# Signal2Image Modules in Deep Neural Networks for EEG Classification

## TL;DR

This paper explores converting EEG signals into image-like formats using Signal2Image modules to leverage deep neural networks for improved EEG classification accuracy and efficiency.

## Contribution

It introduces and compares trainable and non-trainable Signal2Image modules, demonstrating the superiority of a one-layer CNN S2I in most tested models.

## Key findings

- One-layer CNN S2I outperforms non-trainable S2Is in 11 of 15 models.
- Empirical evidence shows improved EEG classification accuracy.
- Visual analysis of S2I outputs illustrates differences in signal representations.

## Abstract

Deep learning has revolutionized computer vision utilizing the increased availability of big data and the power of parallel computational units such as graphical processing units. The vast majority of deep learning research is conducted using images as training data, however the biomedical domain is rich in physiological signals that are used for diagnosis and prediction problems. It is still an open research question how to best utilize signals to train deep neural networks. In this paper we define the term Signal2Image (S2Is) as trainable or non-trainable prefix modules that convert signals, such as Electroencephalography (EEG), to image-like representations making them suitable for training image-based deep neural networks defined as `base models'. We compare the accuracy and time performance of four S2Is (`signal as image', spectrogram, one and two layer Convolutional Neural Networks (CNNs)) combined with a set of `base models' (LeNet, AlexNet, VGGnet, ResNet, DenseNet) along with the depth-wise and 1D variations of the latter. We also provide empirical evidence that the one layer CNN S2I performs better in eleven out of fifteen tested models than non-trainable S2Is for classifying EEG signals and we present visual comparisons of the outputs of the S2Is.