# Two Heads are Better than One: A Bio-inspired Method for Improving Classification on EEG-ET Data

arXiv: https://arxiv.org/abs/2304.06471 · HTML: https://mlsanity.com/paper/2304.06471/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 49 references that are in the map.
- First posted on arXiv 25 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image Segmentation](https://mlsanity.com/paper/2101.09057/) (2021)
- [EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction](https://mlsanity.com/paper/2111.05100/) (2021)
- [Transfer Learning for Human Activity Recognition using Representational Analysis of Neural Networks](https://mlsanity.com/paper/2012.04479/) (2020)
- [Two Eyes Are Better Than One: Exploiting Binocular Correlation for Diabetic Retinopathy Severity Grading](https://mlsanity.com/paper/2108.06763/) (2021)
- [Spectrally Adaptive Common Spatial Patterns](https://mlsanity.com/paper/2202.04542/) (2022)

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

- [EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures](https://mlsanity.com/paper/2408.03449/) (2024)

## Abstract (arXiv)

Classifying EEG data is integral to the performance of Brain Computer Interfaces (BCI) and their applications. However, external noise often obstructs EEG data due to its biological nature and complex data collection process. Especially when dealing with classification tasks, standard EEG preprocessing approaches extract relevant events and features from the entire dataset. However, these approaches treat all relevant cognitive events equally and overlook the dynamic nature of the brain over time. In contrast, we are inspired by neuroscience studies to use a novel approach that integrates feature selection and time segmentation of EEG data. When tested on the EEGEyeNet dataset, our proposed method significantly increases the performance of Machine Learning classifiers while reducing their respective computational complexity.