Depression is the most common mental illness, which has become the major cause of fear and suicidal mortality or tendencies. Currently, about 10% of the world population has been suffering from depression. The classical approach for detecting depression relies on the clinical questionnaire, which depends on the patients' responses as well as observing their behavioral activities. However, there is no established method to detect depression from EEG biomarkers. Therefore, exploration of EEG biomarkers for depression assessments is vital and has a great potential to improve our understanding and clinical interventions. In this study, we have conducted a systematic review of 52 research articles using the PRISMA-P systematic review protocol, where we analyzed their research methodologies and outcomes. We categorized the experimentations in these articles according to their physical and psychological aspects scaled by the commonly used clinical questionnaire-based assessments. This study finds that the negative stimuli are the better identification strategies for evaluating depression through EEG signals. From this exploration, researchers observed that the Neural Connectivity Analysis and Brain Topological Mapping have huge potentials for finding depression biomarkers, and it is evident that the right-side hemisphere and frontal and parietal-occipital cortex are distinct regions to detect depression using EEG signals. For this mechanism, researchers are using many signal processing and machine learning approaches. In the case of filtering, Independent Component Analysis (ICA) is commonly used to eliminate physiological and non-physiological artifacts. Among machine learning approaches, Convolutional Neural Network (CNN) and Support Vector Machine (SVM) showed better performance for classifying healthy and depressed brains. The authors hope, this study will create an opportunity to explore more in the future for EEG as diagnostic tools by analyzing brain functional connectivity for focusing on clinical interventions.
<p>Paleolakes on Mars have been proposed to be hydrologically active for thousands of years. They provide water, the prime ingredient for life to develop, and quiescent settings, making these lakes excellent targets in preserving biosignatures. Since ground truth analysis on Mars is limited to certain locations, most of the interpretations about Martian geology and past climate have been made through remote sensing. This study presents a comprehensive account of the physical and chemical aspects of an Earth-based hypersaline playa that has undergone intermittent wet and dry periods.</p><p>Sambhar Lake is the largest endorheic playa in India, situated southeast of the Aravalli mountains within the Thar Desert. The lake formed as a result of neotectonic and aeolian activity followed by stream capture like some paleolakes and hydrologically active inter-crater depressions on Mars. Sambhar Lake lies between arid and semi-arid transitional zones and is fed by two ephemeral streams indicating climate-driven hydrology. The surface and sub-surface brine samples collected from the lake were alkaline, Na-Cl type with salinity higher than the seawater. Silicate weathering and evaporation were identified as important processes responsible for influencing the hydro-geochemistry of the lake. Petrographic and geochemical analysis of the sediment and rock samples showed the presence of clay minerals and evaporites ranging from carbonates to halites suggesting that the lake had witnessed multiple hydrological cycles. The weathering index of the dried lake bed was comparable to some Gale crater samples and lakes with basaltic origin on Earth. The geochemical evolution of the Sambhar Lake is primarily governed by the inlet streams and their composition, partition of solutes in the water, and concentration of the evaporites. Thus, Sambhar Lake is a classic example of the climate-induced transition of a lacustrine basin to a playa. It may be helpful to study the evolution of hydrological basins, their morphology, and the process of mineral formation on Mars.</p>
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