  The planned Laser Interferometer Space Antenna (LISA) is expected to detect
gravitational wave signals from ~100 extreme-mass-ratio inspirals (EMRIs) of
stellar-mass compact objects into massive black holes. The long duration and
large parameter space of EMRI signals makes data analysis for these signals a
challenging problem. One approach to EMRI data analysis is to use
time-frequency methods. This consists of two steps: (i) searching for tracks
from EMRI sources in a time-frequency spectrogram, and (ii) extracting
parameter estimates from the tracks. In this paper we discuss the results of
applying these techniques to the latest round of the Mock LISA Data Challenge,
Round 1B. This analysis included three new techniques not used in previous
analyses: (i) a new Chirp-based Algorithm for Track Search for track detection;
(ii) estimation of the inclination of the source to the line of sight; (iii) a
Metropolis-Hastings Monte Carlo over the parameter space in order to find the
best fit to the tracks.
