# A Novel Capsule Neural Network Based Model for Drowsiness Detection Using Electroencephalography Signals

arXiv: https://arxiv.org/abs/2204.01666 · HTML: https://mlsanity.com/paper/2204.01666/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 11 of its 49 references that are in the map.
- First posted on arXiv 4 Apr 2022.

## Builds on (most cited first, 11 of 11)

- [SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://mlsanity.com/paper/1904.08779/) (2019)
- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Dynamic Routing Between Capsules](https://mlsanity.com/paper/1710.09829/) (2017)
- [Deep Learning Human Mind for Automated Visual Classification](https://mlsanity.com/paper/1609.00344/) (2016)
- [Capsules for Object Segmentation](https://mlsanity.com/paper/1804.04241/) (2018)
- [Capsule Network Performance on Complex Data](https://mlsanity.com/paper/1712.03480/) (2017)
- [Brain Tumor Type Classification via Capsule Networks](https://mlsanity.com/paper/1802.10200/) (2018)
- [Deep Learning Classification of 3.5 GHz Band Spectrograms with Applications to Spectrum Sensing](https://mlsanity.com/paper/1806.07745/) (2018)
- [Sparse Unsupervised Capsules Generalize Better](https://mlsanity.com/paper/1804.06094/) (2018)
- [Generative Adversarial Network Architectures For Image Synthesis Using Capsule Networks](https://mlsanity.com/paper/1806.03796/) (2018)
- [Detecting inter-sectional accuracy differences in driver drowsiness detection algorithms](https://mlsanity.com/paper/1904.12631/) (2019)

## Abstract (arXiv)

The early detection of drowsiness has become vital to ensure the correct and safe development of several industries' tasks. Due to the transient mental state of a human subject between alertness and drowsiness, automated drowsiness detection is a complex problem to tackle. The electroencephalography signals allow us to record variations in an individual's brain's electrical potential, where each of them gives specific information about a subject's mental state. However, due to this type of signal's nature, its acquisition, in general, is complex, so it is hard to have a large volume of data to apply techniques of Deep Learning for processing and classification optimally. Nevertheless, Capsule Neural Networks are a brand-new Deep Learning algorithm proposed for work with reduced amounts of data. It is a robust algorithm to handle the data's hierarchical relationships, which is an essential characteristic for work with biomedical signals. Therefore, this paper presents a Deep Learning-based method for drowsiness detection with CapsNet by using a concatenation of spectrogram images of the electroencephalography signals channels. The proposed CapsNet model is compared with a Convolutional Neural Network, which is outperformed by the proposed model, which obtains an average accuracy of 86,44% and 87,57% of sensitivity against an average accuracy of 75,86% and 79,47% sensitivity for the CNN, showing that CapsNet is more suitable for this kind of datasets and tasks.