Relationship between Total Expenditure on Cosmetic Procedures in the United States and NASDAQ 100 Index.

The volume of cosmetic procedures and expenditures has increased over the past 15 years. Recent studies have shown that the market for cosmetic procedures follows the standard laws of economics. However, no studies published in the literature have found a direct correlation between US stock market indices and expenditures on cosmetic surgery and minimally invasive procedures. Methods: The authors analyzed annual cosmetic procedure statistics from the American Society of Plastic Surgeons for 2005-2020 against economic indices, including major US stock market indices [NASDAQ 100, S&P 500, Dow Jones Industrial Average (DJIA), Russell 2000 indices from the New York Stock Exchange, gross domestic product (GDP)], median income of US citizens, and the US population from the Federal Reserve Bank of St. Louis. Pearson correlation coefficient and multiple regression analysis were used for the statistical analysis. Results: Total expenditure on cosmetic surgery and minimally invasive procedures (TECP) has more than doubled since 2005 to 2020. TECP revealed statistically significant correlations with all other indicators. TECP had the greatest correlation with DJIA (r = 0.952, P < 0.001). In the multiple regression analysis, the increase in TECP led to the rise of the NASDAQ 100 index (adjusted R2 was 0.790, P < 0.001). Conclusions: There was a statistically significant correlation between the TECP in USA and the major indices of the US stock market. In particular, the increase in TECP led to the rise of the NASDAQ 100 index.

Novel Hybrid Brain-Computer Interface for Virtual Reality Applications Using Steady-State Visual-Evoked Potential-Based Brain-Computer Interface and Electrooculogram-Based Eye Tracking for Increased Information Transfer Rate.

Brain-computer interfaces (BCIs) based on electroencephalogram (EEG) have recently attracted increasing attention in virtual reality (VR) applications as a promising tool for controlling virtual objects or generating commands in a "hands-free" manner. Video-oculography (VOG) has been frequently used as a tool to improve BCI performance by identifying the gaze location on the screen, however, current VOG devices are generally too expensive to be embedded in practical low-cost VR head-mounted display (HMD) systems. In this study, we proposed a novel calibration-free hybrid BCI system combining steady-state visual-evoked potential (SSVEP)-based BCI and electrooculogram (EOG)-based eye tracking to increase the information transfer rate (ITR) of a nine-target SSVEP-based BCI in VR environment. Experiments were repeated on three different frequency configurations of pattern-reversal checkerboard stimuli arranged in a 3 × 3 matrix. When a user was staring at one of the nine visual stimuli, the column containing the target stimulus was first identified based on the user's horizontal eye movement direction (left, middle, or right) classified using horizontal EOG recorded from a pair of electrodes that can be readily incorporated with any existing VR-HMD systems. Note that the EOG can be recorded using the same amplifier for recording SSVEP, unlike the VOG system. Then, the target visual stimulus was identified among the three visual stimuli vertically arranged in the selected column using the extension of multivariate synchronization index (EMSI) algorithm, one of the widely used SSVEP detection algorithms. In our experiments with 20 participants wearing a commercial VR-HMD system, it was shown that both the accuracy and ITR of the proposed hybrid BCI were significantly increased compared to those of the traditional SSVEP-based BCI in VR environment.

Brain-Controlled, AR-based Home Automation System using SSVEP-based Brain-Computer Interface and EOG-based Eye Tracker: A Feasibility Study for the Elderly End User.

Over the past decades, brain-computer interfaces (BCIs) have been developed to provide individuals with an alternative communication channel toward external environment. Although the primary target users of BCI technologies include the disabled or the elderly, most newly developed BCI applications have been tested with young, healthy people. In the present study, we developed an online home appliance control system using a steady-state visual evoked potential (SSVEP)-based BCI with visual stimulation presented in an augmented reality (AR) environment and electrooculogram (EOG)-based eye tracker. The performance and usability of the system were evaluated for individuals aged over 65. The participants turned on the AR-based home automation system using an eye-blink-based switch, and selected devices to control with three different methods depending on the user's preference. In the online experiment, all 13 participants successfully completed the designated tasks to control five home appliances using the proposed system, and the system usability scale exceeded 70. Furthermore, the BCI performance of the proposed online home appliance control system surpassed the best results of previously reported BCI systems for the elderly.