Hi Peter, you are dealing with biological time series data that have some properties to be acount when you try to classify and consideration about these properties are important Because you need to be a preprocessing if you have a signal is non-stationary/stationary, or if the signals that are you look for can be periodic or not, or if the signals are considered linear/nonlinear, or if you are look for low frequencies or high frequencies.
Then after that, you can consider a popular method for separation of sources to know which sources are meassured then Independen Component Analysis (where you consider the non-gaussianity of the sources signals)can be a good option or a varition of it. Cheer Carlos A. Estombelo Montesco 2007/3/30, Peter Flom <[EMAIL PROTECTED]>:
Thanks to those who already replied! Their replies make me realize I needed to post a few more details. Here's the problem: I will be dealing with EEG data (brain waves). There will be, for each subject, 19 time series, each with thousands of points (sampling is once per milisecond). We have some old data, which are already classified, but those classes aren't absolutely certain to be correct. There may be some errors, and there may be classes that have not been identified in the literature. We will be getting new data, where the classes will be unknown. We want to look at new data and classify them; we also want to look at all the data (old and new) for anything previous researchers have missed. Any further suggestions will be very welcome Thanks Peter ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l
-- +--------------------------------------------------------------------------------------+ Carlos Alberto Estombelo Montesco PhD. Student in Physics Applied to Medicine and Biology ....................................................................................... University of Sao Paulo Department of Physics and Mathematics School of Philosophy, Sciences and Letters of Ribeirão Preto Av. Bandeirantes, 3900 CEP: 14040-901 Ribeirão Preto, SP, Brazil fax : +55 16 3602 4887 email: [EMAIL PROTECTED] +--------------------------------------------------------------------------------------+ ---------------------------------------------- CLASS-L list. Instructions: http://www.classification-society.org/csna/lists.html#class-l
