## 6 Conclusions and Future Work  
In this project, a preliminary study was conducted using time and frequency features with the KNN classifier. By using features extracted from band-pass-filtered segments of data, the KNN classifier achieved a prediction accuracy of 57.23% for the FW-RV pair and 50.87% for the GP-RL pair. A potential solution to improve this low accuracy was suggested:using CSP-filtered features as inputs for the classifier. However, the results did not improve, as the primary aim of the preliminary study was to understand the overall process of neural signal processing and classification. A baseline system was then developed using the CSP-CWT approach, with GoogLeNet as the classifier. For intrasubject classification of the FW-GP pair, out of 11 participants, nine achieved an accuracy of 90-100%, one achieved 82%, and another achieved 52%. For intersubject classification across six unique pairs (FW-GP, FW-RL, FW-RV, GP-RL, GP-RV, and RV-RL), the accuracy ranged from 80.87% to 99.73%, with F1 scores ranging from 0.8104 to 0.9973. This baseline system demonstrated good performance in classifying the four hand movements, comparable to the system in [17]. Using the baseline system, three variants were proposed for four-way classification. Among these, the ML-CSP-OVR variant achieved the best accuracy: 78.08% for intersubject classification with combined data, and 90.5% for participant 12 in intrasubject classification. Additionally, the leave-one-out intersubject classification achieved an accuracy of 76.39%, indicating the potential of the proposed system to serve as a generalized, subject-independent solution. Future improvements to the ML-CSP-OVR system could involve integrating the FBCSP-OVR system to enhance performance, as multiple filters can improve EEG signal quality. While the GoogLeNet classifier performs well, its deep-layer structure incurs significant computational costs. To address this, a simplified CNN (SCNN) [59] could be used to reduce computational cost while maintaining good classification accuracy. Additionally, decision trees, particularly random forests, should be explored, as they have shown good performance in MI EEG analysis [19] and in the fields of biomedical devices [60] and IoT technology [61; 62]. Furthermore, other areas of unsupervised domain framework could also be explored [59; 63]. Honestly, due to limitations in our resources, our proposed work involves twelve participants performing grasp and lift actions across a total of 3936 trials distributed over 10 series. Each participant conducted 328 trials. I agree that the sample size or dataset should be increased to enhance the trustworthiness of our proposed work in the future.  
Conceptualization, Methodology, Investigation, Supervision, Resources and Software, C.L.K.; Methodology, Investigation and Data Curation, C.K.H.; Project administration, Resources, Visualization and Formal analysis, T.H.A.; Methodology, Investigation, Visualization and Formal Analysis, Y.Y.K.; Investigation, Supervision, Data Curation and Funding Acquisition, T.H.T. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Data Availability Statement: The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors. The authors declare no conflicts of interest.