# Decoding Event-related Potential from Ear-EEG Signals based on Ensemble Convolutional Neural Networks in Ambulatory Environment

arXiv: https://arxiv.org/abs/2103.02197 · HTML: https://mlsanity.com/paper/2103.02197/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 3 Mar 2021.

## Builds on (most cited first, 2 of 2)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Decoding Visual Responses based on Deep Neural Networks with Ear-EEG Signals](https://mlsanity.com/paper/2002.01085/) (2020)

## Built on by (most cited first, 1 of 1)

- [Brain-Driven Representation Learning Based on Diffusion Model](https://mlsanity.com/paper/2311.07925/) (2023)

## Abstract (arXiv)

Recently, practical brain-computer interface is actively carried out, especially, in an ambulatory environment. However, the electroencephalography (EEG) signals are distorted by movement artifacts and electromyography signals when users are moving, which make hard to recognize human intention. In addition, as hardware issues are also challenging, ear-EEG has been developed for practical brain-computer interface and has been widely used. In this paper, we proposed ensemble-based convolutional neural networks in ambulatory environment and analyzed the visual event-related potential responses in scalp- and ear-EEG in terms of statistical analysis and brain-computer interface performance. The brain-computer interface performance deteriorated as 3-14% when walking fast at 1.6 m/s. The proposed methods showed 0.728 in average of the area under the curve. The proposed method shows robust to the ambulatory environment and imbalanced data as well.