Kauppi et al: Face Prediction from fMRI Data during Movie Stimulus: Strategies for Feature Selection

The topic of the poster was to predict from a person's fMRI (functional magnetic resonance imaging) data whether he's seeing a face or faces in a movie or not. In an fMRI test setup, a stimulus, in this case the movie Crash, is presented for the test subject. The test subject's brain activity is measured, resulting in high-dimensional brain activity data that contains complex interactions. In the data, the brain is divided into voxels, i.e. cubes or 3D-pixels.

Similar research had been done before, but there the test subjects were shown a set of movie clips, instead of a whole movie. The authors claim that showing a whole movie results in "more naturalistic" data.

The problem is a classification task with two classes: "face" and "non-face". It was solved using ordinary least squares (OLS) for regression. However, since there was a lot of data, OLS couldn't be used in the conventional way. The prediction was done using only a subset of the features, which were selected using prior information and different methods, resulting in four regression models:

• Stepwise Regression (SWR)
• Simulated Annealing (SA)
• Least Absolute Shrinkage and Selection Operator (LASSO)
• Least Angle Regression (LARS)

Out of which LASSO and LARS are regulated to be sparse, possibly resulting in less overfitting.

Figure: The best prediction acquired with LARS compared to the (roughly binary) annotation. As can be seen, binary prediction (1 when > 0.5) would match the annotation well. Also locations of 6 features in three bain regions visualized.

Human brain is divided into different regions with different tasks. This study provided a natural way (at least for a computer scientist) to find out which regions are associated with face recognition, and thus, can be used in the prediction. In their paper, the authors say, "our results support the view that face detection is distributed across the visual cortex, albeit the fusiform cortex has a strong influence on face detection."

Riitta Hari: Towards Two-Person Neuroscience

Prof. Riitta Hari kicked off ICANN'11 with her invited talk "Towards Two-Person Neuroscience". So far the research on human brain has mostly focused on the study of a single brain. Humans, however, are social creatures, whose thoughts and actions are reflected by the other members in the community. In virtually any human culture, isolation is used as a punishment, not only for children but also for adults.


We all know that the interaction with other people affects our mood and thoughts very strongly. While an individual is interacting with another person, the brains of the two persons become coupled as one's brain analyzes the behavior of the other and vice versa. This is why the neuroscience community is now looking towards a pair instead of an individual as a proper unit of analysis.



There have already been studies on humans under controlled interaction, such as a movie or a computer game. While watching a movie, brains of individual viewers have been shown to be activated in a very synchronous fashion. Game against a human opponent activates the brain differently from a game against computer, which is also reflected in the reported feelings of the players.

Mirroring is a phenomenon which has been possible to study with existing technology. We feel pain when we are shown a picture of a suffering person. Already Ludwig Wittgenstein noted that "The human body is the best picture of the human soul". How individual's feelings tune into other person's feelings, is a more complicated question. It is a combination of the following factors:

• similar senses, motor systems and the brain that the individuals have


• the experience that they collect throughout their lives, and


• the beliefs they test by acting in the community.

Machine learning steps in for the analysis of the high-dimensional data produced by the functional measurement technologies. Dimensionality reduction methods such as independent component analysis (ICA) extract noise-free components that can potentially be biologically interpreted.

So far in most of the studies of human interaction, only the activity of one brain has been measured regardless of the presence of the other interacting person. Soon, however, accurate measurements of several subjects at a time will be possible, and that will most likely push for a leap in the development of computational data fusion techniques. Then, we will not only have a link between a stimulus and a brain image but between a stimulus and images of several subjects' brains.

When the focus of brain research moves towards the analysis of two or more interacting subjects, efficient multi-view methods will be needed. Thus, multi-view learning is currently a hot area of machine learning research.

Prof. Hari's message to the ICANN audience was that the analysis remains the bottleneck in brain research. As methodological researchers, we should next consider the opportunities opened by the new experiment settings and measurement technologies, and see how to learn more from the data.