# A Deep-Learning-Based Neural Decoding Framework for Emotional Brain-Computer Interfaces

arXiv: https://arxiv.org/abs/2303.04391 · HTML: https://mlsanity.com/paper/2303.04391/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 32 references that are in the map.
- First posted on arXiv 8 Mar 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Machine learning for neural decoding](https://mlsanity.com/paper/1708.00909/) (2017)
- [Data Augmentation for Enhancing EEG-based Emotion Recognition with Deep Generative Models](https://mlsanity.com/paper/2006.05331/) (2020)
- [Reconstruction of Natural Visual Scenes from Neural Spikes with Deep Neural Networks](https://mlsanity.com/paper/1904.13007/) (2019)
- [Towards the Next Generation of Retinal Neuroprosthesis: Visual Computation with Spikes](https://mlsanity.com/paper/2001.04064/) (2020)

## Abstract (arXiv)

Reading emotions precisely from segments of neural activity is crucial for the development of emotional brain-computer interfaces. Among all neural decoding algorithms, deep learning (DL) holds the potential to become the most promising one, yet progress has been limited in recent years. One possible reason is that the efficacy of DL strongly relies on training samples, yet the neural data used for training are often from non-human primates and mixed with plenty of noise, which in turn mislead the training of DL models. Given it is difficult to accurately determine animals' emotions from humans' perspective, we assume the dominant noise in neural data representing different emotions is the labeling error. Here, we report the development and application of a neural decoding framework called Emo-Net that consists of a confidence learning (CL) component and a DL component. The framework is fully data-driven and is capable of decoding emotions from multiple datasets obtained from behaving monkeys. In addition to improving the decoding ability, Emo-Net significantly improves the performance of the base DL models, making emotion recognition in animal models possible. In summary, this framework may inspire novel understandings of the neural basis of emotion and drive the realization of close-loop emotional brain-computer interfaces.