Emotion-Inspired Deep Structure (EiDS) for EEG Time Series Forecasting
This work addresses EEG forecasting for medical diagnosis, but appears incremental as it builds on existing LSTM methods.
The authors tackled the problem of forecasting chaotic EEG time series for diagnosing neurological disorders by proposing an emotion-inspired deep structure (EiDS), which achieved performance comparisons with LSTM variants.
Accurate forecasting of an electroencephalogram (EEG) time series is crucial for the correct diagnosis of neurological disorders such as seizures and epilepsy. Since the EEG time series is chaotic, most traditional machine learning algorithms have failed to forecast its next steps accurately. Thus, we suggest a model, which has formed by taking inspiration from the neural structures that underlie feelings (emotional states), to forecast EEG time series. The model, which is referred to as emotion-inspired deep structure (EiDS), can be used to predict both short- and long-term of EEG time series. This paper also compares the performance of EiDS with other variations of long short-term memory (LSTM) networks.