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.]Neurofeedback / Mindfulness
A. Zhigalov, E. Heinilä, T. Parviainen, L. Parkkonen, and A.Hyvärinen. Decoding attentional states for neurofeedback: Mindfulness vs. wandering thoughts. NeuroImage, 185:565574, 2019. Decoding EEG, MEG, EMG
Hubert Banville, Isabela Albuquerque, Aapo Hyvärinen, Graeme Moffat, DenisAlexander Engemann, Alexandre Gramfort. Selfsupervised representation learning from electroencephalography signals. MLSP, 2019.
J.P. Kauppi, L. Parkkonen, R. Hari, and A. Hyvärinen.
Decoding MEG rhythmic activity using spectrospatial information.
NeuroImage, 83:921936, 2013.
J.P. Kauppi, J. Hahne, K.R. Müller, and A. Hyvärinen.
ThreeWay Analysis of Spectrospatial Electromyography Data: Classification and Interpretation.
PLoS ONE, 83:921936, 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 (IEEEEMBC'17), Jeju, Korea, 2017.
Restingstate connectivity analysis for EEG/MEGJ. Hirayama and T. Ogawa and A. Hyvärinen. Unifying Blind Separation and Clustering for RestingState EEG/MEG Functional Connectivity Analysis. Neural Computation, 27:13731404, 2015.pdf Matlab code [A new method for analysing nonstationarity of powercoherence in EEG and MEG. An extension of ICA where the variance patterns are clustered.]
P. Ramkumar, L. Parkkonen, and A. Hyvärinen.
Grouplevel spatial independent component analysis of Fourier envelopes of restingstate MEG data.
NeuroImage, 86:480491, 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 shorttime Fourier transforms for spontaneous EEG/MEG analysis.
NeuroImage, 49(1):257271, 2010.
Analysing changing connectivityA. Hyvärinen, J. Hirayama , V. Kiviniemi and M. Kawanabe.
Orthogonal Connectivity Factorization: Interpretable decomposition of Variability in Correlation Matrices.
Neural Computation, 28:445484, 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.
Testing and validating ICA
A. Hyvärinen.
Testing the ICA mixing matrix based on intersubject or intersession consistency. NeuroImage, 58:122136, 2011.
A. Hyvärinen and P. Ramkumar.
Testing independent component patterns by intersubject or intersession 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 selforganizing clustering.
NeuroImage, 25(1):193205, 2005.
J. Himberg, A. Hyvärinen and F. Esposito. Validating the independent components of neuroimaging timeseries via clustering and visualization.
NeuroImage 22(3):12141222, 2004.
Hyperscanning in EEG/MEG
C. Campi and L. Parkkonen and R. Hari and A. Hyvärinen. Nonlinear canonical correlation for joint analysis of MEG signals from two subjects.
Frontiers in Brain Imaging Methods 7:107, 2013.
FMRI restingstate 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):253260, 2003.
