  This paper focuses on subject adaptation for EEG-based visual recognition. It
aims at building a visual stimuli recognition system customized for the target
subject whose EEG samples are limited, by transferring knowledge from abundant
data of source subjects. Existing approaches consider the scenario that samples
of source subjects are accessible during training. However, it is often
infeasible and problematic to access personal biological data like EEG signals
due to privacy issues. In this paper, we introduce a novel and practical
problem setup, namely source-free subject adaptation, where the source subject
data are unavailable and only the pre-trained model parameters are provided for
subject adaptation. To tackle this challenging problem, we propose
classifier-based data generation to simulate EEG samples from source subjects
using classifier responses. Using the generated samples and target subject
data, we perform subject-independent feature learning to exploit the common
knowledge shared across different subjects. Notably, our framework is
generalizable and can adopt any subject-independent learning method. In the
experiments on the EEG-ImageNet40 benchmark, our model brings consistent
improvements regardless of the choice of subject-independent learning. Also,
our method shows promising performance, recording top-1 test accuracy of 74.6%
under the 5-shot setting even without relying on source data. Our code can be
found at
https://github.com/DeepBCI/Deep-BCI/tree/master/1_Intelligent_BCI/Source_Free_Subject_Adaptation_for_EEG.
