

2010 ◽  
Vol 8 (6) ◽  
pp. 424-424
Author(s):  
S. Govenlock ◽  
K. Kliegl ◽  
A. Sekuler ◽  
P. Bennett
Keyword(s):  
Steady State ◽  
Evoked Potential ◽  
Orientation Selectivity ◽  
Visually Evoked Potential

Steady-State Visually Evoked Potential topography associated with a visual vigilance task

1990 ◽  
Vol 3 (2) ◽  
pp. 337-347 ◽  
Author(s):  
Richard B. Silberstein ◽  
Mark A. Schier ◽  
Andrew Pipingas ◽  
Joseph Ciorciari ◽  
Stephen R. Wood ◽  
...  
Keyword(s):  
Steady State ◽  
Evoked Potential ◽  
Vigilance Task ◽  
Visually Evoked Potential

scholarly journals Design of Wearable Headset with Steady State Visually Evoked Potential-Based Brain Computer Interface

Micromachines ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 681
Author(s):  
Bor-Shyh Lin ◽  
Bor-Shing Lin ◽  
Tzu-Hsiang Yen ◽  
Chien-Chin Hsu ◽  
Yao-Chin Wang
Keyword(s):  
Steady State ◽  
Information Transfer ◽  
Evoked Potential ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Signal Quality ◽  
Eeg Signal ◽  
Visually Evoked Potential ◽  
Information Transfer Rate ◽  
Field Programmable

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).

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