## 4 Discussion
### Limitations and Future Directions  
In terms of study limitations, we should acknowledge (i) the limited sample size, which hinders the generalizability of our current findings, and (ii) the heterogenicity of the pediatric cohort in terms of underlying pathology, which could potentially bias our results.  
In the field of epilepsy, collecting large amounts of data is challenging as the time to add a patient with their surgical outcome would take a minimum of 1 year. Further, all patients diagnosed with epilepsy are not considered for surgery or for iEEG. We are continuously adding patients to our cohort as and when they become available. Multi-site collaborations are also warranted to increase sample size and generalizability. The other key issue that we need to address is the harmonization of data collection, as each of the centers has its own protocol for treating patients with epilepsy. We intend to test our hypothesis on a larger scale and test the feasibility of these features. This would enable us to test the generalizability of differences in pathology, age, sex, and race.  
Our promising findings encourage further investigations on larger cohorts as these would allow to (i) control for potential confounding factors and (ii) train or tune the hyper-parameters of the deep learning network on actual iEEG TF images (instead of utilizing pre-trained network architecture validated on generic real-world images). Our findings also warrant further investigations to compare the benefits of the proposed image-based method versus feeding the raw iEEG data into a machine learning or deep learning algorithm. Given that our current method mimics the human observer, we would like to leverage the power of modern signal processing and artificial intelligence to seek biomarkers suited to identify the pathology and work towards optimizing the resection volume to enhance the quality of life of patients with epilepsy. Finally, our data also open the avenues to exploring other applications of visual complexity metrics in EEG and medical imaging analysis.  
The iEEG recordings present some challenges and limitations in clinical practice: they are costly, require a cooperative patient, carry surgical risks [51], and leave large brain areas unexplored. Thus, our promising findings prompt future efforts toward the translation of the proposed methodology to scalp EEG and magnetoencephalography data since they are non-invasive pre-surgical tools that can contribute to the pre-surgical estimation of the SOZ without the need for invasive recordings [26; 28; 52; 53].  
Conceptualization, N.M. and E.T.; methodology, N.M., S.G. and E.T.; software, N.M. and S.G.; validation, N.M., S.G. and E.T.; formal analysis, N.M., S.G. and E.T.; investigation, N.M., S.G. and E.T.; resources, J.B., S.S., P.P., A.R., P.E.G. and E.T.; data curation, N.M., S.G., M.P. ([PERSON]) and E.T.; writing--original draft preparation, N.M. and E.T.; writing--review and editing, N.M., S.G., M.P. ([PERSON]), J.B., S.S., P.P., D.H., M.P. ([PERSON]), C.P., A.R., P.E.G. and E.T.; visualization, N.M. and E.T.; supervision, E.T.; project administration, N.M. and E.T.; funding acquisition, E.T. All authors have read and agreed to the published version of the manuscript.  
The research reported in this publication was supported by the National Institute Of Neurological Disorders And Stroke of the National Institutes of Health under Award Number R03 NS127044. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.  
Due to privacy and ethical restrictions, data will be made available upon request to the corresponding author.  
The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.