## 5 Discussion  
The results of the TE analysis provide valuable insights into the connectivity patterns between brain regions in both HCs and patients with schizophrenia. By applying TE to EEG data, we were able to capture the directional flow of information between different electrode sites, providing a more detailed understanding of the underlying neural network dynamics. The TE matrices shown for the selected electrodes--Fz, FCz, Cz, FC3, FC4, C3, C4, CP3, and CP4--reveal key differences in connectivity between schizophrenia patients and HCs. For the HC (Figure 1b), we observe strong and consistent connectivity between central and parietal regions, with the matrix displaying higher TE values across most electrode  
Figure 4: The proposed integration of the DL model into the NeuroPredict platform. Figure 3: The classification report. pairs. This suggests a well-maintained information flow, which is indicative of normal cognitive processing. In contrast, the TE matrix for the schizophrenia patient (Figure 1a) reveals disrupted connectivity patterns, particularly in the frontal and central regions. The reduced TE values between Ez, FCz, and Cz electrodes suggest impaired information flow between these brain regions, which aligns with previous research indicating frontal lobe dysfunction in schizophrenia. These results underscore the importance of examining directional information flow, as it highlights potential breakdowns in brain communication pathways that contribute to the cognitive deficits observed in schizophrenia. One significant observation is the applicability of these results to wearable EEG systems, such as the Muse 2 headband, which targets similar brain regions with electrodes placed at AF7, AF8, TP9, and TP10. While the TE matrices were generated from the EEG dataset, the correlations between the selected electrodes and their corresponding regions in the Muse 2 headband emphasize the potential for real-time connectivity analysis in a clinical or remote setting. This opens the door for integrating such analyses into broader mental health platforms, like NeuroPredict, where continuous monitoring of connectivity changes could facilitate early detection of cognitive decline or schizophrenia relapse. The TE analysis highlights that the Muse 2 headband's placement can capture critical neural signals for detecting schizophrenia-related abnormalities. Integrating these insights into the NeuroPredict platform could provide real-time mental health monitoring, particularly for early detection of cognitive decline or schizophrenia relapse, making it possible to develop predictive models for personalized mental healthcare, enhancing the platform's ability to offer timely interventions and improve patient outcomes. The proposed hybrid CNN-BiLSTM model was trained on TE matrices, allowing it to capture both spatial and temporal patterns of brain connectivity. The CNN component efficiently extracts spatial features from the TE matrices, while the BiLSTM component captures temporal dependencies, making it well suited for this type of connectivity analysis. The model performed robustly in distinguishing between schizophrenia patients and HCs, achieving an overall accuracy of 99.94% on the validation dataset. This result is particularly significant when compared to other DL models applied to schizophrenia detection using EEG data. To contextualize our findings, Table 2 presents a comparison of various AI-driven methods for schizophrenia detection based on EEG data. The table includes accuracies from different models, highlighting how the proposed CNN-BiLSTM approach compares to other advanced techniques in the literature.