This research introduces a novel approach to enhance the empirical mode breakdown (EMD) technique for the analysis of electroencephalogram (EEG) signals in the context of depression detection. By incorporating [specific modifications, enhancements, or methodologies], the proposed improved EMD method aims to address [specific challenges or limitations] associated with traditional EMD. The efficacy of the suggested methodology is evaluated using a dataset of EEG signals from individuals diagnosed with depression. Comparative analyses against standard EMD and other existing methods demonstrate [performance metrics or improvements], highlighting the potential of the enhanced EMD technique as a valuable tool for more accurate and reliable depression detection through EEG signal analysis. This study contributes to the ongoing pursuit of advanced signal processing methodologies for improved mental health diagnostics. Non-linear and non-stationary data are the main types of data that are analyzed using the Empirical Mode Decomposition (EMD) technique. The process breaks down a signal into a collection of oscillating parts known as intrinsic mode functions (IMFs). Each IMF represents a local characteristic timescale present in the original signal. EMD has applications in various fields such as signal processing, time series analysis, and biomedical engineering. It's particularly useful for analyzing complex signals with multiple underlying components. Each Intrinsic Mode Function (IMF) is a component of the signal that oscillates around zero and represents a specific frequency or mode present in the original signal.