Appropriate data segmentation improves speech encoding models
In recent decades, research on the neural processing of speech and language increasingly investigated ongoing responses to continuously presented naturalistic speech, allowing researchers to ask interesting questions about different representations of speech and their relationships. This requires statistical models that can dissect different sources of variance occurring in the processing of naturalistic speech. One commonly used family of models are temporal response functions (TRFs) which can predict neural responses to speech as a weighted combination of different features and points in time. TRFs model the brain as a linear time-invariant (LTI) system whose responses can be characterized by constant transfer functions. This implicitly assumes that the underlying signals are stationary, varying to a fixed degree around a constant mean. However, continuous neural recordings commonly violate this assumption. Here, we use simulations and EEG recordings to investigate how non-stationarities affect TRF models for continuous speech processing. Our results suggest that non-stationarities may impair the performance of TRF models, but that this can be partially remedied by dividing the data into shorter segments that approximate stationarity.