Electronic noses emulate biological olfaction by combining cross-reactive sensor arrays with pattern recognition algorithms. A critical challenge in conventional enose systems operated under static conditions is the limited diversity of extracted features, which constrains discrimination performance for complex or mixed odors. To address this issue, many gas sensing signal modulation strategies have been proposed to actively perturb sensor responses and enrich the information content of acquired signals. In this work, we present a brief overview of state-of-the-art signal modulation techniques employed in e-nose systems, including sensing element materials modulation, gas concentration and flow-rate modulation, light illumination modulation, and temperature modulation. The underlying physical and chemical mechanisms, advantages and inherent limitations of each strategy are discussed. By comparing these techniques, we aim to offer practical guidance for the rational selection and integration of modulation strategies tailored to applicationspecific constraints, thereby supporting the development of next-generation intelligent olfactory sensing platforms.