# AI-Based Electroencephalogram Analysis in Rodent Models of Epilepsy: A Systematic Review
## 5 Discussion
### Research Gaps
As part of the complete data science procedure, performance and interpretability metrics should be iteratively refined to update other steps of the process [205]. Consequently, [PERSON] et al. [206] suggested interpretability as beneficial for troubleshooting in practical scenarios, leading to improved rather than diminished performance. Furthermore, interpretability should be prioritised for high-stakes decisions like criminal justice, medicine, and banking rather than using post hoc models to explain black-box credibility [206; 207]. Although interpretability and explainability are often used interchangeably, post hoc explainability techniques aim to audit black-box ML/DL models by creating simplified surrogate models [208]. By extension, nontechnical people can use these simplified surrogate models to understand the inner workings of black-box ML/DL models [209; 210]. However, concerns have been raised regarding the effectiveness of post hoc explainability methods in elucidating the inner mechanism of black-box models, and certain drawbacks have been observed [200; 207; 208; 211; 212; 213]. Amid these concerns, numerous ML/DL approaches have been developed that maintain high accuracy while achieving significant interpretability, even on the most challenging datasets [214; 215; 216; 217; 218; 219]. Hence, the interpretable detection of epileptiform discharge in rodent models of epilepsy should be prioritised. It is also important to involve preclinical researchers as stakeholders in the development of these models rather than developing them solely within the field of computer science. This ensures that their requirements are taken into consideration. Regardless of the training methodology adopted in training ML/DL models, data splitting should be disjointed such that all epochs of a mouse belong to the same set. Only one study clearly stated how the signal epochs were split without intersection [120]. In addition to training data, training methodology or data splitting is critical to the performance of the ML/DL models. As is well-known, the performance of a model on the training set is an unduly optimistic prediction of its performance on unseen data. It is, therefore, a common practice to set aside some data to evaluate the performance of a model. The most straightforward and probably the most frequently used training strategy has been the train-test split [220]. The seven studies in this survey that adopted the train-test split methodology ensured the epileptiform discharge classes were balanced [102; 103; 106; 108; 110; 111; 118] despite the inability of this data-splitting methodology to maintain a balanced representation in the train sets and EEG data being highly unbalanced, resulting from the scarcity of epileptiform discharges in EEG signals. Furthermore, the train-test split is less efficient with limited data. With the limited number of mice in the studies, using train-test split is unsuitable and risks overfitting/underfitting. Additionally, different splits may lead to varying model performances [221]. On the contrary, K-fold cross validation reduces the dependence of the model on the single source of training data, and a stable model estimate is achieved by averaging the performance of the different folds. K-fold cross validation was adopted in eight studies in this survey; the most frequently used is ten-fold cross-validation. This training methodology has no defined rule for determining the optimal value of K. The degree of variance and bias brought about by the training process and the selection of K are subject to trade-offs. Hence, a range of K values should be tested by treating the value of K as a hyperparameter tuned to optimize the performance of the model. The models in this survey cannot be directly compared owing to the nonuniformity of the evaluation metrics reported in the respective studies.