  The impact parameter characterizes the centrality in nucleus-nucleus
collision geometry. The determination of impact parameters in real experiments
is usually based on the reconstructed particle attributes or the derived
event-level observables. For the scheduled Cooler-storage-ring External-target
Experiment (CEE), the low beam energy reduces correlation between the impact
parameter and charged particle multiplicity, which decreases the validity of
the explicit determination methods. This work investigates a few neural
network-based models that directly take the digitized signals from the external
Time-of-flight detectors as input. The model with the best performance shows a
mean absolute error of 0.479 fm with simulated U-U collisions at 0.5 AGeV. The
performances of the models implemented with digi inputs are compared with
reference models with phase space inputs, showing the capability of neural
networks to handle the original but potentially interrelated digitized signal
information.
