Fast Fourier Transform

Due to the outbreak of COVID-19 pandemic, there exists a situation of worldwide/nationwide lockdown across different countries which results in the growth of new-age technologies including augmented reality (AR) and virtual reality (VR) and their demands. Lockdown situation and social distancing had given sufficient time to develop innovative ideas by using AR/VR technologies. Many global seminars or conferences had been cancelled to practice social distancing and adopt the AR/VR technologies. The consequences of COVID-19 pandemic such as uncertain prognoses and shortages of resources had led to an increase in the depression level among people. This chapter demonstrates the applications of AR/VR and its proposed methodology to integrate the benefits of VR to the standardized mental health treatment process of mental health issues such as depression. The need for effective diagnosis and treatment of mental health combined with the recent advancement of artificial intelligence technologies had led to an increase in explorations in this direction. Deep learning has potentially discovered new learning patterns on the human-machine interface to identify risk factors of mental illness as well as to give optimized therapies.

AR and VR and AI allied technologies and depression detection and control mechanism

Head-worn wearables, such as consumer-grade EEG headsets deployed in Brain Computer Interfaces (BCI), are getting popularity in the gaming and entertainment industry, and for people with certain disabilities. However, the increasing popularity of these wearables creates a significant privacy risk. For instance, tech companies are intending to use brainwave devices to detect workers' emotional state and mental condition. There are AI techniques that can learn what people are looking at in real-time. Silently conversing with the computing system is now possible using neuromuscular signals, for instance, untold digit recognition with higher accuracy is possible, which can retrieve untold PIN or password. These applications can reveal more private information than designated benign purpose, such as, while detecting performance of worker, sensitive information like Parkinson's disease, substance abuse disorder, heart disease, can be revealed from brainwave. The consequences of these privacy leakages may be potentially devastating, such as tracking users for targeted advertisements and launching targeted attacks against users. In this paper, we analyze current devices, explore previously studied attacks, research efforts to extract information from brainwave and analyze and synthesize potential future attacks from the current deployment. This systematization will provide right direction towards ensuring privacy risk of BCI devices, which is a pre-requisite to building future defense mechanisms against the attacks.

SoK: Your Mind Tells a Lot About You: On the Privacy Leakage via Brainwave Devices

Nowadays, consumers’ well-being plays a decisive role in the purchase of cosmetic products. Although factors influencing consumers' well-being are very subjective, companies strive to develop their products in such a way that a positive effect is likely. Therefore, methods are required to objectively explore and scientifically prove the product’s performance on humans. In this placebo-controlled study, a method was developed to evaluate relaxation or stress relief associated with one olfactory ingredient of a cosmetic product (face cream). Our experimental protocol included product testing in 25 healthy females, while an emotion questionnaire, analysis of saliva samples regarding the concentration of the hormones cortisol and α-amylase and mobile EEG measurement for quantification of the alpha brain waves before and after stress induction were conducted. It was shown that with this experimental design, the sample with the ingredient produced significant stress relief, as evidenced by significantly less negative emotion, significantly lowered cortisol levels and showed a trend towards a significant increase in alpha activity compared to placebo application. Our data provide evidence that this method is suitable for analyzing the differences between the two samples. In the future, this method can be utilized in the current or a further optimized form to evaluate the psychophysiological effects of cosmetic products on humans.

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Measurement of Stress Relief during Scented Cosmetic Product Application Using a Mood Questionnaire, Stress Hormone Levels and Brain Activation

............................................................................................................................. ii Acknowledgments ............................................................................................................ iv Table of

/pdf/decoding-passengers-brain-signals-to-detect-and-analyze-286l6mfx.pdf

Decoding Passenger’s Brain Signals to Detect and Analyze Emergency Road Events

The desire to end someone's life commonly triggered by depression or mental health. One way to detect whether someone is depressed is by checking whether the person is constantly stressed for a long time. Electroencephalograph (EEG) is one of physiological signal that can be used for monitoring someone's mental condition, such as stress. The proposed and developed Stress Detection and Meditation Application is an application that can be used for meditating and reducing stress level by using EEG to get brainwave signals. In this developed system, Fast Fourier Transform (FFT) was used as the feature extraction and k-Nearest Neighbor (k-NN) was used to classify the features and detect whether the person is stressed or not. The Delta, theta, alpha, and beta waves as the features were the most suitable feature type for the stress detection and meditation application, while the value of $k = 3$ in k-NN classification provides the best $k$ value with 80% accuracy.

Alya Fernandya

Papers

Real Time EEG-based Stress Detection and Meditation Application with K-Nearest Neighbor