## 3 Results
### Accuracy and F1-Score of Self-Acquired Dataset
The most obvious recognition is that FBCSP in each measurement (or each day) is outweighed by other algorithms, approximately 20-30%. The DFBCSP frameworks still perform well compared to each other. However, all algorithms, including FBCSP, have almost equal efficiency when all measurements of one subject are computed. This can be inferred that FBCSP works better when the number of data is large enough, while DFBCSP algorithms only require a lower amount to be sufficient. Consequently, BCI utilizing DFBCSP would require less data and \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Subject**} & \multicolumn{6}{c}{**F-LH**} \\ \cline{2-9}  & \multicolumn{2}{c}{**FBCSP**} & \multicolumn{2}{c}{**DFBCSP—Fisher**} & \multicolumn{2}{c}{**DFBCSP—MRmR**} & \multicolumn{2}{c}{**DFBCSP—FnnR**} \\ \cline{2-9}  & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\ \hline
01 & 86.94 & 0.87 & 86.51 & 0.87 & 86.51 & 0.86 & 86.3 & 0.86 \\
02 & 89.61 & 0.9 & 89.21 & 0.89 & 88.19 & 0.88 & 87.58 & 0.88 \\
03 & 54.29 & 0.54 & 55.68 & 0.55 & 53.46 & 0.53 & 53.19 & 0.51 \\
04 & 77.62 & 0.77 & 79.71 & 0.79 & 79.08 & 0.79 & 78.45 & 0.78 \\
05 & 75.62 & 0.73 & 75.62 & 0.72 & 76.3 & 0.72 & 76.52 & 0.73 \\
06 & 86.79 & 0.86 & 87.31 & 0.87 & 87.31 & 0.87 & 88.6 & 0.88 \\
07 & 63.82 & 0.64 & 61.29 & 0.63 & 62.44 & 0.64 & 63.82 & 0.65 \\
08 & 93.53 & 0.93 & 92.9 & 0.93 & 93.11 & 0.93 & 91.65 & 0.92 \\
09 & 49.58 & 0.49 & 46.85 & 0.48 & 46.64 & 0.48 & 47.27 & 0.48 \\
10 & 63.71 & 0.64 & 59.85 & 0.6 & 67.95 & 0.69 & 54.44 & 0.54 \\
11 & 94.18 & 0.94 & 94.98 & 0.95 & 93.78 & 0.94 & 91.97 & 0.92 \\
12 & 72.32 & 0.73 & 74.17 & 0.74 & 75.65 & 0.77 & 69 & 0.7 \\
13 & 98.92 & 0.99 & 99.35 & 0.99 & 99.57 & 1 & 98.28 & 0.98 \\
14 & 84.71 & 0.85 & 81.53 & 0.82 & 82.38 & 0.82 & 72.82 & 0.73 \\
15 & 65.1 & 0.65 & 63.53 & 0.65 & 66.22 & 0.68 & 60.85 & 0.63 \\
16 & 84.8 & 0.85 & 80.08 & 0.8 & 82.55 & 0.83 & 71.66 & 0.72 \\ Mean \(\pm\) SD & 77.6 \(\pm\) 14.83 & 0.77 \(\pm\) 0.15 & 76.79 \(\pm\) 15.39 & 0.77 \(\pm\) 0.15 & 77.57 \(\pm\) 14.88 & 0.78 \(\pm\) 0.15 & 74.53 \(\pm\) 15.56 & 0.74 \(\pm\) 0.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy and F1-score of four methods on the author’s dataset for “Feet vs. Left hand” pair. Figure 8: Accuracy calculated for each day of a random subject from the author’s dataset. Four methods are in different colors. time for both training and testing. This also implies that DFBCSP algorithms are more suitable for a robust and practical BCI system. To further reassure our hypothesis, the difference in the accuracy of \"all measurement\" and \"single measurement\" of every subject is shown in Figure 9. \"All\" is defined as data of all recordings taken into account, and \"single\" is defined as data of each day taken into account and then accuracy is averaged across each measurement. A positive value indicates the accuracy of \"all measurement\" is better than that of \"single measurement\", and vice versa. Most subjects have an accuracy difference between \"all measurement\" and \"single measurement\" to be positive. This indicates that a larger data size improves classification performance. The differences in all three DFBCSP frameworks are mainly below 8%, which is not remarkable as there were variations in performance among measurements of each subject. However, there is a great fall of the FBCSP framework in three pairs in most subjects when the evaluation is performed in each measurement (smaller data size). The difference ranges from 10-30%, which means the performance of FBCSP in \"single measurement\" is significantly lower than in \"all measurement\" (\(p=0.0029\), \(p=0.0015\) and \(p=0.0008\) for pair 1, pair 2 and pair 3 accordingly). The mean values of the difference of three DFBCSP frameworks are no more than 3% for three pairs, while that of FBCSP is 12.30%, 14.22%, and 15.37% for pair 1, pair 2, and pair 3, respectively (Table 5).