  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.
