It is almost November: National Novel Writing Month aka NaNoWriMo. The goal of this event is to write 50,000 words (about 175 pages) in one month, so it is really a strong push to get yourself to write. This could perhaps be a nice way to kickstart my PhD thesis! I am only halfway, so most of my research is not done, but I could already write everything around it (general introduction, background, motivation, related work, methods), and add some fictional results to get it going ;) This could really help in getting a clear view of the final product, where I want to go, what I still need to do.
I love the Pomodoro Technique and the Chromodoro Chrome extension that supports it. But I don’t like the beep sound that Chromodoro uses to notify me. Fortunately there is a simple solution, that I’d like to share. How to change the Chromodoro beep sound:
- Find chromodoro.js (it should be located in a subdirectory of the User Data for Chrome), and open it in a text editor like notepad.
- Find the following piece: beep.swf
and replace it by: beep.wav
- Now put your own sound in the same directory as chromodoro.js, and name it beep.wav.
Note: if your sound is not a wav file, then replace .wav above with the correct file type.
A bit off topic for braingaming, but on topic for people who have to manage their own time, like PhD students :)
We have been invited to demonstrate AlphaWoW and Mind the Sheep at the Researchers’ Night in Nijmegen this Friday. There will be interesting talks about BCI, neurofeedback, and brain research, and of course many cool demos. Entrance fee is 7,50.
As a scientist, significance is very important. Is using P300 more pleasant than using SSVEP? Does this analysis pipeline yield a higher detection accuracy than the other? Is for this mental task the brain activity different from general baseline activity?
Generally, we compute the chance p that the second set of samples is from the same population as the first. If p is small, we reject this hypothesis (Of course there is still a chance of p that we are wrong in this). As a convention, if p is smaller than 0.05 we say the difference is statistically significant. If p is smaller than 0.10, you can speak of a trend, which generally means that you should try to do more tests. Because with more samples it is easier to get a significant difference.
There is a nice article on measuringusability.com about this convention of needing a p < 0.05 for significance, which argues not to blindly follow this convention, but look at what your results really mean. Continue reading
Every year there is an event called eNTERFACE. It is announced as a summer workshop, but it is a bit more. About teams work on different multimodal human-computer interaction projects for 4 weeks, and there are some tutorials and lectures. You can submit a project proposal yourself if you like, or you can join the team for the project of somebody else.
Last year Christian Muehl from our group was team leader for a project which resulted in the game Bacteria Hunt, in which you play an amoeba controlled by keyboard, SSVEP, and alpha activity. The goal is to eat as many bacteria as you can.
Our position paper has been accepted for the Brain Body Bytes workshop at CHI2010. All the workshop papers have just been posted online (here), and it looks really interesting. They are available online for free, so do check them out. Can’t wait to go there!
For the last couple of months, I’ve been looking into EEG analysis and classification for SSVEP detection, for a journal article we have been working on, based on our Bacteria Hunt project at eNTERFACE’09. The SSVEP response is a very direct reaction of the brain to a flickering stimulus. Say you have an image that can be on (white) or off (black), and you alternate them at a frequency of 7.5 Hz (7.5 alternations per second), then we can see a peak at 7.5Hz in the frequency spectrum in the occipital lobe.
One of the methods that is often used in SSVEP detection methods, is to do zero-padding before applying a Fourier transform to look at the frequency spectrum. The idea is to add data points, so you can have smaller frequency bins in the spectrum. The SSVEP response is very narrow-band, so a small frequency bin would be perfect. If you have a 1-second window of 512 samples, recorded at 512 Hz sample frequency, then the bin size is 1 Hz. If you add zeros to increase your window to 1024 samples, you have a bin size of 512 Hz/1024 samples = 0.5 Hz.
But there is one important thing to understand: zero-padding does not provide additional information — it only interpolates from the data you already have. So if you want a spectrum with smaller bins, it is better to just use a larger EEG window. And often there is no reason not to do so. If you have a setup which should provide real-time feedback, make the windows larger (you can keep the inter-window interval the same, so feedback is given at the same intervals as before). If you want to do offline analysis, be sure that during your experiment the stimulus duration was long enough, and then just take the full window size you need to get the appropriate bins. Be aware that it takes some time for the SSVEP response to be elicited as well.
There is a site that explains the zero-padding issue really nicely: blinkdagger – FFT and zero-padding — definitely a recommended read!