Abstract
Neural recordings are non-stationary 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 subjects.
| Original language | English |
|---|---|
| Article number | 026018 |
| Journal | Journal of Neural Engineering |
| Volume | 10 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2013 Apr 1 |
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ASJC Scopus subject areas
- Biomedical Engineering
- Cellular and Molecular Neuroscience
Cite this
Explorative data analysis for changes in neural activity. / Blythe, Duncan A J; Meinecke, Frank C.; Von Bünau, Paul; Muller, Klaus.
In: Journal of Neural Engineering, Vol. 10, No. 2, 026018, 01.04.2013.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Explorative data analysis for changes in neural activity
AU - Blythe, Duncan A J
AU - Meinecke, Frank C.
AU - Von Bünau, Paul
AU - Muller, Klaus
PY - 2013/4/1
Y1 - 2013/4/1
N2 - Neural recordings are non-stationary 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 subjects.
AB - Neural recordings are non-stationary 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 subjects.
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UR - http://www.scopus.com/inward/citedby.url?scp=84875847045&partnerID=8YFLogxK
U2 - 10.1088/1741-2560/10/2/026018
DO - 10.1088/1741-2560/10/2/026018
M3 - Article
C2 - 23502973
AN - SCOPUS:84875847045
VL - 10
JO - Journal of Neural Engineering
JF - Journal of Neural Engineering
SN - 1741-2560
IS - 2
M1 - 026018
ER -