# Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers

arXiv: https://arxiv.org/abs/2307.02819 · HTML: https://mlsanity.com/paper/2307.02819/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 64 references that are in the map.
- First posted on arXiv 6 Jul 2023.

## Builds on (most cited first, 13 of 13)

- [BENDR: using transformers and a contrastive self-supervised learning task to learn from massive amounts of EEG data](https://mlsanity.com/paper/2101.12037/) (2021)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Machine Learning for Synthetic Data Generation: A Review](https://mlsanity.com/paper/2302.04062/) (2023)
- [Transformer-based Spatial-Temporal Feature Learning for EEG Decoding](https://mlsanity.com/paper/2106.11170/) (2021)
- [EEG based Emotion Recognition: A Tutorial and Review](https://mlsanity.com/paper/2203.11279/) (2022)
- [EEGEyeNet: a Simultaneous Electroencephalography and Eye-tracking Dataset and Benchmark for Eye Movement Prediction](https://mlsanity.com/paper/2111.05100/) (2021)
- [BiRA-Net: Bilinear Attention Net for Diabetic Retinopathy Grading](https://mlsanity.com/paper/1905.06312/) (2019)
- [Multimodal Continuous Emotion Recognition: A Technical Report for ABAW5](https://mlsanity.com/paper/2303.10335/) (2023)
- [Deep Learning in EEG: Advance of the Last Ten-Year Critical Period](https://mlsanity.com/paper/2011.11128/) (2020)
- [Efficacy of Transformer Networks for Classification of Raw EEG Data](https://mlsanity.com/paper/2202.05170/) (2022)
- [Time Majority Voting, a PC-based EEG Classifier for Non-expert Users](https://mlsanity.com/paper/2207.12662/) (2022)
- [Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading](https://mlsanity.com/paper/2010.15344/) (2020)
- [BrainActivity1: A Framework of EEG Data Collection and Machine Learning Analysis for College Students](https://mlsanity.com/paper/2207.13239/) (2022)

## Built on by (most cited first, 7 of 7)

- [ViT2EEG: Leveraging Hybrid Pretrained Vision Transformers for EEG Data](https://mlsanity.com/paper/2308.00454/) (2023)
- [Fusing Pretrained ViTs with TCNet for Enhanced EEG Regression](https://mlsanity.com/paper/2404.15311/) (2024)
- [AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection](https://mlsanity.com/paper/2405.03075/) (2024)
- [Advancing EEG-Based Gaze Prediction Using Depthwise Separable Convolution and Enhanced Pre-Processing](https://mlsanity.com/paper/2408.03480/) (2024)
- [EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures](https://mlsanity.com/paper/2408.03449/) (2024)
- [Integrating HCI Datasets in Project-Based Machine Learning Courses: A College-Level Review and Case Study](https://mlsanity.com/paper/2408.03472/) (2024)
- [Effect of Kernel Size on CNN-Vision-Transformer-Based Gaze Prediction Using Electroencephalography Data](https://mlsanity.com/paper/2408.03478/) (2024)

## Abstract (arXiv)

This paper presents a systematic literature review on Brain-Computer Interfaces (BCIs) in the context of Machine Learning. Our focus is on Electroencephalography (EEG) research, highlighting the latest trends as of 2023. The objective is to provide undergraduate researchers with an accessible overview of the BCI field, covering tasks, algorithms, and datasets. By synthesizing recent findings, our aim is to offer a fundamental understanding of BCI research, identifying promising avenues for future investigations.