## 6 Conclusions  
This study demonstrates the potential of integrating DL methods and TE analysis for the diagnosis and continuous management of schizophrenia using EEG data. With the help of TE matrices, valuable insights into the directional flow of information between key brain regions can be highlighted, emphasizing significant differences in neural connectivity between schizophrenia patients and HCs, which may be of great importance in understanding the disrupted brain network dynamics in schizophrenia, particularly in the frontal and central regions. The proposed hybrid CNN-BiLSTM with TE matrices as inputs and the high accuracy of 99.94% of the classification placed this study parallel to some of the most superior methods as reported in the literature. The model's ability to capture both spatial and temporal dependencies in EEG data provides a robust framework for detecting schizophrenia-related abnormalities, marking a significant improvement over traditional diagnostic methods. Furthermore, the results of the TE analysis, particularly in relation to the brain regions targeted by the Muse 2 headband, underscore the potential for real-time, non-invasive monitoring of mental health conditions. By integrating these findings into the NeuroPredict platform, a comprehensive RPM system, we envision a more proactive approach to schizophrenia care. Continuous monitoring of EEG signals, combined with advanced AI algorithms, will enable early detection of cognitive decline or relapse, facilitating timely interventions and improving patient outcomes. While this study shows promising results, there are several limitations that need to be addressed. One of the main challenges lies in the relatively small size of the dataset used, which may limit the generalizability of the model to larger and more diverse populations. The TE analysis and CNN-BiLSTM model were also trained on preprocessed EEG data, which may not capture all the nuances of raw EEG signals in real-world settings. Furthermore, the model's performance, although impressive, needs further validation with longitudinal data and across different stages of schizophrenia to assess its ability to detect early cognitive decline or relapse consistently. Integrating the TE matrices into real-time RPM systems, like NeuroPredict, poses technical challenges in terms of computational resources and latency, which must be carefully managed to ensure timely and accurate feedback for both clinicians and patients. In conclusion, this paper contributes to the growing body of research on AI-driven approaches to mental health diagnostics, particularly in applying EEG data for schizophrenia detection. The integration of DL models and TE analysis within real-time monitoring platforms, like NeuroPredict, represents a significant step toward more personalized and effective care for patients with schizophrenia, offering enhanced diagnostic accuracy, con tinuous monitoring, and improved quality of life. Future work will focus on further optimizing these models, expanding their applicability to other neuropsychiatric conditions, and exploring real-world deployment in clinical settings. Conceptualization E.-A.P. and L.B. ; methodology, E.-A.P., L.B., C.P. and O.B. ; software, E.-A.P. ; validation, E.-A.P. and C.P. ; formal analysis, E.-A.P. ; investigation, E.-A.P., L.B., C.P. and O.B. ; writing--original draft preparation, E.-A.P., L.B., C.P., O.B. and D.-N.N. ; writing--review and editing, E.-A.P., L.B., C.P., O.B. and D.-N.N. ; visualization, E.-A.P. All authors have read and agreed to the published version of the manuscript. The authors gratefully acknowledge the contribution of the Romanian Ministry of Research, Innovation, and Digitization for the development of the NeuroPredict Platform inside the project \"Advanced Artificial Intelligence Techniques in Science and Applications\" (contract No. 13N/2023 (PN 23 38 05 01)) for the period 2023-2026. Data Availability StatementData available in a publicly accessible repository.