Explorative Data Analysis for Changes in Neural Activity

TL;DR

This paper introduces a novel algorithm that disentangles different causes of non-stationarity in neural data, improving the interpretation of neural changes related to learning and other processes.

Contribution

The paper presents a new method using repeated SSA at multiple scales to better identify meaningful neural activity changes amidst unrelated variations.

Findings

Effective in simulations, theory, and EEG BCI data

Improves detection of learning-related neural changes

Handles complex non-stationarities in neural recordings

Abstract

Neural recordings are nonstationary time series, i.e. their properties typically change over time. Identifying specific changes, e.g. those induced by a learning task, can shed light on the underlying neural processes. However, such changes of interest are often masked by strong unrelated changes, which can be of physiological origin or due to measurement artifacts. We propose a novel algorithm for disentangling such different causes of non-stationarity and in this manner enable better neurophysiological interpretation for a wider set of experimental paradigms. A key ingredient is the repeated application of Stationary Subspace Analysis (SSA) using different temporal scales. The usefulness of our explorative approach is demonstrated in simulations, theory and EEG experiments with 80 Brain-Computer-Interfacing (BCI) subjects.