Abstract: In this research, a novel method employing the fusion technique of data in and feature out for determining the wake to sleep transition and vice versa is described. Inputs of the process are the Electrooculogram (EOG) and Head Nodding (HN) signals. The method involves a direct fusion of two pairs of signals consisting of the combination of EOG and head nodding signals to generate three new sets of data in the form of Lissajous-like plots. These Lissajous-like plots are further analyzed to extract the feature vector to represent the two states of awake and asleep. In turn, the extracted feature set is used to determine the state of alertness and/or sleepiness. The effectiveness of the derived feature vector from the fusion approach is evaluated and compared against the non-fusion based method. Results show that the fusion based approach fared better than the non-fusion method. This has implications for the application of the developed method in determining driver drowsiness.