<![CDATA[Depression can induce significant anguish and impair one's ability to perform effectively in professional, academic, and familial settings. This condition has the potential to result in suicide. Annually, the number of deaths resulting from suicide exceeds 700,000. Among individuals aged 15-29, suicide has emerged as the fourth most prevalent cause of mortality. Challenges in treating depression include limited accessibility to mental health care in rural regions and misdiagnosis resulting from subjective evaluations, wherein insufficient expertise can contribute to inaccurate diagnoses. Electroencephalography (EEG) has gained popularity as a tool for the identification and study of a number of mental illnesses in the past several years. Therefore, an automated technique is required to precisely distinguish between normal EEG signals and depression signals. This research focused on developing an EEG-based depression detection system in the prefrontal cortex lobe area (Fp1, Fpz, and Fp2). One of the developments carried out in this research is the implementation of Bidirectional Long Short-Term Memory (Bi-LSTM) as the model classification and minimum redundancy maximum relevance (mRMR) feature selection. Results suggest that the combination of mRMR feature selection with 25 features and the Bidirectional LSTM obtained 92.83% for the accuracy.]]>
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