**3. Results and discussion**

Characterized as an increase in the amplitude of the stimulating frequency, the photic driver response results in significant baseline and harmonics [33]. Thus, it is possible to determine the stimulus frequency based on the SSVEP measurement. For this purpose, 115 feature vectors were extracted from the SSVEP signals recorded using seven different frequencies. The extracted feature vectors were run with seven basic ML algorithms. Simultaneously, the frequencies that constitute the SSVEP data set were evaluated with multiple, selected three-class, and binary classifications. Also, the effect of the increase in the difference between frequencies on the accuracy criterion was investigated, and the results are shown in detail between **Figures 2**–**17**, and **Tables 2**–**5**.

**Figure 2.** *Binary classification performance of the time-domain features.*

#### **Figure 3.**

*Percentage of classifier where the best result is the most often obtained as a result of running the algorithms 2,520 times in total.*

**Figure 4.** *Results of selected 3-class classifications for frequency-domain features.*

*Evaluating Steady-State Visually Evoked Potentials-Based Brain-Computer Interface System… DOI: http://dx.doi.org/10.5772/intechopen.98335*

#### **Figure 6.**

*Percentage of successful classifiers that give the highest accuracies from 2,520 runs in total.*

#### **Figure 8.**

*Classification performance of energy, entropy, and variance together as a feature set (all features together).*

#### **Figure 9.**

*Percentage of classifier where the best result is the most often obtained as a result of running the algorithms 2,520 times in total a) energy, entropy, and variance as separate features, b) energy, entropy, and variance as a feature set.*

*Binary classification performance of the features for bior 3.5 mother wavelet function.*

*Evaluating Steady-State Visually Evoked Potentials-Based Brain-Computer Interface System… DOI: http://dx.doi.org/10.5772/intechopen.98335*

**Figure 11.** *Binary classification performance of the features for coif 1 mother wavelet function.*

**Figure 12.** *Binary classification performance of the features for Db 4 mother wavelet function.*

**Figure 14.**

*Binary classification performance of the features for Rbio 2.8 mother wavelet function.*

**Figure 15.** *Binary classification performance of the features for Sym 4 mother wavelet function.*

**Figure 16.**

*Change of accuracy value according to the differences between frequencies for mother wavelet functions.*
