You can get started with FeatureFinder right away using the sample data provided below. These data were collected from a mock experiment that investigated the question: When we listen to music, how are our reactions affected? Perhaps music acts as a distraction and could increase our reaction time when we drive. On the other hand, certain music could facilitate concentration and relaxation, helping us to react to an unexpected event calmly and quickly.
OK, so you’ve downloaded the data… what next? A good first step would be to check out the tutorial videos or go through the tutorial-style documentation using the sample data files. Once you’re comfortable with the program, you can start investigating the research question by extracting features such as “DifferenceOfAverages” (make sure you apply the RMS50 filter!) and coding your own feature to look at reaction time.
Our mock experiment required participants to catch an object that falls unexpectedly, and they were asked to do so as fast and with as little effort as possible. Each file in the data set represents the electromyogram (EMG) signal collected from a participant’s forearm muscles as they caught the object. Participants didn’t know when they had to make the catch, but the files are setup so that the stimulus occurs two seconds into the file. Ten catches (i.e., trials) occurred while the participant listened to music of her choice, and ten catches were done in silence. Nine participants, two conditions (i.e., music, silence), and ten trials resulted in the 180 files of test data.
Data was sampled at 1000 Hz and all bad data (possibly resulting from a missed catch or poor electrode connection) were left in the data set. You’ll also see that some signals are significantly cleaner (i.e., lower baseline noise) than others.