  This paper focuses on EEG-based visual recognition, aiming to predict the
visual object class observed by a subject based on his/her EEG signals. One of
the main challenges is the large variation between signals from different
subjects. It limits recognition systems to work only for the subjects involved
in model training, which is undesirable for real-world scenarios where new
subjects are frequently added. This limitation can be alleviated by collecting
a large amount of data for each new user, yet it is costly and sometimes
infeasible. To make the task more practical, we introduce a novel problem
setting, namely subject adaptive EEG-based visual recognition. In this setting,
a bunch of pre-recorded data of existing users (source) is available, while
only a little training data from a new user (target) are provided. At inference
time, the model is evaluated solely on the signals from the target user. This
setting is challenging, especially because training samples from source
subjects may not be helpful when evaluating the model on the data from the
target subject. To tackle the new problem, we design a simple yet effective
baseline that minimizes the discrepancy between feature distributions from
different subjects, which allows the model to extract subject-independent
features. Consequently, our model can learn the common knowledge shared among
subjects, thereby significantly improving the recognition performance for the
target subject. In the experiments, we demonstrate the effectiveness of our
method under various settings. Our code is available at
https://github.com/DeepBCI/Deep-BCI/tree/master/1_Intelligent_BCI/Subject_Adaptive_EEG_based_Visual_Recognition.
