## 3 Results  
To perform a comparative analysis, baseline accuracy was obtained with the traditional combination of common spatial pattern (CSP) and a linear classifier [20] (in our case, logistic regression). In addition to the described adjustment of the logistic regression regularization coefficient, the optimal number of CSP filters in the range from 1 to 9 was also selected during cross-validation. In this approach, the resulting accuracy was 51.1 \(\pm\) 10.8%, that is, even such an efficient and widely used model resulted in quite poor classification. Next, the algorithm of discriminative frequencies selection, including mu, beta-2, gamma-1, and gamma-2 ranges in the frontal, central, and parietal brain areas was tested. The best linear combination of the bands considered was determined for each MI class in order to provide the most accurate discrimination between its signal epochs and the background signal epochs. However, it also improved the discrimination between three MI classes themselves: after applying such a transformation, the accuracy increased to 65.4 \(\pm\) 9.1%. Furthermore, this transformation allows one to reduce the dimensionality of the feature vector by more than 50 times, resulting in higher computational efficiency and compact feature space representation [36]. Additionally, Hjorth parameters [37] and pairwise interchannel correlation coefficients were used to form feature vectors. When Hjorth parameters were used, the accuracy of classification was 53.9 \(\pm\) 11.4%; use of correlation coefficients led to the classification accuracy of 53.7 \(\pm\) 8.2% (Table 1). Further refinement was the addition of the search for the most informatory time intervals within 2 s in each mental movement act. Since the onset and duration of MI execution were quite voluntary, it is reasonable to find the informatory time interval for each MI act. Three types of features were used to test this approach: power spectral densities, Hjorth parameters, and coefficients of pairwise interchannel correlation. The resulting classification accuracies are listed in Table 2.  
The decrease in accuracy according to the PSD features obtained for shorter windows was probably due to the use of fewer data points rather than all 2 s, which leads to poorer spectra. However, it can be argued that the sequential determination of discriminative frequency ranges and informatory time segments, although using different features, leads to an improvement in the classification results. Indeed, according to Table 2, the highest accuracy among all subjects was obtained using 750 ms windows and coefficients of pairwise interchannel correlation as features. It is also clearly shown in Tables 2 and 3 that using shorter windows is advantageous compared to using the entire 2 s epochs if coefficients of pairwise interchannel correlation or Hjorth parameters are used as features. The ROC curves and AUC scores calculated for the transformed sample of all subjects after the completion of the search for discriminative time segments do not allow us to clearly see which window length is the most advantageous. However, according to the best features (coefficients of pairwise interchannel correlation and Hjorth parameters) and segments of lengths of up to 1000 ms (Figure 6A,B,D,E,G,H) providing the highest intersubject accuracies, a window of 750 ms seems to be somewhat optimal. Indeed, for the former features, 500 ms is somewhat better than comparable 750 \begin{table}
\begin{tabular}{c c c} \hline \hline
**Approach** & **Feature** & **Classification Accuracy** \\ \hline CSP + LR & CSP patterns & \(51.1\pm 10.8\%\) \\ \hline \multirow{3}{*}{Search for informative frequencies} & PSD & \(65.4\pm 9.1\%\) \\ \cline{2-3}  & Hjorth parameters & \(53.9\pm 11.4\%\) \\ \cline{2-3}  & Correlation coefficients & \(53.7\pm 8.2\%\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Motor imagery classification accuracies in the approach for the detection of the most informative frequencies.