Mental health state assessment method based on brain-computer interface and eye movement tracking

Abstract

The invention discloses a mental health state assessment method based on a brain-computer interface and eye movement tracking, and relates to the technical field of physiological parameter measurement and monitoring, and the mental health state assessment method comprises the following steps: synchronously collecting eye movement signals and frontotemporal electroencephalogram signals of a subject, and carrying out time alignment; identifying a glancing event based on the eye movement signal, and determining a starting moment and glancing intensity; respectively extracting an instant electrical component and a hysteresis electromechanical component from the electroencephalogram signal by taking the starting moment as an anchor point; constructing a frontal temporal electromechanical hysteresis quantity triggered by glancing by using the arrival time and amplitude of the two types of components; and performing normalization processing in combination with the baseline parameter, and calculating a mental state quantitative index. According to the invention, the accuracy and robustness of mental state assessment can be improved.

Description

Technical Field

[0001] This invention relates to the field of physiological parameter measurement and monitoring technology, and in particular to a method for assessing mental health status based on brain-computer interface and eye tracking. Background Technology

[0002] With the development of wearable physiological monitoring technology, the real-time assessment of mental state in dynamic scenarios such as virtual reality interaction, driving assistance, and daily mobile office work using brain-computer interfaces combined with eye-tracking technology has become a research hotspot. In these uncontrolled real-world application scenarios, subjects' eyes frequently undergo rapid saccades in order to acquire visual information. However, this frequent eye movement introduces high-amplitude interference components into the physiological signal acquisition channels located on the forehead and temples. These interference components include not only the electric field effect generated by eye rotation but also the mechanical disturbance caused by the contraction of eye muscles pulling on the soft tissue of the scalp. This greatly reduces the signal-to-noise ratio of the target physiological signal and seriously affects the accuracy and cross-time stability of mental state assessment.

[0003] Existing technologies, when processing such complex signals, typically treat eye-movement-related signals as noise or artifacts that need to be completely suppressed. The conventional approach is to use algorithms such as independent component analysis, regression filtering, or adaptive filtering to attempt to separate electrooculography (EOG) components and electromyography (EMG) interference from EEG signals, or to directly remove data segments containing eye-movement events. However, this ignores the fact that the frontotemporal muscle traction and skin deformation responses triggered by eye movements themselves contain physiological information closely related to the subject's muscle tone and autonomic nerve excitation levels. Simple denoising not only increases the computational load but also often loses key physiological fluctuation information due to over-correction. In addition, when the subject is under stress or high tension, the stiffness of facial muscles and the elasticity of skin change, leading to changes in the morphology and transmission patterns of interference signals. Existing denoising algorithms are unable to adapt to such dynamic changes, resulting in insufficient stability of the final output monitoring indicators and difficulty in providing continuous and interpretable evaluation results in dynamic tasks with frequent eye movements. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies that typically treat eye-tracking interference as noise and suppress or eliminate it, resulting in the loss of electromechanical response characteristics containing physiological information, and thus causing poor stability and insufficient robustness of the assessment results. The invention proposes a mental health status assessment method based on brain-computer interface and eye-tracking.

[0005] To address the problems existing in the prior art, the present invention adopts the following technical solution: Mental health assessment methods based on brain-computer interfaces and eye tracking include: S1. Simultaneously acquire the subject's eye movement signals and frontotemporal EEG signals, and time-align the eye movement signals and the frontotemporal EEG signals; S2. Identify saccade events based on aligned eye movement signals, and determine the start time and saccade intensity scalar for each saccade event; S3. Using the start time as the anchor point, extract the event window from the frontotemporal EEG signal and extract the instantaneous electrical component and the delayed electromechanical component. S4. Determine the arrival time and amplitude of the instantaneous electrical component and the hysteresis electromechanical component respectively, and construct the saccade-triggered frontotemporal electromechanical hysteresis based on the arrival time, the amplitude and the saccade intensity scalar. S5. Statistically analyze the frontotemporal electromechanical lag within the preset assessment time window, normalize it in combination with the pre-stored baseline parameters, and calculate the mental state quantitative index.

[0006] Preferably, recognizing saccade events based on aligned eye-tracking signals and determining the start time and saccade intensity scalar for each saccade event includes: Calculate the angular velocity and angular acceleration sequences of the eye movement signals; An adaptive threshold is calculated based on the statistical characteristics of the angular velocity sequence. When the absolute value of the angular velocity sequence first crosses the adaptive threshold, it is identified as the start time of the saccadic event. Within a preset saccade duration window after the start time, the maximum absolute value of the angular acceleration sequence is extracted and used as the saccade intensity scalar.

[0007] Preferably, using the start time as the anchor point, an event window is extracted from the frontotemporal EEG signal, and the immediate electrical component and the delayed electromechanical component are extracted, including: For each saccade event, an event window containing a preset duration before and after the start time is extracted from the frontotemporal EEG signal; Bandpass filtering with a passband frequency of 10Hz to 60Hz was performed on the frontotemporal EEG signals within the event window to obtain the instantaneous electrical components; Bandpass filtering with a passband frequency of 0.1 Hz to 6 Hz is performed on the frontotemporal EEG signal within the event window to obtain the hysteresis electromechanical component.

[0008] Preferably, determining the arrival time and amplitude of the instantaneous electrical component and the hysteresis electromechanical component includes: Calculate the energy envelope of the instantaneous electrical component, record the time corresponding to the maximum value of the energy envelope within the first search window as the arrival time of the instantaneous electrical component, and record the square root of the maximum value as the amplitude of the instantaneous electrical component. Calculate the absolute value of the derivative of the hysteresis electromechanical component, and record the time corresponding to the maximum value of the absolute value of the derivative in the second search window as the arrival time of the hysteresis electromechanical component, and record the maximum absolute value of the hysteresis electromechanical component in the second search window as the amplitude of the hysteresis electromechanical component. Both the first search window and the second search window are set relative to the start time, and the time interval of the first search window is earlier than the time interval of the second search window.

[0009] Preferably, the frontotemporal electromechanical hysteresis triggered by saccades is constructed by calculating the ratio of normalized hysteresis to electromechanical amplitude; The formula for calculating the normalized hysteresis is:

[0010] The formula for calculating the electromechanical amplitude ratio is:

[0011] In the formula, For the first The second scanning incident, For the first One brainwave channel, For normalization lag, The electromechanical amplitude ratio, For the instantaneous arrival time of the electrical component, To delay the arrival time of the electromechanical components, The amplitude of the instantaneous electrical component, The amplitude of the hysteresis electromechanical component, To scan the intensity scalar, This is a preset value to prevent the division of small positive numbers into zero; The frontotemporal electromechanical hysteresis is determined based on the ratio of normalized hysteresis to electromechanical amplitude.

[0012] Preferably, the frontotemporal electromechanical hysteresis is determined based on the ratio of normalized hysteresis to electromechanical amplitude, including: For a single saccade event, obtain the normalized delay to electromechanical amplitude ratio of the EEG channel; The median of the normalized delay of the EEG channels was used to obtain the normalized delay of a single saccade. The median of the electromechanical amplitude ratios of the EEG channels is used to obtain the electromechanical amplitude ratio of a single saccade. The frontotemporal electromechanical hysteresis is formed by combining the normalized hysteresis of a single saccade with the electromechanical amplitude ratio of a single saccade.

[0013] Preferably, the frontotemporal electromechanical lag within a preset assessment time window is statistically analyzed, and normalized in conjunction with pre-stored baseline parameters to calculate a quantitative index of mental state, including: The truncated mean values ​​of the frontotemporal electromechanical hysteresis corresponding to all saccade events within the preset evaluation time window are statistically analyzed to obtain the mean ratio of the current hysteresis mean to the current amplitude mean. Calculate the ratio of the baseline hysteresis mean in the pre-stored baseline parameters to the current hysteresis mean, and take the logarithm of the ratio to obtain the hysteresis explanation component; Calculate the ratio of the current mean amplitude ratio to the mean baseline amplitude ratio in the pre-stored baseline parameters, and take the logarithm of the ratio to obtain the amplitude ratio interpretation component; The mental state quantitative index is obtained by weighted summation of the hysteresis explanatory component and the amplitude ratio explanatory component.

[0014] Preferably, obtaining the baseline parameters includes: Eye movement signals and frontotemporal EEG signals were collected from subjects in a resting state, and the frontotemporal electromechanical lag sequence was extracted in a resting state. The frontotemporal electromechanical hysteresis sequence under resting state is statistically analyzed to obtain the mean baseline hysteresis and the mean baseline amplitude ratio, which are then stored as the baseline parameters.

[0015] A device for quantitatively assessing mental state, comprising: An eye-tracking acquisition unit is used to acquire the subject's eye-tracking signals; The EEG acquisition unit is used to acquire frontotemporal EEG signals from the subject. A processor, and a memory connected to the processor; wherein the memory stores instructions executable by the processor, which, when executed, cause the processor to perform the method described above.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. This invention utilizes synchronously acquired eye movement signals and frontotemporal EEG signals, taking the start time of the saccade event as the physical anchor point. It decomposes the interference in the EEG signal into an instantaneous electrical component and a delayed electromechanical component, and quantifies the time difference and amplitude ratio between the two, thereby constructing a frontotemporal electromechanical hysteresis quantity that reflects the mechanical damping characteristics of the electrode-skin contact interface. This processing method no longer treats eye movement signals as noise to be eliminated, but uses them as an excitation source that can actively detect the subject's muscle tone and autonomic nervous state. It makes full use of the biomechanical response information triggered by saccades, so that the assessment results can directly reflect the subject's physiological tension level, significantly improving the accuracy and physiological interpretability of mental state assessment.

[0017] 2. This invention calculates the normalized hysteresis-to-electromechanical amplitude ratio of the hysteresis electromechanical component relative to the instantaneous electrical component, and performs normalization processing in conjunction with the subject's own resting baseline parameters. This eliminates the influence of differences in saccade intensity, individual skin conductivity, and channel gain on the assessment results, making the final output mental state quantitative index more robust to dynamic factors such as changes in wearing conditions and fluctuations in facial muscle stiffness. It effectively solves the problem of unstable monitoring indicators caused by changes in signal morphology in uncontrolled dynamic scenarios, and achieves continuous and stable monitoring across time periods and scenarios. Attached Figure Description

[0018] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings: Figure 1 This is a flowchart illustrating the mental health status assessment method based on brain-computer interface and eye tracking of the present invention. Figure 2 This is a timing diagram of the instantaneous electrical component and the hysteresis electromechanical component triggered by the saccade of the present invention. Detailed Implementation

[0019] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0020] Example: This example provides a method for assessing mental health status based on brain-computer interface and eye tracking. See [link to example]. Figure 1 Specifically, including: S1. Simultaneously acquire the subject's eye movement signals and frontotemporal EEG signals, and time-align the eye movement signals and the frontotemporal EEG signals; In embodiments of the present invention, the eye movement signals and frontotemporal EEG signals of the subject are acquired simultaneously, and the eye movement signals and the frontotemporal EEG signals are time-aligned, including: Simultaneously collect eye movement signals and frontotemporal electroencephalogram (EEG) signals from the subjects; The eye movement signal and the frontotemporal EEG signal were time-aligned. It should be noted that the simultaneous acquisition of eye movement signals and frontotemporal EEG signals of the subject refers to the simultaneous sampling and continuous recording of the eye movement acquisition unit and the EEG acquisition unit by the same timing reference or equivalent unified timestamp within the same measurement session, so that the two types of signals correspond to the same physiological activity process on the time axis. The changes in eye movement and scalp potential both originate from the individual's neuromuscular activity and electrophysiological activity under task stimulation or natural state. Eye movement signals refer to the measurement sequence reflecting changes in eye rotation and gaze direction obtained by the eye movement tracking component. The measurement sequence can be converted into the gaze direction by the positional relationship between the pupil center position and the corneal reflex point or obtained by the change of eye rotation angle over time, thereby characterizing the occurrence and intensity changes of eye movement events such as fixation, salivation, and blinking over time. Frontotemporal EEG signals refer to the scalp potential change sequence over time collected by electrodes placed in the forehead and temporal scalp regions. This sequence originates from the conduction of neuronal population activity in the volume conductor of brain tissue and superimposed with the electrochemical effect of the electrode-skin contact interface and local electromyography and other factors to form measurable potential changes.

[0021] Specifically, the subject wears frontotemporal EEG electrodes from the brain-computer interface (BCI) acquisition unit and an eye-tracking module from the eye-tracking acquisition unit. The frontotemporal EEG electrodes must be accurately positioned to align with the Fp1 and Fp2 channels on the front of the subject's forehead and the T7 and T8 channels on the temporal side. The eye-tracking module must be fixed in a suitable position in front of the subject's eyes to ensure stable capture of eye movements. Synchronization and timing are then initiated, outputting a unified synchronization trigger signal to both the BCI acquisition unit and the eye-tracking acquisition unit. This controls both acquisition units to begin signal acquisition simultaneously. During acquisition, the eye-tracking acquisition unit obtains raw eye movement signals at a sampling rate of 90 Hz to 250 Hz, while the BCI acquisition unit obtains signals at a sampling rate of 250 Hz to 1000 Hz. The raw frontotemporal EEG signals are acquired, and both acquisition units bind the acquired raw signals with the unified timestamp information provided by the synchronization and time stamping unit and store them in the memory in real time. For the acquired raw eye movement signals and raw frontotemporal EEG signals, the processor first performs preliminary preprocessing on the two types of signals to remove obvious impulse noise. Then, the processor determines a unified discrete time sequence. The sampling interval of this discrete time sequence corresponds to a sampling frequency of 250 Hz to 500 Hz. Subsequently, linear interpolation or cubic spline interpolation methods are used to map the sampling points of the raw eye movement signals and raw frontotemporal EEG signals to the above-mentioned unified discrete time sequence, so that each unified discrete time corresponds to a unique eye movement signal value and a unique frontotemporal EEG signal value.

[0022] S2. Identify saccade events based on aligned eye movement signals, and determine the start time and saccade intensity scalar for each saccade event; In embodiments of the present invention, saccade events are identified based on aligned eye movement signals, and the start time and saccade intensity scalar of each saccade event are determined, including: Calculate the angular velocity and angular acceleration sequences of the eye movement signals; An adaptive threshold is calculated based on the statistical characteristics of the angular velocity sequence. When the absolute value of the angular velocity sequence first crosses the adaptive threshold, it is identified as the start time of the saccadic event. Within a preset scanning duration window after the start time, the maximum absolute value of the angular acceleration sequence is used as the scanning intensity scalar. It should be noted that the angular velocity sequence is a sequence formed by the first-order rate of change of eye rotation angle with respect to time, used to characterize the speed and degree of change in direction of eye rotation; the angular acceleration sequence is a sequence formed by the second-order rate of change of angular velocity with respect to time, used to characterize the speed of change in eye rotation speed, i.e., the intensity of eye rotation initiation and braking; the adaptive threshold is a discrimination boundary value calculated from the statistical characteristics of the angular velocity sequence, which is automatically adjusted according to individual differences of the subject and the level of natural eye movement under the current task conditions, used to distinguish between normal fixation micromovements and rapid saccadic behaviors; the absolute value of the angular velocity sequence is a non-negative representation of the magnitude of the angular velocity, used to characterize only the intensity of rotation, ignoring the direction of rotation. When the absolute value of the angular velocity sequence first crosses the adaptive threshold, this moment corresponds to the initial boundary of the eyeball transitioning from a relatively stable gaze to a rapid rotation phase. A saccade event refers to the movement process in which the eyeball rapidly jumps from one gaze position to another within a short period of time. This is accompanied by rapid contraction of the extraocular muscles, resulting in significant angular changes and velocity peaks. The saccade intensity scalar is a single value used to quantify the strength of a single saccade movement. Using the maximum absolute value of the angular acceleration sequence as the saccade intensity scalar within the continuous window means that the maximum amplitude of the change in eyeball rotation speed is used to characterize the intensity of the initiation of the saccade, thereby obtaining an intensity measure related to the magnitude of the instantaneous driving force of the extraocular muscles.

[0023] Specifically, after time alignment of the eye movement signal and the frontotemporal EEG signal, the processor first performs noise reduction preprocessing on the aligned eye movement signal. A bandpass filter of 0.1-6Hz is used to remove high-frequency interference and baseline drift in the eye movement signal, resulting in a clean eye movement angle sequence. Based on the uniform sampling interval determined after time alignment, the angular velocity sequence of the eye movement signal is calculated. Specifically, the difference between the eye movement angle values ​​corresponding to two adjacent uniform discrete moments is calculated, and then the difference is divided by the uniform sampling interval to obtain the angular velocity value at each discrete moment, thus forming a complete angular velocity sequence. On this basis, the same difference calculation method is used to obtain the angular acceleration sequence at each discrete moment by calculating the difference between the angular velocity values ​​corresponding to two adjacent discrete moments and dividing by the uniform sampling interval.

[0024] When calculating the adaptive threshold based on the statistical characteristics of the angular velocity sequence, the processor first extracts the absolute values ​​of all values ​​in the angular velocity sequence, statistically obtains the median and median absolute deviation of the absolute value sequence, and then multiplies the median absolute deviation by a preset coefficient between 3 and 6 to ensure that the threshold is higher than the upper limit of the natural fluctuation of gaze micromotion without missing moderate-amplitude saccades. The resulting product is added to the median to obtain the adaptive threshold used to judge saccade events. The absolute values ​​of the angular velocity sequence are monitored step by step. When the absolute value of the angular velocity at a certain discrete moment is detected to be greater than the above adaptive threshold for the first time, and the absolute value of the angular velocity at three or more consecutive discrete moments thereafter remains above the adaptive threshold, the moment when the absolute value of the angular velocity first exceeds the adaptive threshold is determined. The starting time of the saccade event is determined to avoid false detections triggered by single-point noise. Subsequently, a preset saccade duration window of 60-120ms is preferred. The main initiation and braking processes of the saccade usually occur within a few hundred milliseconds, and the window length can cover the critical stages of rapid velocity rise and fall. The starting point of this window is the confirmed starting time of the saccade event. The processor extracts the angular acceleration sequence values ​​corresponding to all discrete moments within the preset saccade duration window, calculates the absolute value of each value, and then selects the maximum value from these absolute values. The maximum value is the saccade intensity scalar of the corresponding saccade event. At the same time, the saccade intensity scalar is bound to the corresponding saccade event starting time and stored in the memory to provide basic data for the subsequent construction of the frontotemporal electromechanical hysteresis triggered by the saccade.

[0025] S3. Using the start time as the anchor point, extract the event window from the frontotemporal EEG signal and extract the instantaneous electrical component and the delayed electromechanical component. In an embodiment of the present invention, using the start time as the anchor point, an event window is extracted from the frontotemporal electroencephalogram (EEG) signal, and the instantaneous electrical component and the delayed electromechanical component are extracted, including: For each saccade event, an event window containing a preset duration before and after the start time is extracted from the frontotemporal EEG signal; Bandpass filtering with a passband frequency of 10Hz to 60Hz was performed on the frontotemporal EEG signals within the event window to obtain the instantaneous electrical components; Bandpass filtering with a passband frequency of 0.1Hz to 6Hz is performed on the frontotemporal EEG signal within the event window to obtain the hysteresis electromechanical component; It should be noted that the instantaneous electrical component refers to the rapid potential change component in the frontotemporal EEG signal with the start time of the saccade event as the time anchor point. It is mainly manifested as transient waveforms or energy surges occurring within a short time scale. This component is obtained within the event window by performing bandpass filtering on the frontotemporal EEG signal with a passband frequency of 10 Hz to 60 Hz. It is used to characterize the instantaneous response of rapid electric field disturbances and short-term neural electrical activity changes related to saccades to scalp potentials. The hysteretic electromechanical component refers to the relatively slow potential change component caused by the same saccade event in the frontotemporal EEG signal. It is mainly manifested as slow change patterns such as low-frequency drift, inflection points, or declines. This component is obtained within the event window by performing bandpass filtering on the frontotemporal EEG signal with a passband frequency of 0.1 Hz to 6 Hz. It is used to characterize the contact impedance changes and related potential changes caused by the dynamic pressure and micro-motion modulation generated at the electrode-skin contact interface after the extraocular and temporalis muscles contract and pull on the scalp soft tissue and propagate through the tissue.

[0026] Specifically, when extracting an event window containing a preset duration before and after the start time from the frontotemporal EEG signal for each saccade event, the start time of the saccade event is first detected by eye movement signals and mapped onto a time axis consistent with the frontotemporal EEG signal. Then, the start and end boundaries of the event window are determined with the start time as the center. The preset duration of the first segment of the event window is preferably 50 to 150 milliseconds to cover the stable baseline before the saccade starts and to be used for subsequent filtering boundary transition. The preset duration of the second segment of the event window is preferably 200 to 600 milliseconds to cover the main stage of the appearance of the instantaneous electrical component and the formation of the hysteretic electromechanical component caused by the saccade. Then, the corresponding discrete sampling point sequence is extracted from the frontotemporal EEG signal according to the start and end boundaries as the event window signal. The event window signal is first processed to remove DC and linear trends to suppress the influence of extremely slow drift on filtering. At the same time, the transition samples are increased at both ends of the event window by mirror extension or symmetrical filling to reduce the endpoint distortion introduced by bandpass filtering.

[0027] When performing bandpass filtering with a passband frequency of 10 Hz to 60 Hz on the frontotemporal EEG signal within the event window to obtain the instantaneous electrical component, zero-phase digital filtering is used to avoid phase delay causing deviation in the arrival time localization. The filter is preferably a fourth- to sixth-order Butterworth bandpass filter or an equiripple finite impulse response bandpass filter. The lower limit of 10 Hz is to effectively suppress low-frequency components such as blinking and slow drift at the contact interface while retaining the rapid potential changes related to saccade events. The upper limit of 60 Hz is to retain the main rapid changes while suppressing power frequency and high-frequency noise and adapting to the effective bandwidth of wearable EEG. The high-frequency bandpass output obtained after filtering is defined as the instantaneous electrical component and used for subsequent determination. The instantaneous electrical arrival time and amplitude; when performing bandpass filtering with a passband frequency of 0.1 Hz to 6 Hz on the frontotemporal EEG signal within the event window to obtain the hysteresis electromechanical component, zero-phase digital filtering is also used, and second- to fourth-order Butterworth bandpass filters are preferred to maintain the smoothness of the low-frequency pattern. The lower limit of 0.1 Hz is to exclude slower temperature drift and long-term baseline drift while retaining the slow-changing components related to the dynamic modulation of the electrode contact interface. The upper limit of 6 Hz is to avoid mixing the high-frequency peaks of the instantaneous electrical component into the hysteresis electromechanical component and to make the hysteresis electromechanical component mainly manifest as a low-frequency inflection point and a slow decline pattern. The low-frequency bandpass output obtained after filtering is defined as the hysteresis electromechanical component.

[0028] S4. Determine the arrival time and amplitude of the instantaneous electrical component and the hysteresis electromechanical component respectively, and construct the saccade-triggered frontotemporal electromechanical hysteresis based on the arrival time, the amplitude and the saccade intensity scalar. In embodiments of the present invention, determining the arrival time and amplitude of the instantaneous electrical component and the hysteresis electromechanical component, and constructing a saccade-triggered frontotemporal electromechanical hysteresis based on the arrival time, the amplitude, and the saccade intensity scalar includes: Calculate the energy envelope of the instantaneous electrical component, record the time corresponding to the maximum value of the energy envelope within the first search window as the arrival time of the instantaneous electrical component, and record the square root of the maximum value as the amplitude of the instantaneous electrical component. Calculate the absolute value of the derivative of the hysteresis electromechanical component, and record the time corresponding to the maximum value of the absolute value of the derivative in the second search window as the arrival time of the hysteresis electromechanical component, and record the maximum absolute value of the hysteresis electromechanical component in the second search window as the amplitude of the hysteresis electromechanical component. It should be noted that the arrival time of the instantaneous electrical component refers to the time point within the event window where the instantaneous electrical component, obtained by bandpass filtering with a passband frequency of 10 Hz to 60 Hz, first exhibits the most significant transient response, using the start time of the saccade event as the time anchor. Preferably, it is determined by the time point when the energy envelope of this component reaches its maximum value within a preset search interval after the start time. This is used to characterize the occurrence time of saccade-related rapid potential changes in the frontotemporal EEG channel. The amplitude of the instantaneous electrical component refers to the magnitude of the transient response corresponding to this arrival time. Preferably, the maximum value of the energy envelope within the preset search interval or the peak-to-peak value of the instantaneous electrical component within that interval is taken as the amplitude to characterize the strength of the rapid potential change; hysteresis. The arrival time of the electromechanical component refers to the time point at which the most significant morphological change of the hysteretic electromechanical component, obtained by bandpass filtering with a passband frequency of 0.1 Hz to 6 Hz, begins to appear within the same event window. Preferably, it is determined by the time point at which the absolute value of the rate of change of the component reaches its maximum value within a preset search interval after the start time. This is used to characterize the occurrence time of slow changes caused by soft tissue propagation and dynamic modulation at the electrode skin contact interface in the frontotemporal EEG channel. The amplitude of the hysteretic electromechanical component refers to the intensity of the slow change corresponding to the arrival time. Preferably, the maximum absolute value of the hysteretic electromechanical component within the preset search interval or the maximum offset of its baseline mean relative to the front segment of the event window is taken as the amplitude to characterize the strength of low-frequency drift or inflection point change.

[0029] Specifically, after obtaining the instantaneous electrical component and the hysteresis electromechanical component within the event window, when calculating the energy envelope of the instantaneous electrical component, a Hilbert transform is performed on the instantaneous electrical component as a discrete-time sequence to obtain the corresponding analytic signal. Then, the squares of the real and imaginary parts of the analytic signal are summed to obtain the energy envelope sequence. The energy envelope sequence is used to characterize the transient intensity distribution of the instantaneous electrical component over time. Subsequently, a first search window is set relative to the start time, and the maximum value of the energy envelope sequence is searched within the first search window. The first search window is preferably set to 0 to 80 milliseconds after the start time. This is because the scan-related rapid potential changes usually occur shortly after the event is triggered, and this period can cover the main peak of the instantaneous response while reducing the interference of the hysteresis electromechanical component on the high-frequency envelope. The time corresponding to the maximum value of the energy envelope within the first search window is recorded as the arrival time of the instantaneous electrical component, and the square root of the maximum value is recorded as the amplitude of the instantaneous electrical component so that the amplitude dimension is consistent with the original component amplitude.

[0030] When calculating the absolute value of the derivative of the hysteresis electromechanical component, numerical difference is first performed on the hysteresis electromechanical component to obtain a sequence of first-order derivatives, and the absolute value of these first-order derivatives is then taken to form a sequence of absolute derivative values. The numerical difference preferably uses central difference to reduce noise amplification and improve the stability of arrival time positioning. Then, a second search window is set relative to the starting time, and the maximum value of the absolute derivative value sequence is searched within the second search window. The second search window is preferably set to 80 to 300 milliseconds after the starting time. This is because the hysteresis electromechanical component is a low-frequency, slowly changing component, and its most significant inflection point or rate of change peak usually appears later than the instantaneous electrical component. Simultaneously, by setting the starting point of the second search window later than the first search window… The search window endpoint can avoid misjudging the residual high-frequency components of the instantaneous electrical components as the arrival of hysteresis electromechanical components. The time corresponding to the maximum value of the absolute value of the derivative within the second search window is recorded as the arrival time of the hysteresis electromechanical component. Within the same second search window, the absolute value of the hysteresis electromechanical component is taken and the maximum value is calculated. This maximum absolute value is recorded as the amplitude of the hysteresis electromechanical component to quantify the slow change intensity. Finally, the first search window and the second search window are written into the processor's parameter table as preset time intervals and are equivalently converted according to the number of sampling points as the sampling rate changes. This ensures that both the first search window and the second search window are set relative to the starting time, and the time interval of the first search window is earlier than the time interval of the second search window.

[0031] The normalized hysteresis to electromechanical amplitude ratio is calculated based on the arrival time and amplitude of the instantaneous electrical component and the hysteresis electromechanical component. It should be noted that normalized hysteresis is a metric used to quantify the time lag between the delayed electromechanical component and the instantaneous electrical component induced by the same saccade event in the frontotemporal EEG signal, and to eliminate the influence of differences in saccade intensity. It is obtained by obtaining the electromechanical hysteresis time difference from the arrival time of the delayed electromechanical component and the arrival time of the instantaneous electrical component, and then normalizing this electromechanical hysteresis time difference using the sum of the saccade intensity scalar and a small positive number excluding zero. This suppresses the temporal localization scale variations caused by different saccade amplitudes and the instantaneous actuation strength of extraocular muscles within a comparable range. Within this range, the metric more comprehensively reflects the hysteresis changes caused by the propagation of scalp soft tissue and the dynamic modulation of the electrode skin contact interface; the electromechanical amplitude ratio is a metric used to quantify the intensity ratio of the hysteretic electromechanical component relative to the instantaneous electrical component and to weaken the influence of channel gain and individual conductivity differences. It is obtained by the ratio of the amplitude of the hysteretic electromechanical component to the amplitude of the instantaneous electrical component plus a small positive number after dividing by zero. By comparing the amplitudes of the two types of components relative to each other within the same event window, the proportional relationship between the slow change intensity of the contact interface and the transient electrical response intensity can be characterized.

[0032] Specifically, for the same saccade event and the same frontotemporal EEG channel, the arrival times of the immediate electrical components, the arrival times of the delayed electromechanical components, the amplitudes of the immediate electrical components, and the amplitudes of the delayed electromechanical components are read. Simultaneously, the saccade intensity scalar of that saccade event is read as a normalization factor. Then, the electrical hysteresis time difference is calculated and intensity normalization is performed to obtain the normalized hysteresis. The formula for calculating the normalized hysteresis is as follows:

[0033] In the formula, For normalization lag, To delay the arrival time of the electromechanical components, The difference between the instantaneous electrical components and the time of arrival represents the degree of hysteresis of slow low-frequency changes relative to the high-frequency transient response under the same saccade event. As a scalar measure of saccade intensity, it is divided by the hysteresis time difference because the more intense the saccade, the greater the instantaneous driving force of the extraocular muscles, which simultaneously enhances the overall intensity of the instantaneous electrical component and the hysteresis electromechanical component, and may compress or expand the localization sensitivity at the arrival time. Therefore, normalizing the hysteresis time difference with saccade intensity can reduce the influence of differences in saccade amplitude on the hysteresis measurement, making the normalized hysteresis more accurately reflect the time delay changes caused by electromechanical modulation of the contact interface. To prevent the exclusion of tiny positive zeros, it is preferable to take one-thousandth to one-hundredth, which keeps the denominator positive and does not significantly deviate from the results within the normal saccade intensity range.

[0034] The electromechanical amplitude ratio is the ratio of the intensity of the hysteresis electromechanical component to the instantaneous electrical component. The formula for calculating the electromechanical amplitude ratio is:

[0035] In the formula, The electromechanical amplitude ratio, The amplitude of the hysteresis electromechanical component, The amplitude of the instantaneous electrical component is used. The ratio of the amplitude of the hysteresis electromechanical component to the amplitude of the instantaneous electrical component is adopted because both originate from the same event window and are triggered by the same scanning event in the two-stage response. The ratio can offset some of the common scale changes caused by channel gain, individual skin conductivity differences, and static differences in electrode contact, making the ratio more prominent in reflecting the relative relationship between the strength of dynamic modulation of the contact interface and the strength of transient electrical response. To prevent division by zero of tiny positive numbers, it is also used to avoid the risk of division by zero in cases of extremely small amplitude of instantaneous electrical components or abnormal conditions, and to improve numerical stability.

[0036] The frontotemporal electromechanical hysteresis is determined based on the ratio of normalized hysteresis to electromechanical amplitude. It should be noted that the frontotemporal electromechanical hysteresis refers to a set of quantities used to characterize the relative relationship between electrical transient response and electromechanical hysteresis response after quantifying the two-stage response triggered by saccades in the frontal and temporal EEG channels, with the onset time of the saccade event as the time anchor. The electrical transient response corresponds to the instantaneous electrical component, and the electromechanical hysteresis response corresponds to the delayed electromechanical component. It includes at least the normalized hysteresis obtained by normalizing the difference between the arrival time of the delayed electromechanical component and the arrival time of the instantaneous electrical component through the saccade intensity scalar normalization, and the electromechanical amplitude ratio obtained by relativizing the amplitude of the delayed electromechanical component to the amplitude of the instantaneous electrical component. It can be calculated separately on multiple frontotemporal channels and then aggregated into the result of a single saccade event using robust methods such as the median. This result can highlight the hysteresis characteristics and intensity proportion characteristics formed by the propagation of scalp soft tissue and the dynamic modulation of the electrode skin contact interface caused by saccades while weakening individual differences in skin conductivity and channel gain differences. This can then be used for the subsequent calculation and continuous monitoring of the mental state quantification index.

[0037] Specifically, when constructing the frontotemporal electromechanical delay for a single saccade event, the normalized delay and electromechanical amplitude ratio corresponding to each frontotemporal EEG channel of that saccade event are read. The frontotemporal EEG channels preferably include a prefrontal channel and a temporal channel, and contain at least four channels to cover the spatial differences in the effects of saccade triggering on the frontal and temporal sides and improve robustness. The prefrontal channels can be selected as Fp1 and Fp2, and the temporal channels can be selected as T7 and T8. If the actual number of channels acquired is more than four, the prefrontal neighborhood channel and the temporal neighborhood channel can be included together. Then, the median calculation is performed on the normalized delay of the EEG channels to obtain the normalized delay of a single saccade, and the median calculation is performed on the electromechanical amplitude ratio of the EEG channels to obtain the electromechanical amplitude ratio of a single saccade. This is done under dry electrode or semi-dry electrode conditions. Individual channels may exhibit abnormally large or small normalized hysteresis and electromyographic amplitude ratios due to transient contact instability or local electromyographic interference. Using the median can suppress the influence of abnormal channels on the results and maintain cross-channel consistency without introducing additional threshold screening rules. At the same time, when there are four channels, the median has a natural anti-interference ability against single abnormal channels. Therefore, the normalized hysteresis and electromyographic amplitude ratio of a single saccade are combined in sequence to form the frontotemporal electromyographic hysteresis and written into the event-level results table. The normalized hysteresis of a single saccade is used to characterize the degree of electromyographic hysteresis under that saccade event, and the electromyographic amplitude ratio of a single saccade is used to characterize the intensity ratio of the hysteresis electromyographic change relative to the instantaneous electrical response. The two together constitute the frontotemporal electromyographic hysteresis for subsequent calculation of the mental state quantification index.

[0038] S5. Statistically analyze the frontotemporal electromechanical lag within the preset assessment time window, normalize it in combination with the pre-stored baseline parameters, and calculate the mental state quantitative index. In an embodiment of the present invention, the frontotemporal electromechanical lag within a preset evaluation time window is statistically analyzed, and normalized in combination with pre-stored baseline parameters to calculate a mental state quantification index, including: The truncated mean values ​​of the frontotemporal electromechanical hysteresis corresponding to all saccade events within the preset evaluation time window are statistically analyzed to obtain the mean ratio of the current hysteresis mean to the current amplitude mean. Calculate the ratio of the baseline hysteresis mean in the pre-stored baseline parameters to the current hysteresis mean, and take the logarithm of the ratio to obtain the hysteresis explanation component; Calculate the ratio of the current mean amplitude ratio to the mean baseline amplitude ratio in the pre-stored baseline parameters, and take the logarithm of the ratio to obtain the amplitude ratio interpretation component; It should be noted that the hysteresis explanatory component refers to the quantitative result used to characterize the relative change in the degree of frontotemporal electromechanical hysteresis within the current assessment time window relative to the subject's resting baseline level. It is obtained by logarithmically transforming the ratio of the pre-stored baseline mean hysteresis to the current mean hysteresis. Using the ratio transforms the absolute scale differences caused by variations in scalp conductivity and wearing force among different subjects into relative changes. The logarithmic transformation converts multiplicative changes into an additive scale and makes the increase and decrease numerically closer to symmetry, thus allowing this component to be directly interpreted as the degree of deviation of the current electromechanical hysteresis from the baseline; amplitude. The explanatory component refers to the quantitative result that characterizes the relative change in the intensity of delayed electromechanical changes within the current assessment time window relative to the subject's resting baseline level. It is obtained by logarithmically transforming the ratio of the current amplitude ratio mean to the pre-stored baseline amplitude ratio mean. By comparing the relative intensity relationship between the delayed electromechanical component and the instantaneous electrical component between the baseline and the current state and expressing it on a logarithmic scale, this component can reflect whether the proportion of the slow change intensity at the contact interface relative to the transient response intensity has systematically increased or decreased. Thus, together with the delayed explanatory component, it constitutes the explanatory component of the quantitative index of mental state.

[0039] Specifically, an evaluation time window is set in the processor and updated in a continuous scrolling or fixed segmentation manner. The window length of the evaluation time window is preferably sixty seconds, which can cover a sufficient number of saccade events to form stable statistics and maintain the response speed to short-term fluctuations in mental state, while adapting to the real-time processing load of wearable devices. Within the current evaluation time window, the frontotemporal electromechanical hysteresis of all saccade events is collected, and the normalized hysteresis sequence and the single saccade electromechanical amplitude ratio sequence are extracted respectively. Then, truncated mean statistics are performed on the two sequences respectively. The truncated mean is preferably a two-sided 10% truncated mean, which is based on the fact that transient instability of dry electrode contact or occasional electromyographic bursts may cause abnormal extreme values ​​in the hysteresis of individual saccade events. The truncated mean can remove the pull of extreme values ​​at both ends on the mean and retain the main distribution characteristics, thereby obtaining the current hysteresis mean and the current amplitude ratio mean.

[0040] When calculating the interpretable components by comparing the current mean hysteresis and the current mean amplitude ratio with the pre-stored baseline parameters, the baseline mean hysteresis and the baseline mean amplitude ratio are first read from the memory. These baseline parameters are extracted from eye movement signals and frontotemporal EEG signals collected from the subject at rest, following a process identical to that used in the assessment phase. Specifically, saccadic detection is performed at rest to obtain the start time and intensity scalar of the saccadic event. An event window is extracted from the frontotemporal EEG signal, and the instantaneous electrical component and the hysteresis electromechanical component are obtained respectively. The two components are then determined. After calculating the arrival time and amplitude of the class components, the normalized hysteresis and electromechanical amplitude ratio are calculated. The median aggregation of a single saccade event is used to obtain the frontotemporal electromechanical hysteresis, and a frontotemporal electromechanical hysteresis sequence in resting state is formed in chronological order. The mean of baseline hysteresis and the mean of baseline amplitude ratio are then statistically analyzed using the same truncation mean as in the assessment phase and stored as baseline parameters. The resting state is preferably a state in which the subject is in a relaxed sitting posture and the visual stimulus is stable, and the acquisition duration is preferably 90 to 180 seconds to cover multiple natural fixation and saccade cycles and improve baseline stability.

[0041] After obtaining the current hysteresis mean and the baseline hysteresis mean, the ratio of the baseline hysteresis mean to the current hysteresis mean is calculated, and the logarithm of this ratio is taken to obtain the hysteresis explanatory component. Taking the logarithm transforms the multiplicative change into an additive quantity, which facilitates linear combination with other components and makes proportional changes numerically symmetrical. At the same time, to avoid the instability of the ratio when the denominator is close to zero, a small positive number is preferably added to both the numerator and denominator of the ratio to prevent zeroing. The small positive number to prevent zeroing is preferably between one-thousandth and one-hundredth to balance stability and avoid introducing significant bias. The ratio of the current amplitude ratio mean to the baseline amplitude ratio mean is calculated, and the logarithm of this ratio is taken to obtain the amplitude ratio explanatory component. Similarly, the method of adding a small positive number to the numerator and denominator to prevent zeroing is used to ensure numerical stability, thereby obtaining the hysteresis explanatory component and the amplitude ratio explanatory component for subsequent calculation of the mental state quantification index.

[0042] The mental state quantitative index is obtained by weighted summation of the hysteresis explanatory component and the amplitude ratio explanatory component. It should be noted that the Mental State Quantification Index is a continuous numerical monitoring indicator based on the relative change of the frontotemporal electromechanical lag sequence of the subject within the current assessment time window relative to the resting baseline. It is obtained by weighting the lag explanatory component and the amplitude ratio explanatory component according to preset weights, and outputting a quantitative result between zero and one after range limitation. The lag explanatory component reflects the degree of deviation of the arrival of the lag electromechanical component relative to the arrival of the instantaneous electrical component under the same saccadic triggering condition, and the amplitude ratio explanatory component reflects the degree of deviation of the intensity proportion of the lag electromechanical component relative to the intensity proportion of the instantaneous electrical component. By normalizing the above two types of deviations at the baseline level of the same subject and combining the contributions of the two types of deviations in a weighted form, the Mental State Quantification Index can characterize the trend of physiological state changes related to stress in the subject within the time window in a single numerical form and supports continuous updates and temporal comparisons. At the same time, the output provided by this index is only used as a physiological monitoring and auxiliary assessment indicator.

[0043] Specifically, the hysteresis explanatory component and the amplitude ratio explanatory component are extracted from the calculation results of the current assessment time window, and the two are linearly synthesized to ensure the monotonicity and interpretability of the results. The formula for calculating the mental state quantification index is as follows:

[0044] In the formula, The hysteresis component represents the degree of deviation of the current electromechanical hysteresis from the resting baseline. The amplitude ratio explanation component represents the degree of deviation of the proportion of hysteresis electromechanical change intensity relative to the resting baseline. The weights of the hysteresis-explained components, The magnitude ratio explains the weight of the component. and All are preset constants and preferably satisfy In order to limit the contributions of the two explanatory components to the same scale and facilitate comparison of results between different devices and different subjects, in the absence of individualized calibration, the preferred approach is to... and To maintain equal weighting and integration of the two types of information and avoid human bias, when there is a particular emphasis in a particular scenario, it can be... Set to 0.6 to 0.7 and The values ​​are set to 0.4 to 0.3 to improve sensitivity to hysteresis changes. The weighted summation adopts a linear form because the hysteresis explanatory component and the amplitude ratio explanatory component correspond to the time hysteresis dimension and the intensity proportion dimension, respectively, and both have been converted into a relative change scale with approximately the same dimension through logarithmic ratio. Linear superposition can directly reflect the synthetic effect of two-dimensional changes and maintain an interpretable contribution decomposition. The clip is a saturation limiting function used to limit the output to the range of zero to one to adapt to the display and avoid extreme ratios that cause the index to go out of bounds, thereby obtaining a mental state quantitative index for continuous monitoring and trend analysis.

[0045] The present invention also provides a device for quantitative assessment of mental state, comprising: an eye-tracking acquisition unit for acquiring eye-tracking signals of a subject; an electroencephalogram (EEG) acquisition unit for acquiring frontotemporal EEG signals of a subject; a processor; and a memory connected to the processor, wherein the memory stores instructions that can be executed by the processor, and the instructions, when executed, cause the processor to implement the above-described method.

[0046] like Figure 2 As shown in the figure, this is a schematic diagram of the frontotemporal EEG signal event response with the saccade initiation time as the time anchor point. The horizontal axis represents time, and the vertical axis represents amplitude. The position corresponding to the saccade initiation time is marked as t0. After the saccade is triggered, a high-frequency band response represented by a thin line appears in the earlier time interval. This response corresponds to the instantaneous electrical component and presents as a peak with a narrow pulse shape. Subsequently, a low-frequency band response represented by a thick line appears in the later time interval. This response corresponds to the hysteresis electromechanical component and presents as a slow change process with a broad peak shape. Dashed lines are set at the prominent positions of the instantaneous electrical component and the hysteresis electromechanical component to indicate the arrival time. The time interval between the two dashed lines is defined as the hysteresis Δt, which is used to characterize the time lag relationship between the hysteresis electromechanical component and the instantaneous electrical component. The high-frequency band corresponding to the thin line is preferably 10 Hz to 60 Hz to obtain the instantaneous electrical component, and the low-frequency band corresponding to the thick line is preferably 0.1 Hz to 6 Hz to obtain the hysteresis electromechanical component.

[0047] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for assessing mental health status based on brain-computer interface and eye-tracking, characterized in that, Includes the following steps: S1. Simultaneously acquire the subject's eye movement signals and frontotemporal EEG signals, and time-align the eye movement signals and the frontotemporal EEG signals; S2. Identify saccade events based on aligned eye movement signals, and determine the start time and saccade intensity scalar for each saccade event; S3. Using the start time as the anchor point, extract the event window from the frontotemporal EEG signal and extract the instantaneous electrical component and the delayed electromechanical component. S4. Determine the arrival time and amplitude of the instantaneous electrical component and the hysteresis electromechanical component respectively, and construct the saccade-triggered frontotemporal electromechanical hysteresis based on the arrival time, the amplitude and the saccade intensity scalar. S5. Statistically analyze the frontotemporal electromechanical lag within the preset assessment time window, normalize it in combination with the pre-stored baseline parameters, and calculate the mental state quantitative index.

2. The method for assessing mental health status based on brain-computer interface and eye tracking according to claim 1, characterized in that, Based on the aligned eye movement signals, saccade events are identified, and the start time and saccade intensity scalar of each saccade event are determined, including: Calculate the angular velocity and angular acceleration sequences of the eye movement signals; An adaptive threshold is calculated based on the statistical characteristics of the angular velocity sequence. When the absolute value of the angular velocity sequence first crosses the adaptive threshold, it is identified as the start time of the saccadic event. Within a preset scanning duration window after the start time, the maximum absolute value of the angular acceleration sequence is extracted and used as the scanning intensity scalar.

3. The method for assessing mental health status based on brain-computer interface and eye tracking according to claim 1, characterized in that, Using the initial moment as the anchor point, an event window is extracted from the frontotemporal EEG signal, and the immediate electrical component and the delayed electromechanical component are extracted, including: For each saccade event, an event window containing a preset duration before and after the start time is extracted from the frontotemporal EEG signal; Bandpass filtering with a passband frequency of 10Hz to 60Hz was performed on the frontotemporal EEG signals within the event window to obtain the instantaneous electrical components; Bandpass filtering with a passband frequency of 0.1 Hz to 6 Hz is performed on the frontotemporal EEG signal within the event window to obtain the hysteresis electromechanical component.

4. The method for assessing mental health status based on brain-computer interface and eye tracking according to claim 1, characterized in that, Determine the arrival time and amplitude of the instantaneous electrical component and the hysteresis electromechanical component, including: Calculate the energy envelope of the instantaneous electrical component, record the time corresponding to the maximum value of the energy envelope within the first search window as the arrival time of the instantaneous electrical component, and record the square root of the maximum value as the amplitude of the instantaneous electrical component. Calculate the absolute value of the derivative of the hysteresis electromechanical component, and record the time corresponding to the maximum value of the absolute value of the derivative in the second search window as the arrival time of the hysteresis electromechanical component, and record the maximum absolute value of the hysteresis electromechanical component in the second search window as the amplitude of the hysteresis electromechanical component. Both the first search window and the second search window are set relative to the start time, and the time interval of the first search window is earlier than the time interval of the second search window.

5. The method for assessing mental health status based on brain-computer interface and eye tracking according to claim 1, characterized in that, Construct the frontotemporal electromechanical hysteresis triggered by saccades, including calculating the ratio of normalized hysteresis to electromechanical amplitude; The formula for calculating the normalized hysteresis is: The formula for calculating the electromechanical amplitude ratio is: In the formula, For the first The second scanning incident, For the first One brainwave channel, For normalization lag, The electromechanical amplitude ratio, For the instantaneous arrival time of the electrical component, To delay the arrival time of the electromechanical components, The amplitude of the instantaneous electrical component, The amplitude of the hysteresis electromechanical component, To scan the intensity scalar, This is a preset value to prevent the division of small positive numbers into zero; The frontotemporal electromechanical hysteresis is determined based on the ratio of normalized hysteresis to electromechanical amplitude.

6. The method for assessing mental health status based on brain-computer interface and eye tracking according to claim 5, characterized in that, The frontotemporal electromechanical hysteresis is determined based on the ratio of normalized hysteresis to electromechanical amplitude, including: For a single saccade event, obtain the normalized delay to electromechanical amplitude ratio of the EEG channel; The median of the normalized delay of the EEG channels was used to obtain the normalized delay of a single saccade. The median of the electromechanical amplitude ratios of the brainwave channels is used to obtain the electromechanical amplitude ratio of a single saccade. The frontotemporal electromechanical hysteresis is formed by combining the normalized hysteresis of a single saccade with the electromechanical amplitude ratio of a single saccade.

7. The method for assessing mental health status based on brain-computer interface and eye tracking according to claim 1, characterized in that, The frontotemporal electromechanical lag within the preset assessment time window is statistically analyzed, and normalized in conjunction with pre-stored baseline parameters to calculate a quantitative index of mental state, including: The truncated mean values ​​of the frontotemporal electromechanical hysteresis corresponding to all saccade events within the preset evaluation time window are statistically analyzed to obtain the mean ratio of the current hysteresis mean to the current amplitude mean. Calculate the ratio of the baseline hysteresis mean in the pre-stored baseline parameters to the current hysteresis mean, and take the logarithm of the ratio to obtain the hysteresis explanation component; Calculate the ratio of the current mean amplitude ratio to the mean baseline amplitude ratio in the pre-stored baseline parameters, and take the logarithm of the ratio to obtain the amplitude ratio interpretation component; The mental state quantitative index is obtained by weighted summation of the hysteresis explanatory component and the amplitude ratio explanatory component.

8. The method for assessing mental health status based on brain-computer interface and eye tracking according to claim 7, characterized in that, The acquisition of the baseline parameters includes: Eye movement signals and frontotemporal EEG signals were collected from subjects in a resting state, and the frontotemporal electromechanical lag sequence was extracted in a resting state. The frontotemporal electromechanical hysteresis sequence under resting state is statistically analyzed to obtain the mean baseline hysteresis and the mean baseline amplitude ratio, which are then stored as the baseline parameters.

9. A device for quantitatively assessing mental state, characterized in that, include: An eye-tracking acquisition unit is used to acquire the subject's eye-tracking signals; The EEG acquisition unit is used to acquire frontotemporal EEG signals from the subject. A processor, and a memory connected to the processor; wherein the memory stores instructions executable by the processor, which, when executed, cause the processor to perform the method as described in any one of claims 1 to 8.

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