**3.1 Time-domain features results**

The multiple and binary classification results of 25 feature vectors extracted from SSVEP signals using time-domain properties are given below, respectively.

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

#### **Figure 17.**

*Percentage of classifier where the best result is the most often obtained as a result of running the algorithms 2,520 times in total (for Haar wavelet function).*


#### **Table 2.**

*Results of multiple classification for time-domain features.*


#### **Table 3.**

*Results of multiple classification for frequency-domain features.*


#### **Table 4.**

*Multiple classification results of wavelet features.*


#### **Table 5.**

*Classification results of the most successful frequency pairs of the Haar mother wavelet.*

### *3.1.1 Multiple classification results*

Presented in **Table 2** are accuracy results for multiple classification. In regard to these results, the highest performance was shown by the Ensemble Learning classifier with 52.40%.

### *3.1.2 Binary classification results*

According to the binary classification results shown in **Figure 2**, the best performance was obtained with an accuracy value of 91.68% in 6–10 Hz frequency pairs based on the average of the subjects. Simultaneously, when the subjects are considered separately, a classification performance up to 100% were obtained. In addition, there is no definitive finding related to the increase in the accuracy value parallel to the difference between frequencies for the time-domain.

The results of classifiers to be expressed in the pie chart in **Figure 3** are the number of hits of the classifiers obtained. These numbers were obtained by running all algorithms 2,520 times in total. The best classification performance is shown by the Ensemble learning classifier.

#### **3.2 Frequency-domain features results**

For the frequency-domain characteristics used in the problem of determining seven different frequencies, firstly, spectrum analysis was performed to detect the stimulus frequencies more clearly than the signal. This analysis is often used to obtain frequency information in evoked SSVEP responses. The power spectrum of SSVEP

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

signals was determined by FFT using MATLAB software to calculate its power, entropy, and variance for each band in the frequency range corresponding to the frequencies. For this purpose, the signal received FFT is divided into EEG bands (delta, theta, alpha, beta, gamma), and energy, entropy, and variance values of each band are calculated. A total of 15 feature vectors are generated.

#### *3.2.1 Multiple classification results*

According to the multiple classification results of the seven frequencies presented in **Table 3**, it was determined that the best performance was in the Ensemble Learning classifier with an accuracy value of 57.10%. Another remarkable finding here is that the results of the classifier from all individuals are the same. This shows us that, like the time-domain, the Ensemble Learning classifier performs better than others. In addition, when multiple classification results of frequency-domain features are compared with multiple classification results of time-domain features, it has been determined that there is an increase of 4.70% on an individual basis and 3.18% on average.

#### *3.2.2 Selected three class classification results*

In this part, three frequencies (6 Hz - 8.2 Hz - 10 Hz), which are considered to increase the classification performance, were chosen among the seven frequencies present in the data set, during the feature extraction phase. The reason for choosing these frequencies are the results of the study done in Ref. [12, 13, 20].

According to the results obtained (**Figure 4**), the highest classification performance for the first participant was 83.30% in the Ensemble Learning classifier, the highest 100% classification performance for the second participant was in the KNN and SVM classifiers, and 88.90% for the third participant in the KNN classifier. Finally, in the fourth participant, it was seen again in the Ensemble Learning classifier with 77.80%.

When the results are evaluated considering the classifiers, the performance of the six different classifiers was calculated by taking the average of the four participants and the highest performance was found in the Ensemble Learning classifier with an accuracy of 79.73%.

#### *3.2.3 Binary classification results*

Considering the averages of the binary classification results of frequency features, the performances obtained vary between the lowest 70.85% and the highest 100%. Accordingly, the highest performance was determined with 100% accuracy value in 7.5–10 frequency pairs.

When the results are evaluated in terms of classifiers, it is clearly seen in **Figure 6** that the classifier with the highest accuracy rate is the Ensemble Learning classifier. Runner-up classifier is the SVM classifier. Other classifiers following Ensemble Learning and SVM were identified as KNN, Logistic Regression and Naive Bayes classifiers, in order. It is also seen that no successful results have been obtained in the LDA and Decision Tree classifiers.

#### **3.3 Wavelet transform features results**

This section aims to analyze three crucial features, such as energy, variance, and entropy, which are frequently used in DWT studies, have been extracted from the

bands (delta, theta, alpha, beta, and gamma) of the EEG signal. These features were generated for six different mother wavelets (Haar, db4, sym4, coif1, bior3.5, rbio2.8) commonly used in the literature. The results of each were evaluated in detail for multiple, binary, and three selected frequencies.

## *3.3.1 Multiple classification results*

On the basis of mother wavelet selection, the results in (**Table 4**), reveal that Bior3.5 and Coif1 mother wavelets were relatively successful, although there is no dominant wavelet type. Experimenting with a larger sample size (number of subjects), in order to generalize, can help obtain more precise results.

In contrast to the mother wavelet selection, when the classifiers are evaluated, the success of Ensemble learning and LDA classifiers is clearly seen.

### *3.3.2 Classification results for three selected frequencies*

In this analysis, as in the classification of frequency-domain features (Section 3.2.2), multiple classification was made by selecting 3 selected frequencies (6 Hz - 8.2 Hz – 10 Hz) where the differences between the frequencies were higher among the seven frequencies. However, unlike the analysis made in the frequency-domain, the selected features are classified and evaluated both they are used together, that is, when energy, variance and entropy features are used as a single feature vector (all features together, and they are used as separate features. Thus, detailed information about the power, irregularity and bias of the signal was obtained. At the same time, it is learned how to use these three features, which have the indispensable properties of the signal, more effectively. And the contribution of these features, which are frequently used in the literature, as a new form of features is wanted to be shown.

In **Figure 7**, the ACC values obtained by classification of the energy, entropy, and variance features extracted using each wavelet family are presented. Mean, minimum and maximum values of the classification results were also shown. According to these results, the values given by the Haar wavelet function for energy, entropy, and variance feature groups, which yield more successful results than other wavelet functions, were 75.85%, 73.08%, and 73.75%, respectively. There were no major differences between the mean values of the features extracted based on the Haar wavelet. However, it was seen that the entropy feature group had a 100% success rate compared to the others.

In **Figure 8**, the extracted features based on wavelet were used as a feature set, and the successful performances of the wavelet families were compared in this way. It was seen that the most successful wavelet family was the Haar wavelet function. The ranking of success in other wavelet families has not changed. The accuracy values are as follows: 75.85% with Haar mother wavelet, 67.53% with bior3.5 mother wavelet, 60.85% with db4 mother wavelet, 56.25% with coif1 mother wavelet, 52.35% with rbio2.8 mother wavelet and 44.73% with sym4 mother wavelet obtained. It was seen that some mother wavelet performances increased when compared with the ACC values in which the features in **Figure 7** were handled separately. Mean values of coif1, db4, and sym4 mother wavelet functions increased.

As a result of the classification processes performed separately for each subject, when the performances of both feature groups were examined, the most successful wavelet function was found as the Haar wavelet. When the average accuracy values of the feature groups are examined, the results in the case that the three features are used *Evaluating Steady-State Visually Evoked Potentials-Based Brain-Computer Interface System… DOI: http://dx.doi.org/10.5772/intechopen.98335*

as a single feature vector gave higher results for all wavelet functions than the other feature group. Although there is no dominant result in the comparison of energy, entropy, and variance features among themselves, the highest result was seen in the entropy feature in Subject 3 with 100%.

The results of classifiers to be expressed in the pie chart in **Figure 9** are the number of hits of the classifiers obtained. With reference to results obtained, it is obvious that the most successful and also the most frequent classifier in the classification was obtained as the Ensemble classifier.

#### *3.3.3 Binary classification results*

In this analysis, feature vectors are treated as a single feature vector and individual (separate) feature vectors, similar to those in Section 3.2.3. The resulting feature vectors were then evaluated by binary classification in order to analyze frequencies in detail. As the results of the experimental design, the classification performances are obtained for:


Each feature (energy, entropy, variance and all features together) extracted using each wavelet family. All values of the classification results are presented in **Figures 10**–**15** for each mother wavelet, respectively.

According to these results, features obtained from the Haar wavelet function yielded higher accuracies than those obtained from the other wavelet functions. Maximum accuracy performances were obtained in the frequency pairs "6–10", "6.5–8.2", "6.5–10" in the Haar wavelet (**Table 5**). When the features are evaluated, it is realized that the "All features together" feature generally has better results for all mother wavelet functions.

And another researched hypothesis results are presented in **Figure 16** for each mother wavelet, respectively. The purpose here is to show the change in the accuracy value according to the increase in the difference between the frequencies.

Finally, classification results obtained are presented in **Figure 17**. Since the classification results of all the features ranking are similar for all the wavelet functions, the classification result of the "All features together" for Haar wavelet function is presented. According to these results, the most successful classifier was obtained as the Ensemble classifier.
