论文标题
脑电图时间序列预测的情绪启发的深层结构（EID）
Emotion-Inspired Deep Structure (EiDS) for EEG Time Series Forecasting
论文作者
论文摘要
对脑电图（EEG）时间序列的准确预测对于正确诊断神经系统疾病（例如癫痫发作和癫痫）至关重要。由于EEG时间序列是混乱的，因此大多数传统的机器学习算法都无法准确预测其下一步。因此，我们提出了一个模型，该模型是通过从感受（情绪状态）的神经结构中汲取灵感来预测脑电图时间序列而形成的。该模型被称为情感启发的深层结构（EID），可用于预测脑电图时间序列的短期和长期。本文还将EID的性能与长期短期内存（LSTM）网络的其他变化进行了比较。
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.
