# EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification

arXiv: https://arxiv.org/abs/2107.03983 · HTML: https://mlsanity.com/paper/2107.03983/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 45 references that are in the map.
- First posted on arXiv 8 Jul 2021.

## Builds on (most cited first, 18 of 18)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Xception: Deep Learning with Depthwise Separable Convolutions](https://mlsanity.com/paper/1610.02357/) (2016)
- [Similarity of Neural Network Representations Revisited](https://mlsanity.com/paper/1905.00414/) (2019)
- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Deep learning-based electroencephalography analysis: a systematic review](https://mlsanity.com/paper/1901.05498/) (2019)
- [Deep Learning Human Mind for Automated Visual Classification](https://mlsanity.com/paper/1609.00344/) (2016)
- [Learning Representations from EEG with Deep Recurrent-Convolutional Neural Networks](https://mlsanity.com/paper/1511.06448/) (2015)
- [ConvTransformer: A Convolutional Transformer Network for Video Frame Synthesis](https://mlsanity.com/paper/2011.10185/) (2020)
- [EEG based Continuous Speech Recognition using Transformers](https://mlsanity.com/paper/2001.00501/) (2019)
- [Convolutional Neural Network Approach for EEG-based Emotion Recognition using Brain Connectivity and its Spatial Information](https://mlsanity.com/paper/1809.04208/) (2018)
- [Decoding Visual Recognition of Objects from EEG Signals based on Attention-Driven Convolutional Neural Network](https://mlsanity.com/paper/2008.12490/) (2020)
- [Comparison of Attention-based Deep Learning Models for EEG Classification](https://mlsanity.com/paper/2012.01074/) (2020)
- [An EEG-based Image Annotation System](https://mlsanity.com/paper/1711.02383/) (2017)
- [An Evoked Potential-Guided Deep Learning Brain Representation For Visual Classification](https://mlsanity.com/paper/2006.15357/) (2020)

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

- [Copilot-Assisted Second-Thought Framework for Brain-to-Robot Hand Motion Decoding](https://mlsanity.com/paper/2603.27492/) (2026)

## Abstract (arXiv)

Different categories of visual stimuli activate different responses in the human brain. These signals can be captured with EEG for utilization in applications such as Brain-Computer Interface (BCI). However, accurate classification of single-trial data is challenging due to low signal-to-noise ratio of EEG. This work introduces an EEG-ConvTranformer network that is based on multi-headed self-attention. Unlike other transformers, the model incorporates self-attention to capture inter-region interactions. It further extends to adjunct convolutional filters with multi-head attention as a single module to learn temporal patterns. Experimental results demonstrate that EEG-ConvTransformer achieves improved classification accuracy over the state-of-the-art techniques across five different visual stimuli classification tasks. Finally, quantitative analysis of inter-head diversity also shows low similarity in representational subspaces, emphasizing the implicit diversity of multi-head attention.