scholarly journals Eye-Movement-Control-Independent Brain Computer Interface Using Modulation of Steady-State Responses in Visual Evoked Potentials

2018 ◽  
Vol 11 (6) ◽  
pp. 438-445
Author(s):  
Seiji NISHIFUJI ◽  
Hirotaka NAKAMURA ◽  
Atsushi MATSUBARA ◽  
Akira IWATA ◽  
Hitoshi HIRANO
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Eye Movement ◽  
Visual Evoked Potentials ◽  
Brain Computer Interface ◽  
Movement Control ◽  
Computer Interface ◽  
Eye Movement Control ◽  
Steady State Responses ◽  
State Responses

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.

scholarly journals Kendali Arah pada Brain Computer Interface Berbasis Steady State Visual Evoked Potentials

2020 ◽  
Vol 10 (2) ◽  
pp. 155
Author(s):  
Jaler Sekar Maji ◽  
Catur Atmaji
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Brain Computer Interface ◽  
True Positive Rate ◽  
Computer Interface ◽  
Frequency Rate ◽  
Directional Control ◽  
Bci System ◽  
Visual Evoked

 Various studies regarding to Steady State Visual Evoked Potentials (SSVEP) based Brain Computer Interface (BCI) system with Electroencephalogram (EEG) signal has developed as BCI implementation on directional control, however lackness found on those studies which are long time on classification duration, to many electrode channels used and the electrode channels located on special area. This study we developed the SSVEP based BCI system with one second classification duration, four active channels used and electrode channels located based on The International 10-20 System. Stimulus used are red colored object with 11 Hz frequency rate represents as left directional control class, blue colored object with 13 Hz frequency rate represents as right directional control class and white colored background represents as relax class. Filter bank with eight frequency range (11 Hz, 22 Hz, 33 Hz, 13 Hz, 26 Hz, 39 Hz, 12-29 Hz dan 30-50 Hz) followed by Root Mean Square (RMS) used as  feature extraction for every second of data. Artificial Neural Network (ANN) classification and 5-Fold Cross Validation are used to knowing the performance of the developed system. Developed BCI system resulted accuracy 78,20% with True Positive Rate (TPR) 86,00% and False Discovery Rate (FDR) 23,21%.

scholarly journals Towards an independent brain–computer interface using steady state visual evoked potentials

2008 ◽  
Vol 119 (2) ◽  
pp. 399-408 ◽  
Author(s):  
Brendan Z. Allison ◽  
Dennis J. McFarland ◽  
Gerwin Schalk ◽  
Shi Dong Zheng ◽  
Melody Moore Jackson ◽  
...  
Keyword(s):  
Steady State ◽  
Evoked Potentials ◽  
Visual Evoked Potentials ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Visual Evoked
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