A Comprehensive Study on Mental Illness Through Speech and EEG Using Artificial Intelligence

Sanjana Bhat, Reeja S R
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Abstract

  A typical mental ailment is depression that considerably harms an individual's everyday activities as well as their mental health. In light of the fact that mental health is one of the biggest problems facing society, researchers have been looking into several strategies for efficiently identifying depression. Mental illness can now be identified through speech analysis thanks to modern artificial intelligence. The speech aids in classifying a patient's mental health status, which could benefit their new study. For the purpose of identifying depression or any other emotion or mood in an individual, a number of past studies based on machine learning and artificial intelligence are being studied. The study also examines the effectiveness of facial expression, photos, emotional chatbots, and texts in identifying a person's emotions. Naive-Bayes, Support Vector Machines (SVM), Linear Support Vectors, Logistic Regression, etc. are ML approaches from text processing. Artificial Neural Network (ANN) is a sort of artificial intelligence method used to extract information from photos and classify them in order to recognise emotions from facial expressions.
利用人工智能通过语音和脑电图全面研究精神疾病
抑郁症是一种典型的精神疾病,对个人的日常活动和心理健康造成极大伤害。鉴于心理健康是社会面临的最大问题之一,研究人员一直在研究几种有效识别抑郁症的策略。借助现代人工智能技术,现在可以通过语音分析来识别精神疾病。语音有助于对病人的精神健康状况进行分类,这将有利于他们的新研究。为了识别抑郁症或个人的任何其他情绪或心情,正在研究过去基于机器学习和人工智能的一些研究。这项研究还考察了面部表情、照片、情感聊天机器人和文本在识别人的情绪方面的有效性。Naive-Bayes、支持向量机(SVM)、线性支持向量、逻辑回归等都是来自文本处理的 ML 方法。人工神经网络(ANN)是一种人工智能方法,用于从照片中提取信息并进行分类,以便从面部表情中识别情绪。
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