Timeline for RLS adaptive filter intuitive explanation of the so-called desired signal
Current License: CC BY-SA 4.0
5 events
| when toggle format | what | by | license | comment | |
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| Jun 13 at 3:18 | comment | added | Baddioes | @AlanTuring look at section 10.1.4 of "Statistical and Adaptive Signal Processing" by Manolakis for active noise cancellation. For the paper you reference, as far as I can tell, it uses info from the piezo sensor as an input into a Kalman filter. That Kalman filter estimates the motion error, which is then subtracted via active noise cancellation from the EEG by the Kalman filter. | |
| Jun 12 at 18:18 | comment | added | AlanTuring | the above is a concrete example of the problem i m facing (and for the ECG it is still not clear: what is in clear terms the desired signal that we can subtract from the estimated signal?! ) | |
| Jun 12 at 18:16 | comment | added | AlanTuring | in this paper an adaptive (Kalman) filter is used to remove motion artifacts on eeg using motion sensor info (a piezo sensor) . I understand the idea that the sensor data is correlated to the induced artefacts on the eeg but we do not have the clean eeg obviously so how to compute a cost function?! they don't explain that! | |
| Jun 12 at 18:14 | comment | added | AlanTuring | thanks for trying to answer but this does not clarify my problem. For example please look at scholar.google.com/… | |
| Jun 12 at 17:32 | history | answered | Baddioes | CC BY-SA 4.0 |