  Accurate localization of brain regions responsible for language and cognitive
functions in Epilepsy patients should be carefully determined prior to surgery.
Electrocorticography (ECoG)-based Real Time Functional Mapping (RTFM) has been
shown to be a safer alternative to the electrical cortical stimulation mapping
(ESM), which is currently the clinical/gold standard. Conventional methods for
analyzing RTFM signals are based on statistical comparison of signal power at
certain frequency bands. Compared to gold standard (ESM), they have limited
accuracies when assessing channel responses.
  In this study, we address the accuracy limitation of the current RTFM signal
estimation methods by analyzing the full frequency spectrum of the signal and
replacing signal power estimation methods with machine learning algorithms,
specifically random forest (RF), as a proof of concept. We train RF with power
spectral density of the time-series RTFM signal in supervised learning
framework where ground truth labels are obtained from the ESM. Results obtained
from RTFM of six adult patients in a strictly controlled experimental setup
reveal the state of the art detection accuracy of $\approx 78\%$ for the
language comprehension task, an improvement of $23\%$ over the conventional
RTFM estimation method. To the best of our knowledge, this is the first study
exploring the use of machine learning approaches for determining RTFM signal
characteristics, and using the whole-frequency band for better region
localization. Our results demonstrate the feasibility of machine learning based
RTFM signal analysis method over the full spectrum to be a clinical routine in
the near future.
