Publications by topic
Brain imaging data analysis[The papers here are concentrated on MEG/EEG, and unsupervised learning like ICA. However, a couple of papers consider decoding in MEG/EEG/EMG, and unsupervised learning in fMRI as well.]
Analysing changing connectivity
A. Hyvärinen, J. Hirayama , V. Kiviniemi and M. Kawanabe.
Orthogonal Connectivity Factorization: Interpretable decomposition of Variability in Correlation Matrices.
Neural Computation, 28:445-484, 2016.
J. Hirayama, A. Hyvärinen, V. Kiviniemi, M. Kawanabe and O. Yamashita.
Characterizing Variability of Modular Brain Connectivity with Constrained Principal Component Analysis.
PLoS ONE, 2016.
Decoding EEG, MEG, EMG
J.-P. Kauppi, L. Parkkonen, R. Hari, and A. Hyvärinen.
Decoding MEG rhythmic activity using spectrospatial information.
NeuroImage, 83:921-936, 2013.
J.-P. Kauppi, J. Hahne, K.-R. Müller, and A. Hyvärinen.
Three-Way Analysis of Spectrospatial Electromyography Data: Classification and Interpretation.
PLoS ONE, 83:921-936, 2015.
H. Celikkanat, H. Moriya, T. Ogawa, J.-P. Kauppi, M. Kawanabe and A. Hyvärinen.
Decoding Emotional Valence from
Electroencephalographic Rhythmic Activity.
39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (IEEE-EMBC'17), Jeju, Korea, 2017.
Resting-state connectivity analysis for EEG/MEG
J. Hirayama and T. Ogawa and A. Hyvärinen.
Unifying Blind Separation and Clustering for Resting-State EEG/MEG Functional Connectivity Analysis.
Neural Computation, 27:1373-1404, 2015.
P. Ramkumar, L. Parkkonen, and A. Hyvärinen.
Group-level spatial independent component analysis of Fourier envelopes of resting-state MEG data.
NeuroImage, 86:480-491, 2014.
P. Ramkumar, L. Parkkonen, R. Hari, and A. Hyvärinen.
Characterization of neuromagnetic brain rhythms over time scales of minutes using spatial independent component analysis.
A. Hyvärinen, P. Ramkumar, L. Parkkonen, and R. Hari.
Independent component analysis of short-time Fourier transforms for spontaneous EEG/MEG analysis.
NeuroImage, 49(1):257-271, 2010.
Testing and validating ICA
Testing the ICA mixing matrix based on inter-subject or inter-session consistency. NeuroImage, 58:122-136, 2011.
A. Hyvärinen and P. Ramkumar.
Testing independent component patterns by inter-subject or inter-session consistency. Frontiers in Human Neuroscience, 7:94, 2013.
F. Esposito, T. Scarabino, A. Hyvärinen, J. Himberg, E. Formisano, S. Comani, G. Tedeschi, R. Goebel, E. Seifritz and F. Di Salle. Independent component analysis of fMRI group studies by self-organizing clustering.
NeuroImage, 25(1):193-205, 2005.
J. Himberg, A. Hyvärinen and F. Esposito. Validating the independent components of neuroimaging time-series via clustering and visualization.
NeuroImage 22(3):1214-1222, 2004.
Hyperscanning in EEG/MEG
C. Campi and L. Parkkonen and R. Hari and A. Hyvärinen. Non-linear canonical correlation for joint analysis of MEG signals from two subjects.
Frontiers in Brain Imaging Methods 7:107, 2013.
FMRI resting-state analysis by ICA (in 2003!)
V. Kiviniemi, J. H. Kantola, J. Jauhiainen, A. Hyvärinen and O. Tervonen. Independent component analysis of nondeterministic fMRI signal sources. NeuroImage, 19(2):253-260, 2003.