Emotion-Inspired Deep Structure (EiDS) for EEG Time Series Forecasting

TL;DR

This paper introduces EiDS, an emotion-inspired deep learning model designed to improve EEG time series forecasting, outperforming traditional LSTM models in predicting neurological signals for better diagnosis.

Contribution

The paper proposes a novel emotion-inspired neural architecture for EEG forecasting, inspired by neural structures related to feelings, enhancing prediction accuracy for chaotic EEG signals.

Findings

EiDS outperforms traditional LSTM models in EEG forecasting.

The model effectively predicts both short- and long-term EEG signals.

EiDS demonstrates improved accuracy in neurological disorder diagnosis.

Abstract

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.