scholarly journals Investigation of Visual Stimulus Signals Using Hue Change for SSVEP

2021 ◽  
Vol 11 (3) ◽  
pp. 1045
Author(s):  
Yoshihiro Sato ◽  
Yuichiro Kitamura ◽  
Takamichi Hirata ◽  
Yue Bao
Keyword(s):  
Steady State ◽  
Visual Evoked Potentials ◽  
Visual Stimulus ◽  
Color Change ◽  
Visual Stimulation ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Black And White ◽  
Discrimination Rate ◽  
Flash Stimulus

This study focuses on the problem of eye irritation when measuring steady-state visual evoked potentials (SSVEPs) using a brain–computer interface and aims to clarify experimentally visual stimulus signals that do not cause discomfort to users. To this end, a method is proposed that introduces a flash stimulus in which the color is changed by changing its hue. This reduces the change in brightness while providing a color change, thereby facilitating visual stimulation with less discomfort. In experiments conducted, flash stimuli of the primary colors red, green, and blue and colors with different hues of 5–45° from these primary colors were generated to investigate the algorithm accuracy of SSVEP and discomfort. Subjective questionnaire and CFF values, which are ophthalmic parameters, were obtained for the subjects and compared to the discrimination rate. As a result of the comparison, it was confirmed that the fatigue level of the visual stimulus generated by the proposed hue change was lower than that of the conventional black-and-white stimulus. It was also confirmed that the combination of the hue difference and frequency could obtain the same discrimination rate as the conventional method.

Visual Stimulus Background Effects on SSVEP-Based BCI Towards a Practical Robot Car Control

2015 ◽  
Vol 12 (02) ◽  
pp. 1550014 ◽  
Author(s):  
Xiaokang Shu ◽  
Lin Yao ◽  
Jianjun Meng ◽  
Xinjun Sheng ◽  
Xiangyang Zhu
Keyword(s):  
Steady State ◽  
Visual Evoked Potentials ◽  
Visual Stimulus ◽  
Classification Accuracy ◽  
Cross Validation ◽  
Brain Computer Interface ◽  
Video Feedback ◽  
Computer Interface ◽  
Dynamic Scene ◽  
Fold Cross Validation

Flickering source is an indispensable component in steady-state visual evoked potentials (SSVEPs)-based brain–computer interface (BCI), and its background severely influences the potentials evoked by the repetitive stimuli. In this paper, we investigated the problem under three different backgrounds in the context of the SSVEP-BCI-based robot car control, including black screen, static scene and dynamic scene of the environment. In the ten subjects experiment, we found significant decrease in SSVEP amplitude in dynamic scene condition compared to the reference condition black screen (p < 0.05), which resulted in classification accuracy decrease as evaluated by 10-fold cross validation. However, our proposed experiment paradigm has shown that training with static scene or dynamic scene condition could well compensate this performance drop and improve the online robot car control with real-time video feedback. The addressed problem in our application would provide some valuable suggestions when translating the SSVEP-BCI from laboratory exploration into practical usages.

scholarly journals Frequency Recognition from Temporal and Frequency Depth of the Brain-Computer Interface based on Steady-State Visual Evoked Potentials

2021 ◽  
pp. 68-73
Author(s):  
Ebru Sayilgan ◽  
Yilmaz Kemal Yuce ◽  
Yalcin Isler
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Ensemble Learning ◽  
Visual Evoked Potentials ◽  
Brain Computer Interface ◽  
System Structure ◽  
Computer Interface ◽  
Support Vector ◽  
Feature Vectors ◽  
Visual Evoked

Brain-computer interface (BCI) system based on steady-state visual evoked potentials (SSVEP) have been acceleratingly used in different application areas from entertainment to rehabilitation, like clinical neuroscience, cognitive, and use of engineering researches. Of various electroencephalography paradigms, SSVEP-based BCI systems enable apoplectic people to communicate with outside world easily, due to their simple system structure, short or no training time, high temporal resolution, high information transfer rate, and affordable by comparing to other methods. SSVEP-based BCIs use multiple visual stimuli flickering at different frequencies to generate distinct commands. In this paper, we compared the classifier performances of combinations of binary commands flickering at seven different frequencies to determine which frequency pair gives the highest performance using temporal and spectral methods. For SSVEP frequency recognition, in total 25 temporal change characteristics of the signals and 15 frequency-based feature vectors extracted from the SSVEP signal. These feature vectors were applied to the input of seven well-known machine learning algorithms (Decision Tree, Discriminant Analysis, Logistic Regression, Naive Bayes, Support Vector Machines, Nearest Neighbour, and Ensemble Learning). In conclusion, we achieved 100% accuracy in 7.5 - 10 frequency pairs among these 2,520 distinct runs and we found that the most successful classifier is the Ensemble Learning classifier. The combination of these methods leads to an appropriate detailed and comparative analysis that represents the robustness and effectiveness of classical approaches.

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