Brainstorming about brainstorming techniques

I'm about to attend a hackathon in Helsinki where I study right now. The team has already brought together, but finding an idea to hack the hell out of is not that simple. That's why I started thinking how to make brainstorming easier, how can I help my brain in the process. Here I share two approaches that I came up with - I'm not saying I was the first to figure these out, though; I have no clue.

Montage brainstorming

For the hackathon, the team had to choose, which gadget(s) to use - if more, than which combination - to build a creative, promising product. There was 20 of them so this decision alone was far from trivial. We agreed to try out a neuro headband, but still, there are a again a million ways to hack further brain signals with the other devices. There, it came to my mind, what if I constitute a montage, images of all devices cut and pasted side by side in a single image to view. It's so much easier to make associations between devices - or more abstractly, between choices of a parameter of an idea - than keeping your mind updating over and over again of the choices available. You just look at the montage and let your brain connect the dots for you. This technique, of course, is only convenient when you have all the possible choices available and their number doesn't exceed, let's say, 30. For most parameters of a hackathon / startup idea, montage brainstorming is not applicable. Don't worry, the other brainstorming method I'm going to show is just for those difficult cases.

The actual montage of devices that I assembled. Lots of tools to hack!

Analogy recovery from the Wikipedia corpus - a natural language processing task

Natural language processing is a field of science aiming at teaching machines the hectic human language, whether it is spoken or written. On the lowest level of abstraction computers talk to each other in sequence of bits, which in chunks forms instructions. The instruction set is limited, as well as well-defined - not any like human language. We can express rather complex concepts by a single word, while a computer would struggle spending a great amount of its words (1 word usually consists of a few bytes) to tell the same stuff. Also, families of human languages are results of thousands years of evolution, thus often neglect logic as much as are built upon traditions. All in all, getting machines understand our most innate communication form is not an easy sport. In fact, it's difficult as hell. So complex that so far we could only rely on heuristics; not even close to a full blown neural net, which would imitate our language processing brain parts, functions.

Existence of an ideal time capsule

Let's say, you want to hide, or make inaccessible some data (e.g. video of yourself, a secret) till a given time - or in other words, send a message to the future. The problem is how would you prevent yourself, or in that matter, anybody else, to read that peace of data before the given time? How do you make absolutely sure that there's no way to open the time capsule prematurely?

You'd say let's put that secret inside a platinum chest, inside a platinum chest, inside a platinum chest ... with a self-destruction function, so if someone peeked into those boxes before intended, then the data would be wiped out of existence. Oh and do not forget to bury that matryoshka platinum chest deep down the crust of earth where platinum is at the limits of melting.

A modern time capsule feeding on solar energy.

Acquiring fear intensity from EEG

My bachelor's thesis project is about the measurement of fear using EEG signals, similarly to my previous lab work. In this case though I made the participants to play a horror game instead just inducing fear by the means of audio stimuli. Also, I attempted to obtain the intensity of the emotion not just whether it is present or not - in addition to EEG signals, self-assessment, heart rate (HR) and skin conductance (GSR) data were gathered to verify the intensity. If you are interested in the whole thing in detail you can read it here (or at the end of this post). Team of the Synetiq neuromarketing research company supervised my thesis. They helped me a lot in putting together the measurement layout, the preprocessing of HR, EEG, and GSR signals and in the analysis of results.

Course of measurement

The experiment consisted of six main parts: 1) first, baseline self-assessment, 2) open-, and closed-eye baseline measurements, 3) baseline gameplay, 4) second self-assessment, 5) fear inducing gameplay, and 6) the last self-assessment. In total, the experiment took about 30 to 40 minutes with the time spent on the placement of the measurement tools excluded.

Parts of the 30-minute measurement as of time – width of the boxes are proportional to the length of the corresponding events.