# Time Majority Voting, a PC-based EEG Classifier for Non-expert Users

arXiv: https://arxiv.org/abs/2207.12662 · HTML: https://mlsanity.com/paper/2207.12662/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 42 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 10 of 10)

- [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)
- [Hierarchical Consistency Regularized Mean Teacher for Semi-supervised 3D Left Atrium Segmentation](https://mlsanity.com/paper/2105.10369/) (2021)
- [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)
- [Mental State Recognition via Wearable EEG](https://mlsanity.com/paper/1602.00985/) (2016)
- [Multi-Phase Cross-modal Learning for Noninvasive Gene Mutation Prediction in Hepatocellular Carcinoma](https://mlsanity.com/paper/2005.04069/) (2020)
- [Sea-Net: Squeeze-And-Excitation Attention Net For Diabetic Retinopathy Grading](https://mlsanity.com/paper/2010.15344/) (2020)
- [Statistical control for spatio-temporal MEG/EEG source imaging with desparsified multi-task Lasso](https://mlsanity.com/paper/2009.14310/) (2020)
- [Adaptive Mean-Residue Loss for Robust Facial Age Estimation](https://mlsanity.com/paper/2203.17156/) (2022)

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

- [Trends in Machine Learning and Electroencephalogram (EEG): A Review for Undergraduate Researchers](https://mlsanity.com/paper/2307.02819/) (2023)
- [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)
- [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)

Using Machine Learning and Deep Learning to predict cognitive tasks from electroencephalography (EEG) signals is a rapidly advancing field in Brain-Computer Interfaces (BCI). In contrast to the fields of computer vision and natural language processing, the data amount of these trials is still rather tiny. Developing a PC-based machine learning technique to increase the participation of non-expert end-users could help solve this data collection issue. We created a novel algorithm for machine learning called Time Majority Voting (TMV). In our experiment, TMV performed better than cutting-edge algorithms. It can operate efficiently on personal computers for classification tasks involving the BCI. These interpretable data also assisted end-users and researchers in comprehending EEG tests better.