  Leveraging multimodal information from biosignals is vital for building a
comprehensive representation of people's physical and mental states. However,
multimodal biosignals often exhibit substantial distributional shifts between
pretraining and inference datasets, stemming from changes in task specification
or variations in modality compositions. To achieve effective pretraining in the
presence of potential distributional shifts, we propose a frequency-aware
masked autoencoder ($\texttt{bio}$FAME) that learns to parameterize the
representation of biosignals in the frequency space. $\texttt{bio}$FAME
incorporates a frequency-aware transformer, which leverages a fixed-size
Fourier-based operator for global token mixing, independent of the length and
sampling rate of inputs. To maintain the frequency components within each input
channel, we further employ a frequency-maintain pretraining strategy that
performs masked autoencoding in the latent space. The resulting architecture
effectively utilizes multimodal information during pretraining, and can be
seamlessly adapted to diverse tasks and modalities at test time, regardless of
input size and order. We evaluated our approach on a diverse set of transfer
experiments on unimodal time series, achieving an average of $\uparrow$5.5%
improvement in classification accuracy over the previous state-of-the-art.
Furthermore, we demonstrated that our architecture is robust in modality
mismatch scenarios, including unpredicted modality dropout or substitution,
proving its practical utility in real-world applications. Code is available at
https://github.com/apple/ml-famae .
