Forecasting events in multichannel electroencephalographic (EEG) brain recordings remains a formidable task given the noise and complexity in neural systems. Here we compare two dynamical systems motivated approaches to forecasting brain events. The first follows previous state-of-the-art (SOTA) research of time-series features of critical slowing down (autocorrelation, variance) as biomarkers of impending events. The second involves a novel long-term-short-term (LSTM) neural network-based filter to estimate the neurophysiological feature variables of mathematical neural population models of the EEG. Previous critical slowing research presented forecasting results for the best EEG channel, however, in practice the best channel cannot be known a priori. Therefore, here we also consider forecasting by combining the different features across the different EEG channels using logistic regression. One application area where forecasting brain events is important is epileptic seizure prediction. Epileptic seizures are debilitating events and up to 50 million people worldwide with drug-resistant epilepsy could benefit by receiving warnings of impending seizures. Here we apply the above methods to a long-term epileptic seizure prediction dataset from 15 patients. It was found that seizure forecasting with (1) logistic regression and critical slowing features, (2) logistic regression and neurophysiological features, and (3) the best channel using critical slowing features, respectively, achieved median sensitivities of 70,54 and $67 \%$ and median time in low seizure risk of 84,84, and $81 \%$. This indicates that a multichannel model approach can perform as well as the best channel approach, removing the need to find the best channel. It also suggests neurophysiological features could be used to increase time in low risk. Future work exploring other features, machine learning models and their various combinations could yield further improvements.

This paper introduces a new inference-based framework for time-resolved chaos analysis of brain models and demonstrates its application to focal epileptic seizures. The intermittent nature of epileptic seizures exhibits an unpredictable behavior that shares some characteristics with chaotic systems. Epilepsy research often uses concepts from chaos theory and nonlinear dynamics to better understand the mechanisms of seizure initiation, propagation, and termination. Traditional methods estimate the degree of chaos in brain dynamics directly from time series data. This provides neither an accurate estimate of the chaos nor insights into the key neurophysiological processes driving brain dynamics during epileptic seizures. Therefore, this study proposes a new method to calculate Lyapunov spectra by combining time series data with neurophysiological brain models and a specialised nonlinear Kalman filter. This study thereby provides insights into the temporal evolution of chaos in epileptogenic regions during epileptic seizures and identifies external inputs from adjacent and distant brain regions as major drivers of altered levels of chaoticity. This paper underscores the importance of fusion of neurophysiological computational models and clinical time series data in understanding the dynamic and chaotic aspects of epilepsy to develop more effective diagnostic and treatment strategies.