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.

翻译：从神经活动片段中精确读取情绪对于情绪脑机接口的发展至关重要。在所有神经解码算法中，深度学习（DL）有潜力成为最具前景的方法，但近年来进展有限。一个可能的原因是，深度学习的有效性高度依赖于训练样本，而用于训练的神经数据往往来自非人灵长类动物，且混杂着大量噪声，这反而误导了深度学习模型的训练。鉴于从人类角度难以精确确定动物的情绪，我们假设代表不同情绪的神经数据中的主要噪声是标注错误。在此，我们报告一种名为Emo-Net的神经解码框架的开发与应用，该框架由置信学习（CL）组件和深度学习组件组成。该框架完全由数据驱动，能够从行为猴子的多个数据集中解码情绪。除了提升解码能力外，Emo-Net还显著提高了基础深度学习模型的性能，使动物模型中的情绪识别成为可能。总之，该框架可能启发对情绪神经基础的新理解，并推动闭环情绪脑机接口的实现。