Electroencephalogram (EEG) signals are of very low amplitude and are easily contaminated by different types of noises like environmental and of non-cerebral in nature. Thus signal pre-processing is a major challenge while dealing with applications involving EEG signals. The scenario becomes much more complex while using commercially available, low resolution devices as they have fewer electrodes. In this paper, we have applied some of the widely used signal processing techniques to get rid of eye blink and noise related artifacts from EEG signals recorded using a low cost wireless device from Emotiv. Investigations reveal that clustering based eye blink detection method and the skewness based noise detection method give the best detection accuracy. As an example use-case, we show how selective filtering of the EEG signals in the blink regions and removal of noisy windows can help in improving the discrimination power between the two types of color Stroop stimulus based cognitive load analysis. Thus with appropriate signal pre-processing techniques, these low resolution devices can be successfully used to differentiate between different levels of mental workload, which in turn makes these devices useful for non-medical Brain Computer Interface (BCI) applications requiring mass deployment.