## 3 Methodology
### Data Checking and Cleaning  
Firstly, the dataset was reviewed to verify the information provided in the paper. According to the dataset description, the 12 participants performed a total of 10 series, comprising 28 grasp and lift (GAL) trials for mixed series and 34 trials for each of the other series types. These series include weight, surface, and mixed types. In the weight series, participants interacted with objects of varying weights across three different levels. Meanwhile, the surface series involved objects with different materials on their surfaces. The mixed series combined elements from both the weight and surface series. Figure 3 illustrates these series types for participant 1. \begin{table}
\begin{tabular}{c c} \hline \hline \# & \# Pairs of Classes \\ \hline
4 & 4 GP-RL \\ \hline
5 & 5 GP-RV \\ \hline
6 & 6 RV-RL \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 3: Types of series for participant 1. Figure 2: Selected EEG channels with 10–20 international standards (**left**). A trial showing a participant lifting the object (**right**) [51]. In the selected windowed dataset, the 10 th series, which pertains to weight variations, is exclusively available in the holistic dataset. Therefore, for the windowed dataset, only nine series are accessible. Given that each participant is limited to two mixed series, the total number of trials can be calculated as follows:  
12 participants\(\times\) 8 series\(\times\) 34 trials per series\(+\) 12 participants\(\times\) 2 mixed series\(\times\) 28 trials per series \(=\) 3936 trials  
The results align with those reported in the paper. To standardize the data across all series, trials exceeding 28 were discarded, reducing variability and facilitating data processing. Subsequently, each trial's window, starting 2 s before the LED activates and ending 3 s after deactivation [51], was scrutinized. Plots are generated for all trials to allow for manual inspection if necessary. A simple code (Listing 1) was employed to verify the 3 s duration; checking the 2 s duration was deemed unnecessary since the LED On duration in the time dataset consistently provided this information. The code was designed to print participant, series, and trial details if deviations from the specified 3 s duration were identified in the trial data, as outlined in the paper. The code generates several trials all related to participant 7. Therefore, participant 7 is discarded. Therefore, 11 participants, nine series, and 28 trials are used in this study. **Listing 1. ** A simple check for 3 s duration. if (round(t(end)) - LED_off_t) = 3  
disp(['P. '; num2 str(p), ', S.'; num2 str(s), ', T.'; num2 str(trild)])  
end  
The vertical dashed lines in Figure 4 are drawn to make sense of the time information given in P.AllLifts data (Figure 5). LED On and LED Off times plotted on the graph are calculated by these formulae:  
* LED On time = trial_start_time + LEDon, and LED Off time = trial_start_time + LEDoff, respectively. The LEDon and LEDoff used in the calculation are from ws.win. The trial_start_time is also given in ws.win data. * x Handstart, FirstDigit, BothLoad, Lift, Replace, BothReleased, and HandStop are all calculated in the same way, i.e., summing up the trial_start_time and the respective given time for the actions given in P.AllLifts. Figure 4: A trial of participant 1 showing 2 s duration before the LED on and 3 s duration after the LED Off. In Figure 6, the blue signal represents the raw EEG signal, while the red signal represents the filtered EEG signal. Four 1 s patches or segments, potentially containing the events of interest, are highlighted. These segments are extracted from the filtered signal for feature extraction, with the filtering process explained in the manuscript. The extracted data is then verified to ensure each segment is exactly 1 s or 500 samples in length. Given that the imaging period should not exceed 0.5 s [24], a 1 s segment duration is chosen to provide sufficient coverage for the event of interest.