## 2 Materials and Methods
### Proposed Framework  
After the preprocessing block: notch filter is for electrical grid interference elimination, low-pass and high-pass filters are for cut-off frequency from 4-40 Hz; peak removal for each trial has voltage exceeding 100 \(\upmu\)V, the preprocessed data is then split into 3 pairs, namely: Left hand vs. right hand (pair 1), Right hand vs. feet (pair 2) and feet vs. left hand (pair 3), for binary classification. Train and test data are created based on 10-fold cross-validation (Figure 3). In the training phase, a filter bank is applied to each EEG trial with a frequency range of 4-40 Hz, a bandwidth of 4 Hz, and 2 Hz overlapping; thus creating 17 filter banks in total. The proposed frequency configuration is based on study [18; 23; 64]. Next, the Fisher's ratio and mRmR algorithms are applied to data in each filter bank to select the most discriminative filter banks. By calculating the power of data samples, Fisher's ratio of each channel can be calculated through the criterion \"maximize between-class variance and minimize within-class variance\" and sorted in descending order. In this study, the most discriminative filter bank in each channel that has the highest Fisher's ratio is selected for further selection. The maximum filter banks we can have in this case are 6 as dataset 1 has 6 channels. For dataset 2, the maximum filter banks would be 17 filter banks as it has a number of channels exceeding the number of filter banks. Meanwhile, prior to using the mRmR feature selection algorithm, features of data of each filter bank have to be extracted by CSP. mRmR works on the two independent variables, which are \"features\" and \"labels\" in this study. mRmR finds the relationship, or how much information, the two independent variables have in common. As a result, the features in dataset 1, which are 6 channels multiplied by 17 filter banks, are sorted in descending order based on the amount of mutual information the algorithm found. For dataset 2, the number of features would be 22 channels multiplied by 17 filter banks. The top 6 filter banks are selected for further selection, regardless of which channel they are generated from. The maximum number of filter banks we can have in this case is 6 to match with [PERSON]'s case. The selection criteria of this study are as follows: we take the intersection of the two cases, which means the common filter banks are selected as the final selection. If the intersection is not possible, the filter banks selected in the mRmR case will be chosen as it is claimed to be more effective than [PERSON]'s [40; 65; 66]. In the testing phase, the test data goes through the filter bank which was previously chosen in the training phase and the features are extracted with the corresponding \(W_{\text{CSP}}\). In the last step, LDA is utilized to perform binary classification. To visualize how informative features are extracted and selected by the proposed method, ERD/ERS responses, discriminative frequency bands, and features dimensions of a single measurement of a random subject of the self-acquired dataset are plotted in Figure 4. Figure 4A-C shows the responses in the time-frequency map of a random subject when performing left hand, right hand, and feet imagery respectively. Only C3, Cz, and C4 were plotted to represent the sides of the cortex. The imagination of limb movement causes ERD in mu and beta waves on the contralateral hemisphere [8; 9; 10; 11; 12]. The more dominant mu (10-14 Hz) and beta (22-26 Hz) suppressions of left-hand imagery can be seen on channel C4, while those of right-hand imagery are on channel C3. As feet imagery involves imagination of moving both feet and its somatosensory area is close to the mid-central of the cortex, the feet imagery causes mu suppression in C3, Cz, and C4. These features (frequency bands and channels) are informative for discriminating the tasks from one another. The proposed method successfully selected the most informative features (Figure 4D) for left hand vs. right hand.