M. Weis

Multi-Dimensional Signal Decomposition Techniques for the Analysis of EEG Data

M. Weis, 2015 .

A CORRELATION TENSOR – BASED MODEL FOR TIME VARIANT FREQUENC Y SELECTIVE MIMO CHANNELS

Martin Haardt, Giovanni Del Galdo, M. Haardt, 2007 .

Analytical performance evaluation for HOSVD-based parameter estimation schemes

Florian Roemer, Martin Haardt, Hanna Becker, 2009, 2009 3rd IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP).

Polygraphic representation of anxiety disorder : a case study

P. Husar, M. Weis, T. Günther, 2010 .

Multi-dimensional PARAFAC2 component analysis of multi-channel EEG data including temporal tracking

Florian Roemer, Martin Haardt, Martin Weis, 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

Multi-dimensional space-time-frequency component analysis of event related EEG data using closed-form PARAFAC

Florian Roemer, Martin Haardt, Martin Weis, 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

Evaluation of multi-dimensional decomposition models using synthetic moving EEG potentials

Martin Weis, Peter Husar, Judith Mengelkamp, 2013, 21st European Signal Processing Conference (EUSIPCO 2013).

Temporally resolved multi-way component analysis of dynamic sources in event-related EEG data using parafac2

Florian Roemer, Martin Haardt, Thomas Guenther, 2010, 2010 18th European Signal Processing Conference.

Identification of Signal Components in Multi-Channel EEG Signals via Closed-Form PARAFAC Analysis and Appropriate Preprocessing

Florian Roemer, Martin Haardt, Martin Weis, 2009 .

Neuronal cell spike sorting using signal features extracted by PARAFAC

P. Husar, T. Just, T. Kautz, 2013, 2013 6th International IEEE/EMBS Conference on Neural Engineering (NER).