## 4 Analysis of Outcome  
The first objective of this project is to assess changes in vestibular-ocular and -motor responses following the Concurrent VR HS-WST. An average VOR gain will be calculated from the left and right horizontal SCCs gain and upward and downward vertical SCCs for each participant. The EOG horizontal and vertical signals during the perturbation trials will be calculated to quantify eye movement amplitude (degrees). Data will be filtered using a Butterworth lowpass filter of second order and at 30 Hz cutoff frequency. Filtered data will be detrended and the displacement (deg) will be calculated for the horizontal and vertical signals. The displacement will be differentiated to velocity (deg/s). Averages of the three trials will be found from the ramp onset to the end of the 5 s trials and from ramp onset to the next 200 ms. Eye movement area (using principal component analysis), velocity, horizontal eye movement, and vertical eye movement standard deviation will be calculated. EMG data will be normalized by using the ratio of the particular trial data to the mean and standard deviation of the resting EMG, expressed as a percentage. The normalized signals will be full wave-rectified, low pass-filtered, and smoothed at 10 Hz using a Butterworth filter of the fourth order (MATLAB 2022, Mathworks Inc., Natick, MA,  
USA). The means of the filtered signals will be calculated to obtain the muscle activations (amplitude) of each condition of the mCTSIB trials. Similarly, the ramp EMG data will be normalized and filtered as stated above. The means of ramp EMG signals will be calculated from ramp onset to the end of the 5 s trial to obtain the muscle activations. Means of toes-up and toes-down will be calculated separately. In addition, EMG amplitudes will be obtained from ramp onset to the next 200 ms. The time onset response with corresponding time onset amplitude, as well as peak amplitude with corresponding peak time, will be extracted using MATLAB's \"findpeaks\" function. Where the locations are not correctly identified by MATLAB, visual inspection with the aid of MATLAB data tip tool will be used to identify them. The COP of the ramp trials from onset to 200 ms and from onset to the end of the 5 s trial will be obtained and transformed into four COP sway variables: COP sway area (using principal component analysis), standard deviation of sway velocity (using path length divided by total time [velocity SD]), standard deviation of mediolateral (ML SD), and anteroposterior (AP SD) sways. Means of toes-up and toes-down will be calculated separately. In addition, the sway velocity of the peak-up and peak-down of the COP postural trajectory will be calculated to obtain the automatic postural response (APR). Repeated measures analysis of variance (RM ANOVA) will be used to analyze VOR gains, eye movement variables during toes-up and -down rotations, and muscle activation signals of the mCTSIB. RM ANOVA will be used to analyze the EMGs of individual trials and average trials for toes-up and -down rotations. RM ANOVA will be used to analyze COP sway measures and the ARP for toes-up and -down trials. Bonferroni post hoc adjustments will be used to adjust for multiple between-group comparisons. Simple frequency analysis will be used to compare the frequency of falls, number of targets reached per exercise, and self-reported VAS dizziness, headaches, and nausea. The second objective is to assess changes in sensory reweighting of postural control processing following the Concurrent VR HS-WST. The total time in seconds of each mCTSIB condition will be calculated for each participant. To analyze the postural changes in different metric dimensions, the COP time series data of the mCTSIB will be transformed into COP sway area, velocity SD, ML SD, AP SD sways, and multiscale entropy (MSE) sway velocity (m = 2; r = 0.2, tau = 10) using custom MATLAB scripts performed offline. Data will be filtered at 30 Hz before the calculations.