# A Study of EEG Feature Complexity in Epileptic Seizure Prediction
## 4 Conclusions  
This study investigated the complexity of a superset of EEG-based features commonly practiced to distinguish an inter-ictal period from a pre-ictal in epileptic seizure prediction. The investigation is based on a classifier-independent complexity analysis which used complexity measures, such as the Fisher discriminant ratio and the volume of class overlap in feature space, to evaluate the discriminant potency of each feature. Implemented using the publicly available Boston Children's Hospital database of EEG records, the analysis supports the conclusion that the features and, thereof, feature-based distinction of the pre-ictal and inter-ictal periods in EEG records, are highly complex.  
This study can be strengthened along three majors veins. Along one vein, larger amounts of data, using different other EEG databases, can confirm and strengthen its conclusions on feature complexity and inter-subject variability. Along a second vein, features other than those generally practiced, which are those used in this study, can be investigated. To this end, feature computation by deep machine learning is exceptionally promising, as it has had recently a remarkable performance with similar and more difficult data. Finally, it can be of significant benefit to investigate domain adaptation for EEG data, whereby the dependence on large amounts of data for accurate EEG-based decision can be alleviated.  
Conceptualization, I.J., A.M., and N.M.; methodology, I.J., A.M., and N.M.; software, I.J.; validation, A.M.and N.M.; formal analysis, I.J.; investigation, I.J., A.M., and N.M.; resources, N.M.; data curation, I.J.; writing--original draft preparation, I.J.; writing--review and editing, A.M. and N.M.; visualization, I.J.; supervision, A.M. and N.M.; project administration, A.M. and N.M.; funding acquisition, I.J., A.M., and N.M. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Canada Research Chair on Biomedical Data Mining (950-231214) and the discovery grant (RGPIN-2020-06313).  
Not applicable.  
The CHB-MIT Scalp EEG Database is available at [[https://physionet.org/content/chbmit/1.0.0/](https://physionet.org/content/chbmit/1.0.0/)]([https://physionet.org/content/chbmit/1.0.0/](https://physionet.org/content/chbmit/1.0.0/)) (accessed on 9 June 2020).  
This research was supported by the Canada Research Chair on Biomedical Data Mining (950-231214).  
The authors declare no conflict of interest.  
The extracted features from EEG records used in the complexity analysis.  
\begin{tabular}{l l}
**Symbols** & **Extracted features** \\ \(\sigma^{2}\) & variance \\ \(\chi\) & skewness \\ \(\kappa\) & kurtosis \\ HM & Hjorth parameter (mobility) \\ HC & Hjorth parameter (complexity) \\ \(\tau_{0}\) & decorrelation time \\ \(\epsilon_{err}\) & error of the auto-regressive modeling \\ \(\delta_{r}\) & relative power of the delta spectral band \\ \(\theta_{r}\) & relative power of the theta spectral band \\ \(a_{r}\) & relative power of the alpha spectral band \\ \(\beta_{r}\) & relative power of the beta spectral band \\ \(\gamma_{r}\) & relative power of the gamma spectral band \\ \(f_{50}\) & spectral edge frequency \\ \(E_{w}\) & wavelet energy \\ \(S_{w}\) & wavelet entropy \\ \(E\) & signal energy \\ \end{tabular}