For my bachelor’s-thesis I examine the electric activity of human forearm muscles (especcially M. palmaris longus). I have been able to record those signals with Audacity using Surface Electromyography.
(After reading the manual) I now wonder if there are ways to further analyse the recorded material.
For example applying a Root Mean Square-Algorithm and intergrating over a certain selection or displaying the material with Excel in order to compare different tracks not just visually.
I’d be grateful for every hint! Or maybe someone knows a differnet software I could use.
Thank you anyways,
P.S.: If you are German you can also reply in German. I am from Germany, too ; )
Answer from Karlsruhe/Germany (but in English, so all others can read it, too):
There is a programming language built into Audacity, called “Nyquist”, see Nyquist Audio Programming in the Audacity Wiki where to find the manuals. There is also a Nyquist section here in the forum where we can help to write code to analyze Electromyography signals.
Electromyography signals can be computed like audio signals, so computing the RMS or integrating the signal is no big problem, Nyquist can also write plain text files, so the data can e.g. be written into a CSV file (comma separated values) that can be read by Excel, Matlab/Octave or Open Office Calc.
It’s best if you ask a specific question in the Nyquist section here in the forum so we can write some example code and you can test if it works as you need it. You do not need any other software than Audacity (what you already have) for this.
As Edgar wrote, there are a lot of possibilities using Nyquist.
Another program that may be of interest is Sonic Visualiser.
first of all, thank you for your quick reply .
Should I open up a new topic for my question?
Since this has not much to do with “Adding New Features to Audacity”, the best would be if you open a new topic in the Nyquist section here in the forum and give a bit of description how your signals look like. For example do the signals contain a DC component (vertical offset), and/or is it important to take the DC component into account in the calculations (I assume yes). There often are a myriad of details to observe in digital signal processing.
Learning Nyquist is not done within one week and your bachelor’s-thesis is probably about forearm muscles and not about learning a programming language, so my original idea was that we will write some example code and give you links to the places in the Nyquist manual, so you can modify the code yourself to your specific needs. But all this would fit much better under the Nyquist section, where more people (who can help with Nyquist) will find it.