Abstract
In this study, Steady-State Visual Evoked Potential (SSVEP)-based Brain-Computer Interface (BCI) system, which is popular in many sectors (game, defense, sports, etc.), especially in medicine, was composed. In addition, a robot hand was designed to be integrated into the BCI system, especially to help partially or completely disabled individuals. For this purpose, feature extraction was performed using discrete wavelet transform (Db6) from SSVEP signals recorded from seven different frequencies (6, 6.5, 7, 7.5, 8.2, 9.3, 10 Hz) and four different individuals. Extracted features were classified by support vector machine (SVM) and k-nearest neighbor (k-NN) algorithms. According to the classification results, the highest performance was obtained in the SVM algorithm with an accuracy of 84%.
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