## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Feature** & **OCSVM** & **kNN** & **Algorithm** & & \\  & **kNN** & **LNND** & **LOF** & **IF** \\ \hline
3 Hz Raw & 0.305 \(\pm\) 0.057 & **0.316 \(\pm\) 0.055** & 0.281 \(\pm\) 0.055 & 0.297 \(\pm\) 0.055 & **0.304 \(\pm\) 0.057** \\ \hline
30 Hz Raw & **0.307 \(\pm\) 0.060** & 0.312 \(\pm\) 0.060 & 0.331 \(\pm\) 0.056 & 0.330 \(\pm\) 0.054 & 0.271 \(\pm\For the mean value in the 30 Hz band, there was a statistically significant difference in the F1-score depending on which algorithm was used, \(\chi^{2}(4)=56.151\), \(p<0.001\). Post hoc analysis with [PERSON]'s tests was conducted with a Bonferroni correction applied, resulting in a significance level set at \(p<0.01\). There was a significant difference in the F1-score between the LNND and other algorithms (Table 5). For the PCA in the 3 Hz band, there was no statistically significant difference in the F1-score between the algorithms, \(\chi^{2}(4)=6.616\), \(p<0.158\). For the PCA in the 30 Hz band, there was no statistically significant difference in the F1-score between the algorithms, \(\chi^{2}(4)=0.988\), \(p<0.912\). For the RAW data in the 3 Hz band, there was no statistically significant difference in the F1-score between the algorithms, \(\chi^{2}(4)=4.934\), \(p<0.212\). For the RAW data in the 30 Hz band, there was no statistically significant difference in the F1-score between the algorithms, \(\chi^{2}(4)=3.253\), \(p<0.516\). The \(F1\)-scores appeared to be low; however, there are certain reasons for this. Firstly, in unsupervised ML, we cannot compare predicted values to true values and change the parameters of the method to improve the performance. Since unsupervised methods aim to separate anomalies from the rest of the data, the main way to affect performance is to estimate the percentage of outliers in the data. Secondly, EEG data contains a lot of artefacts that can also be marked as anomalies, and since the data is not pre-labelled, we have limited options to avoid them. The presence of artefacts affects the threshold value for the method, so in highly contaminated data, the \(F1\)-score can be lower. Nonetheless, even these F1-scores can be beneficial for DSS, where the final decision is made by a human. The proposed system could be used for express analyses of the EEG to save the time and effort of experts. \begin{table}
\begin{tabular}{c r r r r r r r r} \hline \hline  & & **T-Stat** & **df** & \(\mathbf{W_{i}}\) & \(\mathbf{W_{j}}\) & **P** & \(\mathbf{P_{bonf}}\) & \(\mathbf{P_{holm}}\) \\ \hline OCSVM & kNN & 0.039 & 316 & 264.000 & 263.500 & 0.969 & 1.000 & 1.000 \\  & LNND & 7.883 & 316 & 264.000 & 163.500 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 \\  & LOF & 1.294 & 316 & 264.000 & 247.500 & 0.197 & 1.000 & 1.000 \\  & IF & 0.196 & 316 & 264.000 & 261.500 & 0.845 & 1.000 & 1.000 \\ kNN & LNND & 7.844 & 316 & 263.500 & 163.500 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 \\  & LOF & 1.255 & 316 & 263.500 & 247.500 & 0.210 & 1.000 & 1.000 \\  & IF & 0.157 & 316 & 263.500 & 261.500 & 0.875 & 1.000 & 1.000 \\ LNND & LOF & 6.589 & 316 & 163.500 & 247.500 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 \\  & IF & 7.687 & 316 & 163.500 & 261.500 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 \\ LOF & IF & 1.098 & 316 & 247.500 & 261.500 & 0.273 & 1.000 & 1.000 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: 3 Hz mean ([PERSON]’s post hoc comparisons). \begin{table}
\begin{tabular}{c r r r r r r r} \hline \hline  & & **T-Stat** & **df** & \(\mathbf{W_{i}}\) & \(\mathbf{W_{j}}\) & **P** & \(\mathbf{P_{bonf}}\) & \(\mathbf{P_{holm}}\) \\ \hline OCSVM & kNN & 1.013 & 316 & 244.000 & 254.000 & 0.312 & 1.000 & 1.000 \\  & LNND & 5.115 & 316 & 244.000 & 193.500 & \(<\)0.001 & \(<\)0.001 & \(<\)0.001 \\  & LOF & 0.861 & 316 & 244.000 & 252.500 & 0.390 & 1.000 & 1.000 \\  & IF & 1.215 & 316 & 244.000 &  
In the next step, we considered the precision-recall curves for all algorithms trained using the optimal hyperparameters from Table 2 on the different types of input data (Figure 3).