Due to technological limitations inherent in EEG, the attention
monitoring component is prone to be affected by other signals
such as muscle artifacts. Although the monitoring system
employed filters to remove such artifacts and the supervisor
used smoothing and averaging techniques to create long term
trends, which should alleviate problems due to short-term signal
variations caused by EMG, more investigation is necessary
to validate that the attention index indeed represents underlying
cognitive activity free from extraneous signals. Our
preliminary analysis reveals that EEG attention levels were a
marginal predictor for student recall abilities, however a means
of fully validating EEG levels for use in such contexts has yet
to be determined. The capabilities of our technology might
be increased by exploring other means of determining review
content instead of simply averaging attention across prede-
fined lesson subsections. Future technology might benefit
from more robust signal analysis methods including examining
EEG slopes, regression lines, or height and frequency of
local maxima and minima