## 4 Results  
As mentioned above, we applied four different feature extraction approaches with two EEG segmentations (1 s and 2 s) to examine the effect of the segmentation length in distinguishing abnormal conditions. For applying DWT, we employed a pre-defined wavelet decomposition level of six (\(l=6\)) to extract wavelet features. Then, statistically significant features were determined by applying ANOVA with the \(p\)-value (\(<\)0.05). Varying numbers of significant features were determined in each channel. By analyzing the EEG frequency band features, we found three to nine and four to nine significant features from the EC- and EO-data, respectively. In detail, we found nine significant features with the 1 s EEG segmentation in the O2 channel. But, with the 2 s EEG segmentation, only three significant features were determined. For the FC3 channel, we found three and nine significant features with the 1 s and 2 s EEG segmentations, respectively. With the 2 s EEG segmentation, nine significant features were identified in the channels (i.e., CP4, Cz, F3, Fc3, Fz, O1, Oz, P3, P4, CP4, and T7), seven features in the channels (i.e., C3, C4, CP3, CPz, F4, F7, and Fp1), six features in the channels (i.e., FCz, and T8), and four features in the FC4. We also found the same number of features from the channels with the 1 s and 2 s segmentations: nine features--CP4, F3, and P7; eight features--F8; and six features--T8. We determined nine features from the channels (Cpz, Cz, and Fp2) in both the 1 s and 2 s segmentations. Among the various features, we found that the measured standard deviation was commonly identified as a significant feature. The measured average value was also identified as a significant feature in some channels. These features might be critical for analyzing the conditions precisely because they hold unique characteristics for differentiating them. The extracted features from \(\beta\) and \(\gamma\) bands in all channels of the EC- and EO-data were also recognized as significant features.  
By evaluating the PSD features from the EO-data, we identified that all features extracted from 60.6% of the channels were determined as significant features. But for the features from the EC-data, only 27.3% and 51.1% were determined to produce all significant features with the 1 s segmentation and the 2 s segmentation accordingly. We also found that sample entropy (SmpE) and permutation entropy (PE) were identified as significant features from all channels in the EC-data. Three raw features (kurtosis, variance, and entropy) were selected as significant features from 90% of the channels with the 1 s segmentation and 75.7% with the 2 s segmentation. The two features (kurtosis and variance) were determined as significant features in most EC- and EO-data channels. But the feature (z-score) was not identified as a significant feature in any of the channels. The feature (kurtosis) was determined as a significant feature in the two channels (C3 and Oz) with the 1 s segmentation and in the four channels (C3, F3, FCz3, and Oz) with the 2 s segmentation with the EO-data. For the EC-data, only the P7 channel presented significant features with the 1 s segmentation. When evaluating the wavelet features, we determined the features from the coefficient levels (3, 4, and 5) corresponding to 4 Hz\(\sim\)32 Hz to be significant features in the 1 s and 2 s segmentations. In detail, the features (\(\omega_{1}^{3},\omega_{2}^{3},\omega_{5}^{3},\omega_{6}^{3}\)) corresponding to 16 Hz\(\sim\)32 Hz were selected as significant features in level three detail coefficients. Only the features (\(\omega_{2}^{1},\omega_{3}^{1},\omega_{4}^{1}\)) were identified as significant features in level one detail coefficients. The feature (\(\omega_{2}^{2}\)) was also identified as a significant feature in the EC- and EO-data with the 2 s segmentation.