I research into physics and modeling of bioelectromagnetic phenomena, with strong focus on boundary element modeling of quasi-static electromagnetic fields. The main application field for this research is in MEG/EEG, noninvasive brain stimulation, and ECG/MCG. I teach electromagnetic field theory for BSc students of applied physics, and field theory, principles of imaging, and bioelectromagnetism for MSc/PhD students of applied physics and biomedical engineering.

I am happy to share my Matlab-based field-computing, inverse modeling and visualization tools, some of them for collaborative use only, and some quite freely.

• Helsinki BEM library (Stenroos et al., CMPB 2007) is a learning-oriented BEM solver, on which my newer tools are conceptually based. Please see the almost-up-to-date website! The package is well documented and good for learning the BEM, for ECG/MCG, and for general noodling-around. For brainy applications, the newer tools are easier to use. The library is open source and the license is quite strict.
• MEGBEM is a simple, fast, easy-to-use BEM solver aimed for making MEG forward models for experimental use. It works also for MCG. The package is distilled from the test solver used successfully in our paper (M. Stenroos, A. Hunold, J. Haueisen, Neuroimage 2014). The solver uses linear collocation BEM (streamlined and optimized from (Stenroos et al., CMPB 2007)) and isolated-source approach (Stenroos and Sarvas, Phys Med Biol 2012). This solver is not as accurate as my reference solver, but with some precautions it is, however, fully OK for making single- or three-shell MEG models (see the paper).
  There is a strict-license open source package and a more relaxed closed-core package. I would be happy to have the closed-core package included in some analysis toolboxes. Please contact me, if you would like to use this solver or include it in your own analysis package! (I will make a web page ... some day).
• EMEGBEM is essentially the same as MEGBEM but contains also EEG. The solver uses simple, traditional collocation BEM that does not perform too well in idealized EEG simulations, but when typical model simplifications and uncertainties are taken into account, we see that it is fine; in typical 3-shell modeling it may actually be slightly better than the more accurate solvers... For further info on this slightly confusing topic, please see (M. Stenroos and A. Nummenmaa, PLOS ONE, 2016). A lot of things are under construction in this package, but the actual solver core is fine.
• gBEM is the latest extension to my modeling toolset; see the fresh paper (M. Stenroos, Phys Med Biol, 2016). It enables the use of any piece-wise homogeneous geometry in the solving of static potential problems. I share parts of my gBEM tools in order to advance the use of surface-based models, development of meshing, file formats, etc.; it is not ready for application use yet, but I'd be happy to collaborate, if you have an interesting research question, where this kind of modeling would be needed.

• DeFleCT is an approach designing/implementing optimal linear spatial filters for analysis of MEG/EEG data, developed by Olaf Hauk and me at MRC CBU, Cambridge UK. The work is presented in (O. Hauk and M. Stenroos, Hum Brain Mapp 2013) and the Matlab code & the models used in the paper are available here
• If you like visualizations in my papers or conference talks and would like to use them, drop me a line & we'll see, whether I have some tools wrapped for sharing.

If you would like to do your BSc thesis, MSc special assigment or master thesis on my research field please contact me. At the moment I have two topics ready:

1) Adaptive numerical integration for linear Galerkin boundary element solver. This topic is suits a student who has interest in numerical field computation and some experience in programming with Matlab. The study background could by physics, maths, biomedical engineering, radio sciences, or computer science. This work is preferably MSc level.

2) Construction of surface meshes for general piece-wise homogeneous medium. This work is good for a student interested in image processing, numerical modeling, and models of human anatomy. Some studies or other background on image processing and meshing would be nice but is not mandatory; the level of the work is adaptive.