Ergy from the EEG signal on the 25 test segments segments corresponding for the channels was extracted, and also the power of the information segment corresponding towards the channels was extracted, and the energy with the data segment in the within the frequency band was represented by the logarithm from the sum with the squares of all information points inside the frequency band using a base of 10, that is shown in Equation (1).E(k) = lgi =1 x ( k ) in,(1)Sensors 2021, 21,six ofwhere k represents the amount of Orexin A MedChemExpress trials within the information segment (k = 1 within this experiment), n represents the amount of information points in each segment, and x (k)i represents the worth in the ith point within the kth data segment [31]. For the VEP, the same function channels because the EEG statistical analysis have been chosen for statistical analysis (occipital region: POz, PO3, PO4, Oz, PO7, PO8). The information were filtered offline having a band-pass filter of 0.ten Hz, using the scene before the edit point as the baseline for each trial and choosing the initial second of content immediately after the begin with the clip for analysis. VEP was calculated by averaging more than trials and participants. Based on preceding literature detecting time windows of interest [28], four time windows were selected to analyze ERP maxima on the scalp surface: Time window 1 = 14090 ms following stimulus onset, Time window two = 18020 ms just after stimulus onset, Time window 3 = 25080 ms following stimulus onset, and Time window 4 = 40050 ms right after stimulus onset. two.five. Verification of Differences in VR Editing Strategies Based on SVM To be able to additional discover essentially the most suitable frequency band for the classification of viewing load under the neural mechanism of the human brain, this paper adopts a support vector machine learning system to establish an SVM classification model primarily based on EEG energy feature parameters to train and recognize the power induced by films with various VR editing methods for classification. Currently, the mainstream EEG classification solutions contain linear classifiers, which include assistance vector machines [36] and neural networks [37], among which SVM is the most broadly utilized and effective classifier [9]. While SVMs are binary classifiers, they are able to be employed in multi-class problems by using a one-vs-one or one-vs-all tactic. As opposed to neural networks, SVMs would not demand a big number of instruction samples to solve the classification challenge well. For linearly indistinguishable data, SVM can map to a high-dimensional feature space and find the optimal hyperplane within this space. 3. Results Within this paper, one-way repeated measures analysis of variance (ANOVA) was utilised for subjective and objective information, and basic effects evaluation was FCCP In Vivo performed if interactions between elements had been identified. All analyses have been performed with p 0.05 as the significance level measure, plus the Greenhouse-Geisser approach was applied to right degrees of freedom and p values. All statistical analyses had been performed applying SPSS 22.0 (IBM, Armonk, NY, USA). 3.1. Subjective Information The NASA-TLX table evaluates the experimenter’s perceived load in six dimensions: Mental Demand (MD), Physical Demand (PD), Temporal Demand (TD), Effort (E), Overall performance (P), and Frustration Level (FL). The IPQ table consists of a three-factor structure of spatial presence (SP), involvement (INV), and reality (Genuine). The degree of load and immersion is expressed as the amount of the scale score. For the subjective data, the questionnaire final results of all volunteers are averaged and analyzed, and also the statistical benefits are.