# Decoding Visual Recognition of Objects from EEG Signals based on Attention-Driven Convolutional Neural Network

arXiv: https://arxiv.org/abs/2008.12490 · HTML: https://mlsanity.com/paper/2008.12490/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Self-supervised Learning for Electroencephalogram: A Systematic Survey](https://mlsanity.com/paper/2401.05446/) (2024)
- [EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli Classification](https://mlsanity.com/paper/2107.03983/) (2021)
- [Learning Exemplar Representations in Single-Trial EEG Category Decoding](https://mlsanity.com/paper/2406.16902/) (2024)
- [The Repeated-Stimulus Confound in Electroencephalography](https://mlsanity.com/paper/2508.00531/) (2025)
- [Visual Semantic Decoding of Electrocorticography from Video Stimuli using End-to-End Deep Learning](https://mlsanity.com/paper/2607.18923/) (2026)

## Abstract (arXiv)

The ability to perceive and recognize objects is fundamental for the interaction with the external environment. Studies that investigate them and their relationship with brain activity changes have been increasing due to the possible application in an intuitive brain-machine interface (BMI). In addition, the distinctive patterns when presenting different visual stimuli that make data differentiable enough to be classified have been studied. However, reported classification accuracy still low or employed techniques for obtaining brain signals are impractical to use in real environments. In this study, we aim to decode electroencephalography (EEG) signals depending on the provided visual stimulus. Subjects were presented with 72 photographs belonging to 6 different semantic categories. We classified 6 categories and 72 exemplars according to visual stimuli using EEG signals. In order to achieve a high classification accuracy, we proposed an attention driven convolutional neural network and compared our results with conventional methods used for classifying EEG signals. We reported an accuracy of 50.37% and 26.75% for 6-class and 72-class, respectively. These results statistically outperformed other conventional methods. This was possible because of the application of the attention network using human visual pathways. Our findings showed that EEG signals are possible to differentiate when subjects are presented with visual stimulus of different semantic categories and at an exemplar-level with a high classification accuracy; this demonstrates its viability to be applied it in a real-world BMI.