## 4 Conclusions  
In this work, we proposed a novel 1 DCNN-BiRAU model based on random segment recombination strategy, and the results demonstrate its effectiveness in recognizing human motion images. Furthermore, given the inter-subject variability, it is imperative to explore approaches to enhance the accuracy and robustness of our model across subjects.  
While promising results were obtained with a limited sample of 10 individuals, further testing using a larger and more diverse participant pool is needed. We also conducted four-category classification experiments on groups of 10, 20, and 40 subjects, with our model achieving accuracies of 85.15%, 83.49%, and 79.97%, respectively. Expanding the participant sample size will accentuate individual differences but also augment model training and robustness. In future work, we will thoroughly investigate inter-subject variability, optimize the model architectures, and apply our proposed model to real-world applications.  
Figure 5: The performance at group level. (**a**) shows the training and test accuracy of binary classification, and (**b**) shows the training and test accuracy of four-category classification.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Work**} & **Binary Classification** & **Four-Category Classification** & \multirow{2}{*}{**Method**} \\  & **Mean. acc.** & & **Mean. acc.** & \\ \hline [PERSON] et al. & \multirow{2}{*}{86.49\%} & \multirow{2}{*}{68.51\%} & \multirow{2}{*}{CNNs} \\ (2018) [5] & & & \\ [PERSON] et al. & \multirow{2}{*}{81.10\%} & \multirow{2}{*}{79.78\%} & \multirow{2}{*}{DSRNN} \\ (2019) [13] & & & \\ [PERSON] et al. & \multirow{2}{*}{90.08\%} & \multirow{2}{*}{N/A} & \multirow{2}{*}{TS-CNN} \\ (2020) [22] & & & \\ [PERSON] et al. & \multirow{2}{*}{N/A} & \multirow{2}{*}{90.00\%} & \multirow{2}{*}{ResNet50-LSTM} \\ (2022) [23] & & & \\ Ours & **99.47\%** & **90.90\%** & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison with existing studies (individual level).  
**Author Contributions:** Conceptualization, H.H. and C.Y.; methodology, H.H.; software, E.S.; validation, C.Y., H.H. and E.S.; formal analysis, C.Y.; investigation, S.Y.; resources, J.W. ([PERSON]); data curation, Y.K.; writing--original draft preparation, H.H.; writing--review and editing, C.Y.; visualization, J.W. ([PERSON]); supervision, S.Z.; project administration, S.Z.; funding acquisition, S.Z.; All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the High-Level Researcher Start-Up Projects of Northwestern Polytechnical University (Grant No. 06100-23 SH0201228) and the Basic Research Projects of Characteristic Disciplines of Northwestern Polytechnical University (Grant No. G2023 WD0146).  
**Institutional Review Board Statement:** Not applicable. This study utilized a publicly available EEG dataset which does not require ethical approval. All data used do not contain personally identifiable information or offensive content.  
**Informed Consent Statement:** Not applicable. This study utilized an open-source EEG dataset that is publicly available, and did not involve direct participation of human subjects.  
**Data Availability Statement:** The data used in this study were obtained from a publicly available dataset, which can be found at the following link: [[https://archive.physionet.org/pn4/eegmmidb/](https://archive.physionet.org/pn4/eegmmidb/)]([https://archive.physionet.org/pn4/eegmmidb/](https://archive.physionet.org/pn4/eegmmidb/)), accessed on 9 September 2009.  
**Conflicts of Interest:** The authors declare no conflict of interest.