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My grandmother was an outstanding cook. So when I recently came across her old cook book I tried to read through some of the recipes, hoping I could recreate some of the dishes I enjoyed as a kid. However this turned out harder than expected since the book was printed around 1911 in a typeface called f raktur.

The world around is a dynamic mixture of signals from various sources. Just like the colors in the above picture blend into one another, giving rise to new shades and tones, everything we perceive is a fusion of simpler components. Most of the time we are not even aware that the world around us is such a chaotic intermix of independent processes. Only in situations where different stimuli, that do not mix well, compete for our attention we realize this mess. A typical example is the scenario at a cocktail party where one is listening to the voice of another person while filtering out the voices of all the other guests. Depending on the loudness in the room this can either be a simple or a hard task but somehow our brains are capable of separating the signal from the noise. While it is not understood how our brains do this separation there are several computational techniques out there that aim at splitting a signal into its fundamental components. One of these methods is termed I ndependent C omponent A nalysis ( ICA ) and here we will have a closer look on how this algorithm works and how to write it down in Python code. If you are more interested in the code than in the explanation you can also directly check out the Jupyter Notebook for this post on Github.

It has been a long standing task to create machines that can act and reason in a similar fashion as humans do. And while there has been lots of progress in artificial intelligence (AI) and machine learning in recent years some of the groundwork has already been laid out more than 60 years ago. These early concepts drew their inspiration from theoretical principles of how biological neural networks such as the human brain work.

Epilepsy is a form of brain disorder in which an excess of synchronous electrical brain activity leads to seizures which can range from having no outward symptom at all to jerking movements (tonic-clonic seizure) and loss of awareness (absence seizure). For some epilepsy patients surgical removal of the effected brain tissue can be an effective treatment. But before a surgery can be performed the diseased brain tissue needs to be precisely localized. To find this seizure focus, recording electrodes are inserted into the patients brain with which the neural activity can be monitored in real time. In a previous article we looked at how to process such electrophysiological data from a human epilepsy patient in order to extract spike events.

Biological neural networks such as the human brain consist of specialized cells called neurons. There are various types of neurons but all of them are based on the same concept. Signaling molecules…

In the first article of this series we looked at the general organisation of MRI and fMRI datasets. In the second article we moved on and investigated which parts of the brain were active during the fMRI scan by performing a correlation analysis between the data and an idealized response profile. The method worked quite well. We saw activity in the auditory cortex as expected during auditory stimulation but the map looked a bit noisy and we wanted to see if a general linear model (GLM) might give us better results.

In the previous article we covered the basics about the data structure and the differences between structural and functional MRI (fMRI). In this article we move on to the analysis of the fMRI data to answer the following question: What brain regions were active during the scan?

There is a growing interest in applying machine learning techniques on medical data. Brain scans from Magnetic Resonance Imaging experiments (MRI) have been a popular choice with the number of…