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It has been postulated that physiological measures can be a positive indicator of mental workload. One such measure is the electroencephalogram (EEG). It is well known that the EEG signal is easily affected by artifacts. One prominent source of artifacts is eye activity, including blinks and saccades. These contaminates coincide directly with EEG signals, making it difficult to obtain artifact-free data. This paper discusses a methodology that performs artifact separation at the data analysis stage. This technique was used to analyze data from a recent experiment. Workload was manipulated by varying the difficulty of the primary task while responding to mathematical communications on the secondary task. Our findings demonstrate the importance of distinguishing between statistical significances found in the EEG signal as caused by neuronal activity versus those caused by artifacts. The artifact separation approach facilitates this investigation.