Electroencephalogram-based high-speed elevator passenger comfort analysis method, system and device

Abstract

The invention provides a high-speed elevator passenger comfort level analysis method, system and device based on electroencephalogram. Acceleration data of an elevator car and electroencephalogram data of passengers are collected and stored at the same time; electroencephalogram data and acceleration data are intercepted according to the running state of the elevator; the data is preprocessed; on the basis of feature extraction of the data, an operation index and an electroencephalogram index are formed; and finally, by recording the indexes, an operation index curve and an electroencephalogram index curve are formed, a tool is provided for correlation analysis between elevator operation and passenger feeling, and high-speed elevator passenger comfort evaluation is more objective and accurate. According to the method, a new objective analysis tool is provided for high-speed elevator passenger comfort evaluation and elevator operation curve optimization design through acquisition, preprocessing, feature extraction and analysis of the operation signals and the electroencephalogram signals, and evaluation deviation caused by uncertainty of subjective evaluation in passenger comfort evaluation of a current transportation system is avoided.

Description

Technical Field

[0001] The present invention relates to the fields of high-speed elevator passenger comfort analysis and brain science, and in particular to a passenger comfort analysis method, system and device based on human brain electricity during the operation of a high-speed elevator. Background Art

[0002] As a key vertical transportation tool, elevators have become an important part of modern urban infrastructure. Passenger comfort is an important indicator for evaluating the performance of high-speed elevators and is used to evaluate the riding experience of elevator passengers. The current passenger comfort measurement mainly relies on the subjective scale method, which quantifies the operation data of the car through acceleration sensors and constructs a comfort evaluation system. The existing methods generally have evaluation biases due to factors such as perception differences among different groups of people and memory errors. Summary of the invention

[0003] In order to overcome the shortcomings of the prior art, the present invention provides a method, system and device for analyzing passenger comfort based on human EEG during the operation of a high-speed elevator, which aims to provide a new objective analysis tool, thereby avoiding the evaluation bias caused by the uncertainty of subjective evaluation in the current transportation system passenger comfort evaluation, and improving the reliability and effectiveness of the passenger comfort analysis.

[0004] In order to achieve the above object, the present invention provides a method for analyzing the comfort of high-speed elevator passengers based on electroencephalogram, comprising the following steps:

[0005] S1: Simultaneously collect and store the human brain electrical signals of passengers when they take the high-speed elevator and the car operation signals of the high-speed elevator;

[0006] S2: judging the change of the operation state of the high-speed elevator based on the car operation signal; intercepting the human brain electrical signal and the car operation signal during the change of the operation state to obtain human brain electrical data and car operation data;

[0007] S3: pre-processing human EEG data and car operation data;

[0008] S4: extracting features from human EEG data and car operation data, and forming human EEG indicators and car operation indicators after analysis; wherein the step of performing feature analysis on human EEG data includes: performing power spectrum analysis on the intercepted EEG signals and performing effective connection analysis on the EEG signals;

[0009] S5: By recording and storing the human brain electrical index and the car operation index, a human brain electrical index curve and a car operation index curve are formed; wherein the index curve is a quantitative trajectory of a single index mapped in a two-dimensional Cartesian coordinate system.

[0010] Preferably, the pretreatment step comprises:

[0011] Using a filter to remove noise from the human brain electrical data and the car operation data;

[0012] Resampling the human brain electrical data and the car operation data;

[0013] An independent component analysis algorithm is used to remove interference signals in the human EEG data.

[0014] Preferably, in step S4, the step of performing power spectrum analysis on the intercepted EEG signal includes: using the Welch algorithm to calculate the power spectrum density of each data segment in different EEG frequency bands as an EEG power spectrum indicator.

[0015] Preferably, in step S4, the specific steps of performing effective connection analysis on the EEG signal include: obtaining the direct directional transfer function matrix of each data segment; calculating the superposition average of the direct directional transfer function matrices of all data segments, dividing them into frequency bands, and obtaining the mean of the direct directional transfer functions of different connection directions in each frequency band as an EEG effective connection indicator.

[0016] The present invention also provides a high-speed elevator passenger comfort analysis system based on electroencephalogram, which comprises:

[0017] A collection module, used for collecting the human brain electrical signals of passengers and the car operation signals of high-speed elevators;

[0018] a trigger module, coupled to the car operation data acquisition module, configured to determine a change in the operation state of the high-speed elevator car based on the car operation signal, and output an activation signal to the data analysis module during a change in the operation state;

[0019] A data analysis module is configured to receive the activation signal, the human EEG signal and the car operation signal; intercept and record the human EEG signal and the car operation signal according to the activation signal to obtain human EEG data and car operation data; perform time synchronization, data preprocessing and feature extraction analysis on the human EEG data and the car operation data, and output EEG indicators and car operation indicators; wherein, when performing feature analysis on the human EEG data, power spectrum analysis is performed on the intercepted EEG signal by the Welch algorithm to obtain power spectrum indicators of the EEG data, and effective connection analysis is performed on the EEG signal by the direct directional transfer function to obtain effective connection indicators of the EEG data.

[0020] Preferably, when the trigger module determines that the running state of the high-speed elevator car changes, the process is:

[0021] A threshold is set according to the historical data of the car operation signal to determine whether the car operation signal exceeds the threshold range. When the determination result is yes, an activation signal is output; when the determination result is no, the activation signal is stopped from being output.

[0022] Preferably, the car operation signal includes an acceleration signal, a speed signal and a posture signal; the car operation index includes the time domain characteristics, frequency domain characteristics and information entropy of the operation data; the EEG index includes the time domain characteristics, frequency domain characteristics, information entropy and effective connection of the EEG data.

[0023] The present invention also provides a device for analyzing the comfort of high-speed elevator passengers based on electroencephalogram, and uses a method for analyzing the comfort of high-speed elevator passengers based on electroencephalogram for analysis, including:

[0024] EEG sensor, operation sensor, intelligent terminal, uninterruptible power supply, tool box and mobile collection cart; the EEG sensor includes the EEG electrode cap and the EEG signal amplifier; the operation sensor includes the acceleration sensor and the operation sensor host; the mobile collection cart includes from top to bottom: an equipment layer, a tool layer and an operation layer, and each layer is rigidly connected by four groups of fixed rods; the equipment layer includes a storage chassis and a universal wheel connected to the bottom of the storage chassis, which is used to place the uninterruptible power supply; the tool layer is used to place the acceleration sensor, the EEG electrode cap and the tool box; the operation layer includes an operation table, wherein a handrail is provided on one side of the operation table, and the EEG signal amplifier and the operation sensor host are detachably connected on one side of the bottom surface of the operation table; the operation layer is used to place the intelligent terminal; the EEG sensor, the operation sensor, the uninterruptible power supply and the intelligent terminal are electrically connected to each other;

[0025] When performing analysis, the EEG sensor collects human EEG data, and the operation sensor collects car operation data; the collected data is input into the intelligent terminal to judge the change of the operation state of the elevator car, pre-process the human EEG data and the car operation data, and perform feature extraction and analysis, and finally output the human EEG indicators and the car operation indicators.

[0026] The present invention also provides an intelligent terminal, which includes:

[0027] At least one processor, and a machine-readable storage medium in communication with the at least one processor; wherein the machine-readable storage medium has computer instructions executed by the at least one processor; and the computer instructions are used to implement a method and system for analyzing the comfort of high-speed elevator passengers based on electroencephalograms.

[0028] Compared with the prior art, the present invention has the following beneficial effects:

[0029] The present invention is based on brain science and realizes an analysis method that does not rely on subjective feedback from passengers, thus avoiding the influence of verbal communication. The present invention uses EEG signals to analyze the passenger's riding comfort. Compared with peripheral electrophysiological analysis techniques such as electrocardiography and electrodermal analysis, EEG signals have higher time resolution and are suitable for capturing neural events and cognitive events in a short period of time in high-speed elevators. In addition, EEG signals are directly related to high-level cognitive functions such as comfort, while peripheral physiology such as electrocardiography and electrodermal analysis are only indirectly related to high-level neural activities. Based on this, analyzing the comfort of elevator passengers through EEG signals has higher objectivity and reliability. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 A flowchart of a method for analyzing the comfort of high-speed elevator passengers based on electroencephalogram provided by the present invention;

[0031] Figure 2 A framework diagram of a high-speed elevator passenger comfort analysis system based on EEG provided by the present invention;

[0032] Figure 3 A flow chart of a high-speed elevator passenger comfort analysis system based on EEG provided by the present invention;

[0033] Figure 4 This is a schematic diagram of the connection positions of human EEG electrodes;

[0034] Figure 5 It is the acceleration diagram of the high-speed elevator going up and down;

[0035] Figure 6 A schematic diagram of the structure of a high-speed elevator passenger comfort analysis device based on electroencephalogram provided by the present invention;

[0036] Figure 7 A schematic diagram of the use of an EEG-based high-speed elevator passenger comfort analysis device provided by the present invention. DETAILED DESCRIPTION

[0037] The following describes the embodiments of the present invention by specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.

[0038] It should be noted that the illustrations provided in the following embodiments are only schematic illustrations of the basic concept of the present invention, and thus the drawings only show components related to the present invention rather than being drawn according to the number, shape and size of components in actual implementation. In actual implementation, the type, quantity and proportion of each component may be changed arbitrarily, and the component layout may also be more complicated.

[0039] Passenger comfort comes from human perception, in which the nervous system plays a major regulatory role. The neural pathways of this system are widely distributed in the brainstem, cerebellum and cerebral cortex, and trigger physiological responses by integrating sensory information. Based on this, the analysis of high-speed elevator passenger comfort can be achieved by detecting the electrophysiological signals of relevant brain regions and the parameters of the signals.

[0040] like Figures 1 to 7 As shown, the present invention provides a method for analyzing the comfort of high-speed elevator passengers based on electroencephalogram, comprising the following steps:

[0041] S1: Simultaneously collect and store the human brain electrical signals of passengers when they take the high-speed elevator and the car operation signals of the high-speed elevator;

[0042] S2: judging the change of the operation state of the high-speed elevator based on the car operation signal; intercepting the human brain electrical signal and the car operation signal during the change of the operation state to obtain human brain electrical data and car operation data;

[0043] S3: pre-processing human EEG data and car operation data;

[0044] S4: extracting features from human EEG data and car operation data, and forming human EEG indicators and car operation indicators after analysis; wherein the step of performing feature analysis on human EEG data includes: performing power spectrum analysis on the intercepted EEG signals and performing effective connection analysis on the EEG signals;

[0045] S5: By recording and storing the human brain electrical index and the car operation index, a human brain electrical index curve and a car operation index curve are formed; wherein the index curve is a quantitative trajectory of a single index mapped in a two-dimensional Cartesian coordinate system.

[0046] First, the experimenter placed the accelerometer at the center of the high-speed elevator car to collect the acceleration signal of the high-speed elevator. The sampling rate of the accelerometer is 1 kHz and the resolution is 0.0005 g / LSB. The experimenter placed the 32-lead EEG electrode cap on the occupant's head. The sampling rate of the EEG acquisition system was 1 kHz. The electrode distribution followed the 10-10 international standard lead system. The Cz electrode was set as the reference electrode and the Fpz electrode was set as the ground electrode. The occupant was required to clean his hair and inject conductive paste at the electrode position to ensure that the electrode impedance was less than 5k ohms during the EEG recording process. The experiment sent synchronized timestamps to the EEG acquisition system and accelerometer through serial communication. The timestamps were sent every 30 seconds.

[0047] Under the guidance of the experimenter, the passenger enters the elevator and stands near the center of the elevator car with his eyes closed. First, the acceleration signal of the elevator in a stationary state and the resting EEG signal of the passenger in the elevator in a stationary state are collected. The three-axis acceleration signal of the elevator is extracted, and its Euclidean norm is taken as the elevator acceleration data. The acceleration data is transmitted to the trigger module, which first calculates the confidence interval of the acceleration data of the elevator in a stationary state. The calculation formula is:

[0048]

[0049] in, At a 95% confidence level Value, approximately equal to 1.96; is the sample mean of vertical acceleration; is the standard deviation; is the number of samples, is the limit of the confidence interval. The acceptance domain of the data is calculated by the above formula.

[0050] Then, under the operation of the experimenter, each passenger experienced "up-down-up-down", a total of 4 elevator runs. During the elevator operation, the elevator acceleration signal and the passenger's EEG signal were recorded. The start and end time of the acceleration data segment exceeding the upper confidence limit and below the lower confidence limit were taken as the elevator acceleration event window.

[0051] Preprocess the EEG signals.

[0052] Firstly, the EEG data is filtered by a bandpass filter of 0.5-45 Hz, and the sampling signal is reduced to 250 Hz to remove the baseline drift and power frequency interference of the signal; then the reference point of the EEG signal is adjusted to the average reference; the pseudo shadow space reconstruction algorithm is used to remove the bad data segments in the signal; the human EEG data and the car operation data are resampled; the independent component analysis algorithm is used to remove the eye movement and electromyography interference in the EEG data; the EEG and acceleration data are time synchronized according to the synchronization timestamps of each sensor; finally, the EEG data is intercepted by the elevator acceleration event window.

[0053] The power spectrum analysis was performed on the intercepted EEG signal.

[0054] The Welch algorithm was used to calculate the power spectral density of each data segment in different EEG frequency bands. The Delta frequency band is 1~4 Hz; Theta frequency band is 4~8 Hz; Alpha frequency band is 8~13 Hz; Beta frequency band is 13~30 Hz; Gamma frequency band is 30~45 Hz. Among them, Welch used the Hanning window to calculate the power spectral density of each data segment of the EEG sample and calculate the average value of each port. The effective connection features of EEG data in the frontal lobe brain area were extracted, and the direct directional transfer function was used to calculate the effective connection value between each EEG port; then the average value of the effective connection value within each brain area and the average value of the effective connection value between brain areas were calculated as the effective connection feature.

[0055] Perform effective connection analysis on EEG signals.

[0056] The effective connection value parameter is calculated by direct directional transfer function based on Granger causality. First, a multivariate autoregressive model of sliding window is established by time-varying window analysis. A rectangular window is selected with a duration of 0.4 seconds and a step of 0.03 seconds.

[0057] The multivariate autoregressive model is defined as:

[0058]

[0059] In the formula, For 32-channel EEG time series, is a 32×32 coefficient matrix, is the white noise remainder, is the order of the multivariate autoregressive model determined by the Hannan-Quinne Information Criterion.

[0060] Use fast Fourier transform to transform the above formula into frequency domain, namely:

[0061]

[0062] in, is the transfer matrix of the system, is the frequency, is a complex number. The direct directional transfer function is defined as:

[0063]

[0064] in, is the full-frequency directional transfer function; are the row and column numbers of the matrix respectively.

[0065]

[0066] In the formula Indicates the number of channels, is the full-frequency directional transfer function The column number.

[0067] is the partial drying function, defined as:

[0068]

[0069] Its expression is:

[0070] ,

[0071] in, is the power spectrum function.

[0072] After obtaining the direct directional transfer function matrix of each data segment, the superposition average of the direct directional transfer function matrices of all data segments is calculated. Then, after dividing it into frequency bands, the mean of the direct directional transfer function of different connection directions in each frequency band is calculated as an indicator to describe the effective connection strength of EEG.

[0073] Finally, statistical analysis was performed on the EEG data characteristics of the human body in the resting state and the EEG data characteristics captured in the acceleration event window. The analysis results show that under the influence of high-speed elevator acceleration, the absolute power of the Theta band of the human brain increased from the baseline of 0.45±0.022μV² / Hz to 0.726±0.197μV² / Hz.

[0074] Table 1 shows the EEG characteristic parameters (mean ± standard deviation) of the baseline state and the acceleration state, as well as the corresponding results of the p value for the comparison between the two.

[0075]

[0076] Table 1

[0077] Based on the above analysis, compared with the prior art, the present invention realizes the collection and analysis of human EEG data of passengers in a high-speed elevator environment, which is used as the basis for comfort analysis.

[0078] like Figure 3 As shown, the present invention also provides a high-speed elevator passenger comfort analysis system based on EEG, which includes: an acquisition module, a trigger module, and a data analysis module. The acquisition module is used to collect the human EEG signals of the passengers and the car operation signals of the high-speed elevator. The trigger module is coupled to the car operation data acquisition module, and is configured to determine the change in the operation state of the high-speed elevator car based on the car operation signal, and output an activation signal to the data analysis module during the change in the operation state. The data analysis module is configured to receive the activation signal, the human EEG signal and the car operation signal; intercept and record the human EEG signal and the car operation signal according to the activation signal to obtain the human EEG data and the car operation data; perform time synchronization, data preprocessing, and feature extraction analysis on the human EEG data and the car operation data, and output EEG indicators and car operation indicators. Among them, the acquisition module includes a human EEG data acquisition module for collecting the human EEG signals of the passengers; a car operation data acquisition module for collecting the car operation signals of the high-speed elevator;

[0079] Among them, when the trigger module determines the change of the operating status of the high-speed elevator car, specifically: set the threshold according to the historical data of the car operation signal; determine whether the car operation signal exceeds the threshold range; when the judgment result is "yes", output the activation signal; when the judgment result is "no", stop outputting the activation signal; wherein, the threshold is set to the 95% confidence interval of the statistical distribution of the historical data.

[0080] Among them, when the acquisition module collects human EEG data, it uses EEG sensors for collection; the EEG sensor uses an EEG electrode cap set on the occupant's head; according to the relevant provisions of the standard GB / T 24474.1-2020 "Ride Quality Measurement Part 1: Elevator", when the acquisition module collects car operation data, it uses operation sensors for collection; the operation sensor uses an acceleration sensor set at the center of the bottom of the high-speed elevator car.

[0081] Among them, the operation indicators include the time domain characteristics, frequency domain characteristics and information entropy of the operation data; the EEG indicators include the time domain characteristics, frequency domain characteristics, information entropy and effective connections of the EEG data.

[0082] The present invention also provides an intelligent terminal, comprising: at least one processor; and a machine-readable storage medium communicatively connected to the at least one processor; wherein the machine-readable storage medium has computer instructions executed by the at least one processor; and the computer instructions are used to implement a high-speed elevator occupant comfort analysis system based on electroencephalogram.

[0083] like Figures 6 and 7 As shown, the present invention also provides a high-speed elevator passenger comfort analysis device based on electroencephalogram, which includes:

[0084] It includes an EEG sensor, an operation sensor, an intelligent terminal, an uninterruptible power supply, a tool box and a mobile collection cart; the EEG sensor includes an EEG electrode cap and an EEG signal amplifier; the operation sensor includes an acceleration sensor and an operation sensor host; the mobile collection cart includes an equipment layer, a tool layer and an operation layer; the equipment layer and the tool layer are rigidly connected to each other through four groups of fixed rods; the tool layer and the operation layer are rigidly connected to each other through four groups of fixed rods; the equipment layer includes a storage chassis and a universal wheel connected to the bottom of the storage chassis, which is used to place an uninterruptible power supply; the tool layer is used to place the acceleration sensor, the EEG electrode cap and the tool box; the operation layer includes an operation table, wherein at least one side of the operation table is provided with a handrail, and one side of the bottom surface of the operation table is detachably connected with the EEG signal amplifier and the operation sensor host; the operation layer is used to place the intelligent terminal; the EEG sensor, the operation sensor, the uninterruptible power supply and the intelligent terminal are electrically connected to each other.

[0085] When performing the analysis, the human brain electrical data is collected through the brain electrical sensor, and the car operation data is collected through the operation sensor; the collected data is input into the intelligent terminal, and the intelligent terminal analyzes and judges the collected data, and finally outputs the human brain electrical index and the car operation index. Among them, the specific analysis and judgment process of the collected data by the intelligent terminal is the same as the process in the high-speed elevator passenger comfort analysis method based on brain electrical, and will not be repeated here.

[0086] Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

Claims

1. A method for analyzing the comfort of high-speed elevator passengers based on EEG, characterized in that: The following steps are involved: S1: Simultaneously collect and store the human brain electrical signals of passengers when they take the high-speed elevator and the car operation signals of the high-speed elevator; S2: Based on the car operation signal, determine the change of the operation state of the high-speed elevator; intercept the human brain electrical signal and the car operation signal during the change of the operation state to obtain human brain electrical data and car operation data; S3: preprocessing the human brain electrical data and the car operation data; S4: extracting features from the human EEG data and the car operation data, and analyzing them to form human EEG indicators and car operation indicators; wherein the step of performing feature analysis on the human EEG data includes: performing power spectrum analysis on the intercepted EEG signals and performing effective connection analysis on the EEG signals; S5: Forming a human EEG index curve and a car operation index curve by recording and storing the human EEG index and the car operation index; wherein the index curve is a quantitative trajectory of a single index mapped in a two-dimensional Cartesian coordinate system.

2. The EEG-based high-speed elevator passenger comfort analysis method according to claim 1 is characterized in that: The pre-processing steps include: Using a filter to remove noise from the human brain electrical data and the car operation data; Resampling the human brain electrical data and the car operation data; An independent component analysis algorithm is used to remove interference signals in the human EEG data.

3. The method for analyzing the comfort of high-speed elevator passengers based on electroencephalogram according to claim 1, characterized in that: In step S4, the step of performing power spectrum analysis on the intercepted EEG signal includes: using the Welch algorithm to calculate the power spectrum density of each data segment in different EEG frequency bands as an EEG power spectrum indicator.

4. The method for analyzing the comfort of high-speed elevator passengers based on electroencephalogram according to claim 1, characterized in that: In step S4, the specific steps of performing effective connection analysis on the EEG signal include: obtaining the direct directional transfer function matrix of each data segment; calculating the superposition average of the direct directional transfer function matrices of all data segments, dividing them into frequency bands, and obtaining the mean value of the direct directional transfer function of different connection directions in each frequency band as an EEG effective connection indicator.

5. A high-speed elevator passenger comfort analysis system based on EEG, characterized in that: include: A collection module, used for collecting the human brain electrical signals of passengers and the car operation signals of high-speed elevators; a trigger module, coupled to the car operation data acquisition module, configured to determine a change in the operation state of the high-speed elevator car based on the car operation signal, and output an activation signal to the data analysis module during a change in the operation state; A data analysis module is configured to receive the activation signal, the human EEG signal and the car operation signal; intercept and record the human EEG signal and the car operation signal according to the activation signal to obtain human EEG data and car operation data; perform time synchronization, data preprocessing and feature extraction analysis on the human EEG data and the car operation data, and output EEG indicators and car operation indicators; wherein, when performing feature analysis on the human EEG data, power spectrum analysis is performed on the intercepted EEG signal by the Welch algorithm to obtain power spectrum indicators of the EEG data, and effective connection analysis is performed on the EEG signal by the direct directional transfer function to obtain effective connection indicators of the EEG data.

6. The EEG-based high-speed elevator passenger comfort analysis system according to claim 5, characterized in that: When the trigger module determines that the running state of the high-speed elevator car changes, the process is as follows: A threshold is set according to the historical data of the car operation signal to determine whether the car operation signal exceeds the threshold range. When the determination result is yes, an activation signal is output; when the determination result is no, the activation signal is stopped from being output.

7. The EEG-based high-speed elevator passenger comfort analysis system according to claim 1 or 5, characterized in that: The car operation signal includes an acceleration signal, a speed signal and a posture signal; the car operation index includes the time domain characteristics, frequency domain characteristics and information entropy of the operation data; the EEG index includes the time domain characteristics, frequency domain characteristics, information entropy and effective connection of the EEG data.

8. A device for analyzing the comfort of high-speed elevator passengers based on electroencephalogram, applying the method for analyzing the comfort of high-speed elevator passengers based on electroencephalogram according to any one of claims 1 to 4 for analysis, characterized in that: include: EEG sensors, motion sensors, smart terminals, uninterruptible power supplies, tool boxes, and mobile collection carts; The EEG sensor includes the EEG electrode cap and an EEG signal amplifier; The operation sensor includes the acceleration sensor and an operation sensor host; The mobile collection cart includes from top to bottom: an equipment layer, a tool layer and an operation layer, and each layer is rigidly connected by four sets of fixed rods; the equipment layer includes a storage chassis and a universal wheel connected to the bottom of the storage chassis, which is used to place the uninterruptible power supply; the tool layer is used to place the acceleration sensor, the EEG electrode cap and the tool box; the operation layer includes an operation table, wherein a handrail is provided on one side of the operation table, and the EEG signal amplifier and the operation sensor host are detachably connected to one side of the bottom surface of the operation table; the operation layer is used to place the intelligent terminal; the EEG sensor, the operation sensor, the uninterruptible power supply and the intelligent terminal are electrically connected to each other; When performing analysis, the EEG sensor collects human EEG data, and the operation sensor collects car operation data; the collected data is input into the intelligent terminal to judge the change of the operation state of the elevator car, pre-process the human EEG data and the car operation data, and perform feature extraction and analysis, and finally output the human EEG indicators and the car operation indicators.

9. An intelligent terminal, characterized in that: include: At least one processor, and a machine-readable storage medium in communication with the at least one processor; wherein the machine-readable storage medium has computer instructions executed by the at least one processor; the computer instructions are used to implement the EEG-based high-speed elevator occupant comfort analysis method and system as described in any one of claims 1 to 7.

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