Noise Reduction in EEG Signals using Convolutional Autoencoding Techniques
Conor T. Hanrahan · Arrow - TU Dublin (Technological University Dublin) · 2019
The presence of noise in electroencephalography (EEG) signals can significantly reduce the accuracy of the analysis of the signal. This study assesses to what extent stacked autoencoders designed using one-dimensional convolutional neural network layers can reduce noise in EEG signals. The EEG signals, obtained from 81 people, were processed by a two-layer one-dimensional convolutional autoencoder (CAE), whom performed 3 independent button pressing tasks. The signal-to-noise ratios (SNRs) of the signals before and after processing were calculated and the distributions of the SNRs were compared. The performance of the model was compared to noise reduction performance of Principal Component Analysis, with 95% explained variance, by comparing the Harrell-Davis decile differences between the SNR distributions of both methods and the raw signal SNR distribution for each task. It was found that the CAE outperformed PCA for the full dataset across all three tasks, however the CAE did not outperform PCA for the person specific datasets in any of the three tasks. The results indicate that CAEs can perform better than PCA for noise reduction in EEG signals, but performance of the model may be training size dependent.