Electroencephalography (EEG) is a reliable method for identifying the onset of sleepiness behind the wheel. Using EEG technology for driving fatigue detection still presents challenges in extracting informative elements from noisy EEG signals. Due to their extensive computational parallelism, which is similar to how the brain processes information, neural networks have been explored as potential solutions for extracting relevant information from EEG data. The existing machine learning frameworks suffer from high computing costs and slow convergence, both of which contribute to low classification accuracy and efficiency due to the large number of hyper parameters that need to be improved. It is necessary to automate this micronap detection process before it can be used in real-time scenarios. To distinguish between micronap and non-micronap states, a deep neural network (DNN) framework is developed in this research using different EEG representations as input. Additional EEG representations utilized in this investigation include cleaned EEG as a time series, log-power spectrum, 2D-spatial map of log-power spectrum, and raw EEG. Finally, traditional machine learning algorithms are evaluated for their effectiveness in detecting micronaps from these EEG inputs. The findings suggest that micronap detection can be greatly improved by combining cleaned EEG with DNN.