  Electroencephalography (EEG) is shown to be a valuable data source for
evaluating subjects' mental states. However, the interpretation of multi-modal
EEG signals is challenging, as they suffer from poor signal-to-noise-ratio, are
highly subject-dependent, and are bound to the equipment and experimental setup
used, (i.e. domain). This leads to machine learning models often suffer from
poor generalization ability, where they perform significantly worse on
real-world data than on the exploited training data. Recent research heavily
focuses on cross-subject and cross-session transfer learning frameworks to
reduce domain calibration efforts for EEG signals. We argue that multi-source
learning via learning domain-invariant representations from multiple
data-sources is a viable alternative, as the available data from different EEG
data-source domains (e.g., subjects, sessions, experimental setups) grow
massively. We propose an adversarial inference approach to learn data-source
invariant representations in this context, enabling multi-source learning for
EEG-based brain-computer interfaces. We unify EEG recordings from different
source domains (i.e., emotion recognition datasets SEED, SEED-IV, DEAP,
DREAMER), and demonstrate the feasibility of our invariant representation
learning approach in suppressing data-source-relevant information leakage by
35% while still achieving stable EEG-based emotion classification performance.
