Electroencephalogram (EEG) signals are widely employed in stress recognition research due to their ability to capture subtle neural variations associated with mental states. However, EEG signals are often corrupted by various artifacts and noise sources, including ocular, muscular, and environmental interferences, making preprocessing a critical step. This paper presents a comparative evaluation of classical preprocessing approaches (such as filtering and conventional wavelet denoising) against the Adaptive Wavelet Transform (AWT) framework for EEG denoising. The study investigates the effectiveness of these methods based on Root Mean Square Error (RMSE), and preservation of signal energy, focusing on their role in enhancing the discriminability of stress-related EEG patterns. Experimental results on benchmark EEG datasets demonstrate that AWT provides the average MSE value of 0.75e−07, as compared to 2.9e−5 and 3.55e−6 for band-pass filter and Wavelet transform, respectively. Similarly, average retained energy in de-noised signal using AWT is 96.66%, which is higher as compared to energy retained using band-pass filter and wavelet transform for which it is 85.82%, 91.06%, respectively. It proves the superior adaptability in handling non-stationary noise and improves the quality of preprocessed signals, in stress-related EEG analysis.