Explorative Data Analysis for Changes in Neural Activity
This work addresses the challenge of better neurophysiological interpretation for experimental paradigms in neuroscience, particularly for Brain-Computer Interfacing, but it appears incremental as it builds on existing Stationary Subspace Analysis methods.
The paper tackles the problem of identifying task-induced changes in nonstationary neural recordings, which are often masked by unrelated changes, by proposing a novel algorithm that disentangles different causes of non-stationarity, demonstrated through simulations, theory, and EEG experiments with 80 BCI subjects.
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.