deep learning for robust eeg signal forecasting using long short term memory neural network

Signal forecasting in the medical field has many applications, such as signal correction and anomaly detection. according to this application, robust forecasting is required to obtain a signal identical to the original signal. this study proposes a forecasting technique that obtains a robust signal that can be used in different applications. a long short-term memory neural network (lstm-nn) was used to predict future samples from present and past samples. an electroencephalography (eeg) dataset was used to test this technique. four channels were used as input examples, one of which was the predicted output. all four channel samples were fed into the four networks to predict the future samples. to decrease complexity, only one hidden layer is used for this purpose. the statistical results are promising for applications that require an almost perfectly predicted signal. the number of hidden cells is first very low (five cells only), which gives a root mean square error of less than 20, whereas when the number of hidden cells is increased to 100, the root mean square error (rmse) is approximately 7.5 for all four channels.