MEG adaptive noise suppression
At the computational sensorimotor system lab (CSSL), I worked with Dr. Jonathan Simon, analyzing magneto encephalography (MEG) signals (Adaptive filtering, Mathematical inferences). I developed a small toolbox for filtering out MEG environmental and biological noise tweaking the Fast-LMS algorithm.
Abstract: Magnetoencephalography (MEG) measures magnetic fields generated by electric currents in the brain, non-invasively and with millisecond temporal resolution. Typical signals are 10-13 T, so noise contamination due to external magnetic fields is a serious concern. Digital signal processing is typically required in addition to magnetic shielding. Using three reference channels, displaced from the head, to measure the noise, we apply adaptive filtering to subtract out estimates of the noise, via the block least-mean-square ("fast LMS") method. The algorithm is tested by its effects on the number and distribution of channels which have statistically significant signals (distinguishable from background noise at a specified false-positive rate). We show that fast LMS both increases the number significant channels and reduces the variance of false positives
Reference
Ahmar, N. E., & Simon, J. Z. (2005, March). MEG adaptive noise suppression using fast LMS. In Neural Engineering, 2005. Conference Proceedings. 2nd International IEEE EMBS Conference on (pp. 29-32). IEEE.