scholarly journals Assessment of high-frequency steady-state visual evoked potentials from below-the-hairline areas for a brain-computer interface based on Depth-of-Field

2020 ◽  
Vol 184 ◽  
pp. 105271
Author(s):  
Alan Floriano ◽  
Denis Delisle-Rodriguez ◽  
Pablo F. Diez ◽  
Teodiano Freire Bastos-Filho
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
High Frequency ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Depth Of Field ◽  
Visual Evoked

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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