A method and system for decoding spatial distance perception based on lingual gyrus neural activity

Abstract

This invention discloses a spatial distance perception decoding method and system based on lingual gyrus neural activity, relating to the fields of neurophysiological signal processing and brain function decoding. The method includes presenting visual stimuli containing different spatial interaction distances to the subject; recording continuous EEG signal data of the lingual gyrus during the subject's spatial distance perception task; preprocessing and performing time-frequency analysis on the EEG signal data to extract the rate of change of neural oscillation energy within a specific time window in a specific frequency band as a core feature variable; and constructing a decoding model based on the correlation between the core feature variable and spatial interaction distance, thereby outputting a prediction result of the individual's current spatial distance perception state. This invention achieves a direct mapping from underlying neural signals to spatial interaction distance perception, enabling the quantification and decoding of an individual's spatial distance perception state, and providing reliable technical support for the assessment of spatial perception-related diseases and human-computer interaction applications.

Description

Technical Field

[0001] This invention relates to the field of neurophysiological signal processing and brain function decoding technology, and in particular to a spatial distance perception decoding method and system based on the activity of the lingual gyrus nerve. Background Technology

[0002] In human social interactions, individuals can quickly judge the spatial distance between themselves and others based on visual information. This ability is crucial for social behavior regulation, spatial navigation, and situational understanding. In clinical psychology, abnormal spatial distance perception (such as perceiving others as too close or too far) is considered an important behavioral characteristic of mental illnesses such as social anxiety and autism spectrum disorder. Furthermore, in applications such as virtual reality (VR), augmented reality (AR), and human-computer interaction, achieving adaptive interaction based on the user's neural state also requires the objective and real-time quantification of the individual's spatial distance perception state.

[0003] Cognitive neuroscience research suggests that the brain may share some neural computation mechanisms when processing physical spatial distance and psychological or social distance. Among these mechanisms, the lingual gyrus, located in the occipital-temporal lobe of the brain, is believed to be involved in visual scene processing, spatial information representation, and depth perception. Previous studies have suggested that this brain region may exhibit related neural activity changes during interpersonal assessments of social distance or intimacy.

[0004] However, existing research on specific brain regions such as the lingual gyrus relies heavily on non-invasive neuroimaging techniques, which have significant limitations: while functional magnetic resonance imaging has high spatial resolution, its temporal resolution is usually on the order of seconds, making it difficult to capture the dynamic neural response process of the brain to spatial distance perception on the order of milliseconds; while scalp electroencephalography has high temporal resolution, its spatial resolution is limited, making it difficult to accurately locate the electrophysiological activity of deep cortical layers (such as the lingual gyrus).

[0005] Stereotactic electroencephalography (SEEG), as an invasive EEG recording technique, can directly record local field potentials (LFP) from the cerebral cortex, with both millimeter-level spatial resolution and millisecond-level temporal resolution, providing a technical basis for studying the dynamic role of specific brain regions in spatial distance perception.

[0006] However, existing technologies still lack methods for quantitatively decoding spatial distance perception based on the neural activity characteristics of specific brain regions, and a complete technical path from neural signals to distance perception results has not yet been formed. Summary of the Invention

[0007] To address the shortcomings of existing technologies, the present invention aims to propose a spatial distance perception decoding method and system based on the activity of the lingual gyrus nerve. The method uses intracranial stereotactic electroencephalography (SEEG) to record the local field potential signal of the lingual gyrus when the subject performs a spatial distance perception task. Through time-frequency feature processing and core feature extraction, a decoding prediction model between neurophysiological features and spatial interaction distance is constructed, thereby achieving objective quantification and decoding of an individual's spatial distance perception state.

[0008] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0009] A spatial distance perception decoding method based on lingual gyrus nerve activity includes: Stimulus presentation: Presenting visual stimuli with different spatial interaction distances to the subjects and obtaining their behavioral responses to the visual stimuli; Intracranial electroencephalography (EEG) recording: Recording continuous EEG signal data of the lingual gyrus of the subject while performing a spatial distance perception task; Lingual gyrus feature processing and extraction: The continuous EEG signal data of the lingual gyrus is preprocessed and segmented. The change rate of neural oscillation energy in different frequency bands is calculated using time-frequency analysis. The change rate of neural oscillation energy in a specific frequency band within a specific time window is extracted as the core feature variable. Spatial distance decoding and prediction: A decoding model is constructed based on the correlation between the extracted core feature variables and the spatial interaction distance to achieve quantitative decoding and prediction of an individual's spatial distance perception state.

[0010] The following is a further technical solution for the method of the present invention: in the presentation of stimuli, the visual stimuli presented are interpersonal interaction scenarios, and the spatial interaction distance is set to 1 meter, 2 meters, 4 meters and 6 meters.

[0011] The following is a further technical solution for the method of this invention: the subject is an epilepsy patient who needs to have a deep intracranial electrode implanted during clinical treatment, and during the absence of epileptic seizures, the spontaneous neural activity of the brain is basically consistent with that of a healthy individual; in the intracranial electroencephalography recording, the intracranial stereotactic electroencephalography (SEEG) technology is used to directly collect the local field potential signal of the implantation area through the deep electrode implanted in the lingual gyrus region of the subject; the local field potential reflects the comprehensive electrical activity of the neuronal population in the millimeter-level spatial range near the electrode contact on the millisecond-level time scale.

[0012] The following is a further technical solution for the method of the present invention: in the processing and extraction of lingual gyrus features, the continuous EEG signal of the lingual gyrus is filtered, including high-pass filtering, low-pass filtering and notch filtering, and the sampling rate is reduced. Based on the event label, with the moment the visual stimulus is presented as the zero point, a data segment is extracted within a preset time window before and after it as a trial. Each data segment is checked frame by frame according to a set time length to determine whether the data is contaminated by epileptic waves. Data contaminated by epileptic waves or other artifacts is removed, and uncontaminated trial data is retained. Time-frequency analysis was performed on the uncontaminated trial data using complex Morlet wavelet transform to obtain the neural oscillation power at each time point across the entire frequency band. Based on the time-frequency analysis results under different spatial interaction distances (i.e., the neural oscillation power at each time point in the full frequency range), the neural oscillations are divided into 8 specific frequency bands, including: the delta band of 2-4Hz, the theta band of 5-7Hz, the alpha band of 8-12Hz, the low-beta band of 13-19Hz, the high-beta band of 20-29Hz, the low-gamma band of 30-59Hz, the high-gamma band of 60-89Hz, and the ultra-high frequency (UHF) band of 90-200Hz. Using the time period before visual stimulation as a baseline, the relative changes in power of each specific frequency band are calculated to obtain the rate of change of neural oscillation energy. Wherein, the rate of change of neural oscillation energy = [(energy at the corresponding time point after stimulation - baseline energy before stimulation) / baseline energy before stimulation] 100%; The rate of change of neural oscillation energy at different time points in each specific frequency band was extracted at 50 ms intervals and used as the initial feature set. Statistical tests were performed on the initial feature set to calculate whether the rate of change of neural oscillation energy in different frequency bands within each time interval could effectively encode spatial interaction distance. The statistical test specifically involves using the Spearman correlation test, and setting a statistically significant difference threshold. p <0.01; Specific frequency bands and corresponding time windows that meet the significance level threshold are selected, and their neural oscillation energy change rate is used as the core feature. The core feature variable extracted was the rate of change of neural oscillation energy in the low-beta band of 13-19 Hz within a time window of 150 ms to 300 ms after the presentation of visual stimuli. Within this specific time window and frequency band, the rate of change of neural oscillation energy in the lingual gyrus showed a statistically significant correlation with the spatial interaction distance, and the correlation increased positively with the increase of the spatial interaction distance.

[0013] The following is a further technical solution for the method in this invention: In spatial distance decoding prediction, a decoding model of spatial distance perception state is established based on the extracted core feature variables. The core feature variables are used as independent variables and spatial interaction distance is used as dependent variables to obtain the mapping relationship between the two. Based on the mapping relationship, the prediction result of the individual's current spatial distance perception state is output.

[0014] A spatial distance perception decoding system based on lingual gyrus nerve activity includes: The stimulus presentation module presents visual stimuli to the subjects, including those at different spatial interaction distances; The intracranial EEG recording module records continuous EEG signal data of the lingual gyrus when the subject performs a spatial distance perception task; The lingual gyrus feature processing and extraction module preprocesses and segments the continuous EEG signal data of the lingual gyrus, calculates the rate of change of neural oscillation energy in different frequency bands using time-frequency analysis, and extracts the rate of change of neural oscillation energy in a specific frequency band within a specific time window as the core feature variable. The spatial distance decoding and prediction module constructs a decoding model based on the correlation between the extracted core feature variables and spatial interaction distance, so as to realize the quantitative decoding and prediction of an individual's spatial distance perception state.

[0015] Compared with the prior art, the present invention has the following technical effects: This invention directly records the local field potential signals in the lingual gyrus region when subjects perform spatial distance perception tasks using intracranial stereotactic electroencephalography (EEG). It extracts key time-frequency features related to spatial interaction distance, identifies specific frequency band neural oscillation features that can be used to characterize the spatial distance perception state, and establishes a mapping relationship between lingual gyrus neural activity and spatial distance perception state. This enables the quantitative decoding of an individual's spatial distance perception state, providing technical support for research on brain mechanisms related to spatial distance perception, disease assessment, and human-computer interaction applications.

[0016] The present invention will be further described below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is a system schematic diagram of the present invention; Figure 2 This is a schematic diagram of the stimulus presentation process of the present invention; Figure 3 This is a schematic diagram of a two-person scene under different spatial distance conditions according to the present invention; Figure 4 This is a schematic diagram of the spatial distance decoding result based on the core features of the present invention. Detailed Implementation

[0019] To make the above-mentioned objects, features, and advantages of the present invention more apparent and understandable, specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of the present invention. However, the present invention can be practiced in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0020] like Figure 1 As shown, a spatial distance perception decoding system based on lingual gyrus neural activity includes a stimulus presentation module, an intracranial electroencephalogram recording module, a lingual gyrus feature processing and extraction module, and a spatial distance decoding prediction module.

[0021] A spatial distance perception decoding method based on lingual gyrus nerve activity, specifically including: Stimulus presentation: Present subjects with visual stimuli of interpersonal interaction scenarios with different spatial interaction distances, and obtain the subjects' behavioral responses to the visual stimuli.

[0022] In this embodiment, each trial includes a gaze cueing phase, a scene presentation phase, and a behavioral response phase. For example... Figure 2 As shown, firstly, a fixation point is presented in the center of the screen to guide the subject to focus their attention; then, a visual stimulus of a two-person scene is presented; after the two-person scene disappears, a color cue mark is presented in the center of the screen, and the subject judges whether the two characters in the previous scene are in an interactive state according to the button rules corresponding to the color cue mark and completes the button response.

[0023] In this embodiment, the visual stimuli for the interpersonal interaction scene include a two-person scene image presented in the center of the screen, such as... Figure 3 As shown, the scene includes two virtual characters standing in different postures, with their relative spatial interaction distances set at 1 meter, 2 meters, 4 meters, and 6 meters. Subjects completed 960 trials throughout the experiment, with all four spatial interaction distances presented repeatedly in a balanced manner. To reduce the interference of visual attributes unrelated to the target variable on spatial distance perception, the race, gender, clothing color, and orientation of the characters were balanced when constructing the visual stimuli for the interpersonal interaction scene, ensuring that various non-target visual attributes were presented in a balanced combination under different spatial interaction distance conditions.

[0024] Intracranial electroencephalography (EEG) recording: The subjects were epilepsy patients requiring deep intracranial electrodes for clinical treatment, and their spontaneous neural activity was considered to be largely consistent with that of healthy individuals during seizure-free periods. Local field potentials (LFP) signals in the lingual gyrus region were directly recorded using implanted stereotactic electroencephalography (SEEG) deep electrodes while the subjects performed spatial distance perception tasks. The LFP reflects the integrated electrical activity of neuronal populations within a millimeter-scale spatial range near the electrode contact point on a millisecond-scale timescale.

[0025] Lingual gyrus feature processing and extraction: The recorded continuous EEG signals were preprocessed and feature transformed. First, the continuous EEG signals were sequentially subjected to a 0.5 Hz high-pass filter, a 200 Hz low-pass filter, and a 50 Hz notch filter to remove environmental and mains noise, and the data was downsampled to 1000 Hz. Then, based on the event label, the time point of the visual stimulus image presentation was used as the time zero point, and a segment of EEG signal data containing 1000 ms before and 2000 ms after the visual stimulus presentation was extracted as a trial. Each segment of data was examined frame by frame, and bad trials contaminated by intermittent spikes, abnormal synchronous discharges, or other forms of artifacts were labeled and removed, retaining only clean trials that were not contaminated.

[0026] Time-frequency analysis was performed on the retained clean trial data using Complex Morlet wavelet transform to dynamically characterize the neural activity of the lingual gyrus during spatial distance perception, acquiring the neural oscillation power of the signal at various time points across the entire frequency band. To balance computational efficiency and temporal dynamic accuracy, the time resolution of the time-frequency analysis was set to 10 ms. Based on frequency domain characteristics, the analysis frequency band was divided into several specific intervals, including: the delta band (2-4 Hz), the theta band (5-7 Hz), the alpha band (8-12 Hz), the low-beta band (13-19 Hz), the high-beta band (20-29 Hz), the low-gamma band (30-59 Hz), the high-gamma band (60-89 Hz), and the ultra-high frequency (UHF) band (90-200 Hz).

[0027] To accurately capture the neural dynamic response induced by spatial visual stimuli, baseline correction was further performed on the aforementioned time-frequency analysis results. Specifically, the time period from 500 ms before the visual stimulus presentation to the stimulus presentation time was defined as the baseline period, and the rate of change of neural oscillation energy at different time points in each frequency band relative to the average energy of the baseline period was calculated. The specific formula is: Rate of change of neural oscillation energy = [(Energy at the corresponding time point after stimulation - Baseline energy before stimulation) / Baseline energy before stimulation] 100%. Subsequently, at 50 ms intervals, the rate of change of neural oscillation energy for each specific frequency band within different time windows was extracted as the initial feature set. Spearman correlation test was used to calculate the correlation between the rate of change of neural oscillation energy for different frequency bands within each time interval and the spatial interaction distance, with a statistically significant difference threshold set at [value missing]. p <0.01, specific frequency bands and corresponding time windows that meet this threshold are selected as core features. The results show that within a time window of 150ms to 300ms after the presentation of visual stimuli, the rate of change of neural oscillation energy in the 13-19 Hz low-beta band is significantly positively correlated with the increase of spatial interaction distance.

[0028] Finally, based on the core features obtained through screening, a decoding model for spatial distance perception is further constructed. Specifically, the low-beta neural oscillation energy change rate in the 13-19 Hz frequency band within a time window of 150 ms to 300 ms after visual stimulus presentation is extracted from each original trial. An ensemble averaging method is used to merge multiple original trials into an ensemble sample to reduce random noise in single-trial data and improve feature stability. In this embodiment, every 20 original trials are merged into one ensemble sample. Subsequently, a linear regression decoding model is established using the neural oscillation energy change rate corresponding to each ensemble sample as the independent variable and its corresponding spatial interaction distance as the dependent variable. Figure 4 As shown, the fitted decoding equation is: Spatial interaction distance = 0.0256 × rate of change of neural oscillation energy + 1.6547. The model's coefficient of determination... R The value of ² was 0.5206, which was statistically significant. p = 6.87 × 10 -7 This indicates that there is a significant linear mapping relationship between core features and spatial interaction distance, which can be used to output the prediction results corresponding to the subject's current spatial distance perception state.

[0029] Various aspects of this disclosure are described herein with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It should be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer-readable program instructions.

[0030] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention in any way. Any person skilled in the art can make many possible variations and modifications to the technical solution of the present invention, or modify it into equivalent embodiments, without departing from the scope of the present invention's technical solution. Therefore, all equivalent changes made based on the shape, structure, and principle of the present invention without departing from the scope of the present invention's technical solution should be covered within the protection scope of the present invention.

Claims

1. A spatial distance perception decoding method based on lingual gyrus nerve activity, characterized in that, include: Stimulus presentation: Presenting visual stimuli with different spatial interaction distances to the subjects and obtaining their behavioral responses to the visual stimuli; Intracranial electroencephalography (EEG) recording: Recording continuous EEG signal data of the lingual gyrus of the subject while performing a spatial distance perception task; Lingual gyrus feature processing and extraction: The continuous EEG signal data of the lingual gyrus is preprocessed and segmented. The change rate of neural oscillation energy in different frequency bands is calculated using time-frequency analysis. The change rate of neural oscillation energy in a specific frequency band within a specific time window is extracted as the core feature variable. Spatial distance decoding and prediction: A decoding model is constructed based on the correlation between the extracted core feature variables and the spatial interaction distance to achieve quantitative decoding and prediction of an individual's spatial distance perception state.

2. The spatial distance perception decoding method based on lingual gyrus nerve activity as described in claim 1, characterized in that, In the stimulus presentation, the visual stimuli presented are interpersonal interaction scenarios, and the spatial interaction distances are set to 1 meter, 2 meters, 4 meters, and 6 meters.

3. The spatial distance perception decoding method based on lingual gyrus nerve activity as described in claim 1, characterized in that, In intracranial electroencephalography (EEG) recording, intracranial stereotactic EEG technology is used to directly acquire local field potential signals in the implanted area through deep electrodes implanted in the lingual gyrus region of the subject. The local field potential reflects the comprehensive electrical activity of neuronal populations in the millisecond timescale within a millimeter spatial range near the electrode contact.

4. The spatial distance perception decoding method based on lingual gyrus nerve activity as described in claim 1, characterized in that, In the processing and extraction of lingual gyrus features, the continuous EEG signal of the lingual gyrus is filtered, including high-pass filtering, low-pass filtering and notch filtering, and the sampling rate is reduced. Based on the event label, with the moment the visual stimulus is presented as the zero point, a data segment is extracted within a preset time window before and after it as a trial. Each data segment is checked frame by frame according to a set time length to determine whether the data is contaminated by epileptic waves. Data contaminated by epileptic waves or other artifacts is removed, and uncontaminated trial data is retained. Time-frequency analysis was performed on uncontaminated trial data using complex Morlet wavelet transform to obtain the neural oscillation power at each time point across the entire frequency band.

5. The spatial distance perception decoding method based on lingual gyrus nerve activity as described in claim 4, characterized in that, Based on the neural oscillation power at various time points across the entire frequency range, neural oscillations are divided into 8 specific frequency bands, including: the delta band of 2-4Hz, the theta band of 5-7Hz, the alpha band of 8-12Hz, the low-beta band of 13-19Hz, the high-beta band of 20-29Hz, the low-gamma band of 30-59Hz, the high-gamma band of 60-89Hz, and the ultra-high frequency band of 90-200Hz. Using the time period before visual stimulation as a baseline, the relative changes in power of each specific frequency band are calculated to obtain the rate of change of neural oscillation energy. Wherein, the rate of change of neural oscillation energy = [(energy at the corresponding time point after stimulation - baseline energy before stimulation) / baseline energy before stimulation] 100%.

6. The spatial distance perception decoding method based on lingual gyrus nerve activity as described in claim 5, characterized in that, At certain time intervals, the rate of change of neural oscillation energy at different time points in each specific frequency band is extracted and used as the initial feature set; Statistical tests were performed on the initial feature set to calculate the correlation between the rate of change of neural oscillation energy in different frequency bands and the spatial interaction distance in each time interval. Specific frequency bands and corresponding time windows that meet the set significant difference level threshold are selected, and their neural oscillation energy change rate is used as the core feature.

7. The spatial distance perception decoding method based on lingual gyrus nerve activity as described in claim 6, characterized in that, The core feature variable finally extracted is the rate of change of neural oscillation energy in the low-beta band of 13-19Hz within a time window of 150 ms to 300 ms after the presentation of visual stimuli.

8. The spatial distance perception decoding method based on lingual gyrus nerve activity as described in claim 1, characterized in that, In spatial distance decoding prediction, a decoding model of spatial distance perception state is established based on the extracted core feature variables. The core feature variables are used as independent variables and spatial interaction distance is used as dependent variables to obtain the mapping relationship between the two. Based on the mapping relationship, the prediction result of the individual's current spatial distance perception state is output.

9. A spatial distance perception decoding system based on lingual gyrus nerve activity, used to implement the spatial distance perception decoding method based on lingual gyrus nerve activity as described in any one of claims 1-8, characterized in that, include: The stimulus presentation module presents visual stimuli to the subjects, including those at different spatial interaction distances; The intracranial EEG recording module records continuous EEG signal data of the lingual gyrus when the subject performs a spatial distance perception task; The lingual gyrus feature processing and extraction module preprocesses and segments the continuous EEG signal data of the lingual gyrus, calculates the rate of change of neural oscillation energy in different frequency bands using time-frequency analysis, and extracts the rate of change of neural oscillation energy in a specific frequency band within a specific time window as the core feature variable. The spatial distance decoding and prediction module constructs a decoding model based on the correlation between the extracted core feature variables and spatial interaction distance, so as to realize the quantitative decoding and prediction of an individual's spatial distance perception state.

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