Abstract
Nowadays, depression has become such a widespread illness. It has been affecting people’s health which can lead to suicide. Some studies relate depression with nervous system and some of them relate stress with the reduced brain activity within the left frontal lobe. Hence, an early depression detection system is an initiative to detect early depression. In this research, the detection of early depression was done using the EEG signal because the EEG signal is taken from our brain which has the cognitive ability. The cognitive skill is the best to explain and influence our emotions which results to certain actions and reactions. EEG is also widely used because it is non- invasive method as it does not require the skull to be punctured or anything to be inserted into the brain. To achieve the desired results, few steps were done which are data acquisition, pre-processing, feature extraction and classification. The results obtained show that there is a decrease in alpha waves and increase in beta waves of depressed patients. The accuracy rates for alpha and beta waves are comparable with the previous literatures.
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Ladeheng, H., Sidek, K.A. (2022). Early Depression Detection Using Electroencephalogram Signal. In: Liatsis, P., Hussain, A., Mostafa, S.A., Al-Jumeily, D. (eds) Emerging Technology Trends in Internet of Things and Computing. TIOTC 2021. Communications in Computer and Information Science, vol 1548. Springer, Cham. https://doi.org/10.1007/978-3-030-97255-4_9
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DOI: https://doi.org/10.1007/978-3-030-97255-4_9
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