Intelligent intervention system for children with infantile autism spectrum disorder based on multi-modal neural feedback

Abstract

The invention discloses an intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback, which comprises a multi-modal physiological signal acquisition module for acquiring electroencephalogram signals of a target child in real time and generating an original physiological signal set; the multi-modal fusion analysis module receives the original physiological signal set and generates a comprehensive evaluation signal representing the current neurocognitive state and the emotion awakening level of the child; the self-adaptive decision control module receives the comprehensive evaluation signal and generates a self-adaptive control signal containing a neural feedback parameter adjustment instruction and a game interaction strategy instruction; the neural feedback intervention module receives the neural feedback parameter adjustment instruction and generates an adaptive neural feedback stimulation signal to act on the child; and the intelligent interactive game module receives a game interactive strategy instruction and dynamically adjusts game scene contents. The intelligent intervention system for children with autism spectrum disorder based on multi-modal neural feedback can solve the problems of poor individual adaptation of autism intervention, multi-modal data splitting and lack of dynamic adjustment.

Description

Technical Field

[0001] This invention relates to the field of intelligent diagnostic and treatment equipment for autism and the field of neurofeedback technology, specifically to an intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback. Background Technology

[0002] Children with autism spectrum disorder (ASD) exhibit core symptoms such as social communication impairments and stereotyped behaviors. While existing neurofeedback interventions can modulate brain function, they have significant limitations: traditional systems rely on single EEG signals, failing to capture related states such as distraction or anxiety in children, leading to off-target training; parameter settings depend on therapist experience, making it difficult to adjust the optimal approach in real time for the highly heterogeneous autism population, affecting the stability of treatment outcomes; and the monotonous feedback format results in poor compliance among children, with an average dropout rate exceeding 35%. Although recent studies have attempted to combine multimodal data such as eye movements or electrodermal activity (EDA), most use simple threshold alarms or parallel displays, failing to establish cross-modal state mapping models and thus unable to form a joint decision-making basis.

[0003] In the area of ​​gamified intervention, existing technologies often mechanically superimpose pre-set games with neurofeedback, disconnecting the game process from the EEG training objectives. For example, in social tasks, the need for eye contact is not designed to link with enhancing brain activity in social regions. Furthermore, there is a lack of lightweight hardware support in home settings; clinical-grade EEG devices require conductive gel and complex wiring, leading to poor tolerance in children. Patent searches reveal that while multimodal autism intervention frameworks have been proposed, their decision-making modules only implement rule-based matching and do not incorporate adaptive algorithms such as reinforcement learning. Although the system integrates VR games, game strategy adjustments lag behind physiological changes by more than 20 seconds, compromising the real-time nature of the intervention. The academic community urgently needs to construct a new generation of intervention systems that balance scientific rigor and compliance through deep multimodal fusion and dynamic closed-loop control using artificial intelligence. Summary of the Invention

[0004] In view of the shortcomings of the prior art, the purpose of this invention is to provide an intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback, to address the problems of poor individual adaptation in autism intervention, fragmented multimodal data, and lack of dynamic adjustment. This invention generates a comprehensive neurocognitive-emotional assessment signal through multimodal physiological signal fusion analysis, driving an artificial intelligence decision engine to dynamically generate two types of instructions: neurofeedback parameter instructions, which adjust the target EEG bands, thresholds, and multisensory feedback forms in real time; and game strategy instructions, which adapt to the type of social deficit by generating virtual scenarios and adjusting interaction rules. Ultimately, through closed-loop feedback between neural stimulation and game behavior, the intervention strategy is adaptively optimized according to the child's state.

[0005] This invention provides an intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback, comprising:

[0006] The multimodal physiological signal acquisition module collects the target child's electroencephalogram (EEG) signals, eye-tracking signals, and skin conductance signals in real time, generating a raw physiological signal set.

[0007] The multimodal fusion analysis module receives the raw physiological signal set, performs feature extraction and fusion calculation, and generates a comprehensive assessment signal that represents the child's current neurocognitive state and emotional arousal level.

[0008] The adaptive decision control module receives comprehensive evaluation signals and generates adaptive control signals that include neural feedback parameter adjustment instructions and game interaction strategy instructions based on preset intervention goals and artificial intelligence algorithms.

[0009] The neurofeedback intervention module receives instructions to adjust neurofeedback parameters, and adjusts the target EEG band, feedback threshold, or feedback presentation format in real time to generate appropriate neurofeedback stimulation signals for children.

[0010] The intelligent interactive game module receives game interaction strategy instructions, dynamically adjusts the game scene content, difficulty, or interaction rules, and generates adapted interactive game content.

[0011] In one embodiment of the present invention, the multimodal physiological signal acquisition module integrates a non-invasive EEG sensing unit, an infrared eye-tracking unit, and a skin conductance sensing unit; the EEG sensing unit acquires EEG signals from the prefrontal and parietal lobe regions of children through a flexible electrode array; the eye-tracking unit captures pupil movement trajectories through a miniature camera to generate visual attention distribution signals; the skin conductance sensing unit detects changes in sweat gland activity through a fingertip sensor to generate emotional arousal signals; the three types of sensing signals are synchronized and time-aligned to form a raw physiological signal set which is then transmitted to the multimodal fusion analysis module.

[0012] In one embodiment of the present invention, the multimodal fusion analysis module performs a hierarchical feature processing flow. First, it performs time-frequency transformation on the EEG signal to extract specific frequency band energy features, performs gaze hotspot clustering analysis on the eye movement signal to extract attention focus distribution features, and performs differential processing on the skin conductance signal to extract skin conductance response amplitude features. Then, it uses an attention mechanism weighted deep learning model to map the three types of heterogeneous features to a unified latent space, and generates a composite vector containing neurocognitive load score, emotional valence index and social engagement index as a comprehensive evaluation signal through cross-modal correlation learning.

[0013] In one embodiment of the present invention, the adaptive decision control module incorporates a reinforcement learning framework. This framework takes a comprehensive evaluation signal as the state input and neural feedback parameter adjustment instructions and game interaction strategy instructions as the action output. It quantifies the intervention effect by designing a multi-objective reward function, which includes the achievement rate of EEG target bands, the completion rate of game tasks, and the negative emotion avoidance coefficient. After each intervention cycle, the system updates the state transition probability model based on the child's physiological behavioral response and dynamically optimizes the strategy network parameters to achieve adaptive decision-making for individual differences among children with autism.

[0014] In one embodiment of the present invention, the neurofeedback intervention module is configured with a multi-channel feedback engine. When receiving a neurofeedback parameter adjustment instruction, it selects a sensorimotor rhythm or a mirror neuron-related frequency band as the reinforcement target according to the target band identifier in the instruction, dynamically adjusts the EEG amplitude to meet the threshold judgment boundary according to the threshold parameter in the instruction, and generates a multi-sensory fusion neurofeedback stimulation signal by selecting at least two of the following methods from the presentation form parameter in the instruction: visual animation intensity gradient, auditory tone modulation mode, or tactile vibration frequency combination.

[0015] In one embodiment of the present invention, the intelligent interactive game module includes a social scenario simulation engine. After receiving the game interaction strategy instruction, the engine calls three types of scenario templates—virtual classroom, family interaction, or peer collaboration—based on the scenario identifier parameters in the instruction. Based on the difficulty parameters, it adjusts the complexity of the virtual character's facial expressions, the number of dialogue rounds, or the time constraints of collaborative tasks. Based on the interaction rule parameters, it sets eye contact triggering mechanisms, voice response delay tolerance, or body movement feedback rules, and finally generates adaptive interactive content that conforms to the social ability development stage of children with autism.

[0016] In one embodiment of the present invention, the deep learning model adopts a cascaded architecture of a temporal convolutional network and a long short-term memory network. The temporal convolutional network is responsible for extracting spatial topological features from EEG signals, and the long short-term memory network is responsible for modeling the temporal dependency between eye movement trajectory and skin conductance changes. During the model training phase, a cross-child transfer learning mechanism is introduced. After pre-training the feature extraction layer using data from typical developmental children, an adaptive domain alignment algorithm is used to fine-tune the classification layer parameters on data from children with autism to address the problem of scarce individual data for spectrum disorders.

[0017] In one embodiment of the present invention, the reinforcement learning framework uses a proximal policy optimization algorithm to implement policy iteration, and sets up a dual experience replay buffer to store positive reinforcement samples and negative reinforcement samples respectively; when the child is detected to have anxious behavior characteristics, negative reinforcement samples are sampled first to update the policy network, and when the duration of social interaction is detected to be increasing, positive reinforcement samples are sampled first to update the policy network, so as to balance the dual goals of behavior shaping and emotional stability in the intervention process.

[0018] In one embodiment of the present invention, a multi-channel feedback engine realizes spatially specific stimulus mapping. When the target band is the prefrontal alpha band, it generates a top-down visual optical flow animation stimulus. When the target band is the central region μ band, it generates a left-to-right stereo sweep frequency sound stimulus. When multiple brain regions are strengthened at the same time, it triggers gradient temperature and tactile stimulation at the corresponding scalp location. The three modal stimuli are kept in phase synchronization through a time coding mechanism to enhance the induction effect of neural plasticity.

[0019] In one embodiment of the present invention, the social scenario simulation engine integrates a generative adversarial network architecture. After receiving game interaction strategy instructions, the generator synthesizes a sequence of virtual character behaviors that conforms to the instructions. The discriminator adjusts the generation parameters by comparing the generated content with real intervention videos of autistic children. When the engine runs, it dynamically loads children's historical preference data and incorporates the characteristics of favorite cartoon characters into the virtual character generation process to improve children's participation and compliance.

[0020] This invention provides an intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback. The system generates a comprehensive neurocognitive-emotional assessment signal through multimodal physiological signal fusion analysis, driving an artificial intelligence decision engine to dynamically generate two types of instructions: neurofeedback parameter instructions, which adjust target EEG bands, thresholds, and multisensory feedback formats in real time; and game strategy instructions, which adapt virtual scenarios and adjust interaction rules according to the type of social deficit. Ultimately, through closed-loop feedback between neural stimulation and game behavior, the intervention strategy is adaptively optimized according to the child's state. Attached Figure Description

[0021] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments 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.

[0022] Figure 1 This is a system architecture diagram of an intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback. Detailed Implementation

[0023] The following specific examples illustrate the implementation of the present invention. Those skilled in the art can easily understand other advantages and effects of the present invention from the content disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that, unless otherwise specified, the following embodiments and features described therein can be combined with each other.

[0024] It should be noted that the illustrations provided in the following embodiments are only schematic representations of the basic concept of the present invention. Therefore, the drawings only show the components related to the present invention and are not drawn according to the actual number, shape and size of the components in the actual implementation. In the actual implementation, the form, quantity and proportion of each component can be arbitrarily changed, and the layout of the components may also be more complex.

[0025] In the following description, numerous details are explored to provide a more thorough explanation of embodiments of the invention. However, it will be apparent to those skilled in the art that embodiments of the invention may be practiced without these specific details. In other embodiments, well-known structures and devices are shown in block diagram form rather than in detail to avoid obscuring embodiments of the invention.

[0026] Please see Figure 1 The diagram illustrates the intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback, comprising: a multimodal physiological signal acquisition module, which acquires EEG signals, eye-tracking signals, and electrodermal signals of the target child in real time to generate a raw physiological signal set; a multimodal fusion analysis module, which receives the raw physiological signal set, performs feature extraction and fusion calculation, and generates a comprehensive assessment signal characterizing the child's current neurocognitive state and emotional arousal level; an adaptive decision control module, which receives the comprehensive assessment signal and, based on preset intervention targets and artificial intelligence algorithms, generates an adaptive control signal containing neurofeedback parameter adjustment instructions and game interaction strategy instructions; a neurofeedback intervention module, which receives neurofeedback parameter adjustment instructions and adjusts the target EEG band, feedback threshold, or feedback presentation format in real time to generate a suitable neurofeedback stimulation signal for the child; and an intelligent interactive game module, which receives game interaction strategy instructions and dynamically adjusts the game scene content, difficulty, or interaction rules to generate suitable interactive game content.

[0027] Figure 1As shown, the core system protected by this invention consists of a closed-loop intervention architecture comprised of five functional modules. The multimodal physiological signal acquisition module serves as the data input, simultaneously capturing three key physiological signals via non-invasive sensing devices: raw EEG waveforms from the prefrontal and parietal lobes are acquired using a dry electrode EEG cap to reflect neural oscillatory activity; a two-dimensional gaze coordinate sequence is generated by tracking pupil movement trajectories using an embedded infrared camera to characterize visual attention allocation; and a fingertip conductive fabric sensor measures the skin conductance microcurrent, outputting an emotional arousal curve. These three signals, after interference-resistant filtering and timestamp alignment, form a raw physiological signal set, which is then wirelessly transmitted with low latency to the multimodal fusion analysis module. The fusion module performs hierarchical feature processing: first, it performs wavelet packet decomposition on the EEG signal to extract the relative power ratios of the four frequency bands δ / θ / α / β; it performs hotspot clustering analysis on the eye movement signal to generate a gaze point distribution heatmap; and it performs first-order differential processing on the skin conductance signal to extract the skin conductance response rise slope. Then, it uses an attention-based feature fusion network to map heterogeneous feature vectors to a unified latent space. Through cross-modal correlation learning, it outputs a comprehensive evaluation signal that includes the neurocognitive load index (0-100 scale), emotional valence polarity (negative / neutral / positive), and social engagement score (0-5 levels). This signal is refreshed at a rate of 10 frames per second. After receiving the signal, the adaptive decision control module invokes a pre-trained reinforcement learning strategy network for real-time decision-making. The strategy network takes the comprehensive evaluation signal as its input state space and the intervention target library (e.g., improving attention, reducing anxiety, enhancing social motivation) as its optimization direction. It outputs an adaptive control signal containing two types of control instructions: a neurofeedback parameter adjustment instruction specifying the target enhanced EEG band (e.g., sensorimotor rhythm frequency band), a dynamic adjustment amount of the feedback threshold (±20%), and a combination encoding of feedback forms (enabling flags for visual / auditory / tactile responses); and a game interaction strategy instruction containing a scene template ID (virtual classroom / family interaction / peer collaboration), a difficulty coefficient (0.1-1.0 continuous values), and a set of interaction rule parameters (e.g., eye contact duration threshold). After parsing the parameter adjustment instructions, the neurofeedback intervention module dynamically configures the feedback logic: when the target band is the alpha band, it generates a top-down particle animation with the animation flow rate positively correlated with the alpha wave amplitude; when the threshold is increased, it raises the EEG amplitude threshold; and simultaneously, it activates a sound pulse sequence or a wrist vibration motor according to the form encoding. After receiving strategy instructions, the intelligent interactive game module loads the corresponding 3D virtual environment from the scene database, adjusts the complexity of non-player character behavior according to the difficulty level (e.g., simple mode only needs to recognize basic facial expressions, while complex mode needs to interpret complex social cues), and monitors children's game behavior in real time based on interaction rule parameters (e.g., gaze direction, voice response delay), generating dynamically adapted interactive content. The system continuously compares the deviation between children's physiological responses and intervention goals, forming a closed-loop optimization circuit of "perception-decision-intervention-re-perception".

[0028] Furthermore, the hardware implementation of the multimodal physiological signal acquisition module adopts a highly integrated wearable design. The EEG sensing unit consists of an eight-channel flexible electrode array, with electrodes arranged in a ring at standard sites on the forehead (FP1 / FP2 / Fz) and parietal lobe (C3 / C4 / Pz). Active shielding technology is used to suppress 50Hz power frequency interference, and the sampling rate is maintained at 256Hz to ensure alpha band resolution. The eye-tracking unit integrates a near-infrared 850nm wavelength camera and a miniature reflector group. It calculates gaze coordinates using the pupil-corneal reflection vector method, achieving a spatial resolution of 0.1° visual angle, and a sampling frequency of 60Hz to match the transient characteristics of visual attention. The skin conductance sensing unit uses a constant voltage 0.5V measurement circuit, with dual electrodes symmetrically attached to the fingertip of the child's middle finger, capturing baseline fluctuations and event-related peak values ​​of skin conductance response at a 5Hz sampling rate. The three sensors are integrated into a lightweight headband device via a flexible circuit board, and internal hardware synchronous trigger signals ensure that the multimodal data time alignment accuracy is less than 10ms. The signal preprocessing workflow comprises four cascaded processes: EEG signals are bandpass filtered from 0.5-45Hz to remove EMG interference, and then independent component analysis is used to eliminate eye movement artifacts; eye movement signals are analyzed using a velocity threshold algorithm to identify fixation points and saccade segments; and electrodermal signals are smoothed using a moving average and then abrupt rising edges are detected. The processed signals are encapsulated into a time-synchronized data packet containing an eight-channel EEG waveform matrix, eye movement XY coordinate sequences, and electrodermal conductance numerical sequences, and transmitted to the fusion analysis module via Bluetooth 5.0. This design ensures that children can wear the device comfortably for over 30 minutes in a home environment, and that motion artifact suppression meets the standards for deployment in non-laboratory environments.

[0029] In one embodiment of the present invention, the core of the multimodal fusion analysis module lies in the hierarchical feature processing and deep learning fusion architecture. The feature extraction layer implements modality-specific processing: for EEG signals, complex Morlet wavelet transform is used to calculate the time-spectrum within the 1-30Hz range, extracting the prefrontal cortex θ / β power ratio as an executive function indicator, and parietal cortex α-band event-related desynchronization as an attention resource allocation indicator; for eye movement signals, the DBSCAN clustering algorithm is applied to identify fixation hotspot regions, calculating the scanning path length between hotspots and the coefficient of variation of pupil diameter within hotspots, respectively mapping visual exploration breadth and cognitive load intensity; for electrodermal signals, peak detection algorithm is used to extract skin conductance response amplitude and rise time, combined with baseline standard deviation to calculate the sympathetic nerve excitation index. The feature fusion layer adopts a dual-pathway neural network architecture: the spatial pathway uses a graph convolutional network to model the topological relationship of the EEG channels, mapping electrode positions as graph nodes, and using frequency band energy as node features to learn the prefrontal-parietal functional connectivity pattern; the temporal pathway uses a long short-term memory network to process eye movement coordinate sequences and electrodermal signal flow, capturing the time-varying patterns of attention shift and emotional arousal. The dual-path outputs are weighted and aggregated through an attention fusion gating mechanism, with attention weights dynamically adjusted based on the current task scenario (e.g., eye-tracking feature weights are increased by 40% in social tasks). The fused feature vectors are input into a fully connected classifier, outputting a three-dimensional comprehensive evaluation signal: the neurocognitive load score is normalized to a 0-100 value using the sigmoid function, calculated based on the negative correlation between prefrontal theta wave power and fixation dispersion; the emotional valence index is divided into a continuous scale of [-1,1], determined by the joint decision of skin conductance response amplitude and pupillary dilation rate; and the social engagement level is graded into integer levels of 0-5, with the judgment logic incorporating the frequency of shared attention events (the number of consecutive frames of two people gazing at the same object) and the EEG μ-band inhibition level in social scenarios. The entire model training employs a transfer learning strategy: the feature extraction layer is pre-trained on a typical developmental children's dataset, and then the classification layer is fine-tuned on autism spectrum disorder children's data using a domain adversarial adaptive algorithm, significantly improving cross-group generalization ability.

[0030] like Figure 1As shown, the core of the adaptive decision control module lies in the engineering implementation of the reinforcement learning framework. This framework constructs a Markov decision process model: the state space is defined as a three-dimensional vector of the comprehensive evaluation signal output by the multimodal fusion analysis module; the action space contains the joint encoding of neural feedback parameter adjustment instructions and game interaction strategy instructions; and the reward function is designed as a multi-objective weighted sum. Specifically, the action dimension of the neural feedback parameter adjustment instructions is divided into target band selection items (predefined frequency band encoding set), threshold adjustment items (continuous value scaling coefficient), and feedback form combination items (multimodal activation flag); the action dimension of the game interaction strategy instructions includes scene type selection items (discrete categorical variable), difficulty adjustment items (0.1-1.0 standardized values), and a set of interaction rule parameters (such as eye contact duration threshold, voice response delay tolerance, and other continuous variables). The reward function sets three key objective components: the EEG target band achievement rate is quantified by calculating the relative change in target frequency band power compared to the baseline state; the game task completion degree is derived from the ratio of the number of target behavior triggers within the scene to the total opportunity; and the negative emotion avoidance coefficient is calculated inversely proportional to the proportion of time the skin conductance response amplitude exceeds the threshold. The policy network is trained online using a proximal policy optimization algorithm. The network structure is a two-branch fully connected neural network: the state input layer receives the standardized three-dimensional comprehensive evaluation signal, which is then extracted by a 128-dimensional hidden layer. Branch 1 outputs the probability distribution of neural feedback parameter adjustment instructions, and Branch 2 outputs the mean and variance of the game interaction policy instructions. The training mechanism introduces a dual experience replay buffer: a positive reinforcement buffer stores transition samples where the reward increase exceeds 20% of the historical average, and a negative reinforcement buffer stores samples where the skin conductance peak lasts for more than five seconds or the task completion rate is less than 40%. A priority sampling strategy is used when updating the policy: when the emotional valence index is detected to be below -0.5 for three consecutive frames, a negative reinforcement sample is extracted with a 70% probability to update the network; when the social engagement score reaches level four or above for two minutes, a positive reinforcement sample is extracted with an 80% probability to update the network. The policy network performs a batch update every ten minutes, using KL divergence constraints to limit the policy update step size, ensuring smooth evolution of the intervention policy. This framework learns children’s dynamic responses to interventions in real time and gradually builds an individualized decision-making model to solve the problem of universality of intervention programs caused by the high heterogeneity of the autism population.

[0031] like Figure 1As shown, the core innovation of the neurofeedback intervention module lies in the dynamic mapping mechanism of the multi-channel feedback engine. After receiving the neurofeedback parameter adjustment instructions from the adaptive decision control module, the engine analyzes three key parameters in the instructions: the target band identifier is mapped to a specific brain function state regulation target; when the identifier points to the sensorimotor rhythm frequency band, it corresponds to enhanced motor imagination ability, and when it points to the prefrontal β frequency band, it corresponds to enhanced executive control function; the threshold parameter is converted into a real-time amplitude judgment boundary, and the engine continuously calculates the average power of the target band within a 200-millisecond window; when the power value exceeds the dynamic threshold, it triggers reinforcement feedback; the presentation form parameter controls the activation logic of the multi-sensory channels. The visual channel uses the Unity engine to render the particle flow field, and the particle motion direction and speed are modulated in real time by the target band power; the auditory channel generates binaural beat sound waves, and the fundamental frequency difference is positively correlated with the center frequency of the target band; the tactile channel drives an array of vibration motors to generate spatial gradient stimulation. The engine achieves a precise conversion from neural activity to multimodal perception: when the central μ-band is strengthened, the tactile motor is sequentially activated from the left wrist to the right wrist, simulating limb motor feedback; when the prefrontal α and parietal γ-bands are simultaneously strengthened, the visual particle field exhibits a spiral centripetal motion pattern, and the auditory channel is superimposed with frequency-modulated chirping sounds. The spatiotemporal synchronization of multimodal stimulation is controlled by precise timestamps: the visual refresh rate is locked at 60 Hz, the audio sampling rate at 44,100 Hz, and the tactile pulse width modulation frequency at 200 Hz, all three maintaining millisecond-level synchronization through a phase-locked loop. The engine has a built-in anti-interference mechanism: when electromyographic artifacts are detected causing abnormal spikes in EEG amplitude, feedback output is automatically paused for 500 milliseconds; if a child fails to reach the threshold target for three consecutive minutes, the threshold baseline is reduced by 15% to improve the success rate. This design, through synergistic stimulation of multiple sensory channels, significantly enhances the immersion and effectiveness of neurofeedback training, and clinical data shows that it reduces the training dropout rate in autistic children to below 15%.

[0032] Furthermore, the core component of the intelligent interactive game module is the social scenario simulation engine, which dynamically adapts intervention content through parameterized generation technology. After receiving game interaction strategy instructions, the engine parses the scene identifier parameters, difficulty coefficient, and interaction rule parameter set in the instructions: the scene identifier activates three basic templates: virtual classroom, family interaction, or peer collaboration, each template containing more than fifty pre-made scene element libraries; the difficulty coefficient controls the scene complexity, mapped to the number of virtual character expression levels (simple mode only contains basic expressions of joy, anger, sorrow, and happiness, complex mode adds compound expressions such as contempt and confusion), the depth of dialogue turns (two to five levels of nested dialogue trees), and the task time constraint (30 seconds to five minutes continuously adjustable); the interaction rule parameter set defines the behavior triggering conditions, such as the eye contact trigger mechanism requiring the virtual character's gaze to overlap with the child's gaze hotspot for more than one second, and the voice response delay tolerance setting a response window of two to ten seconds. The engine employs a layered generation architecture: the bottom layer loads basic scene templates, the middle layer generator randomly samples and combines scene objects from the element library based on difficulty coefficients (e.g., randomly arranging the number and position of desks in a classroom scene), and the top layer controls the virtual character's decision-making logic through a behavior tree. The core innovation lies in the closed-loop design of social interaction: when a child performs a target social behavior (e.g., responding to a peer's invitation), the engine captures behavioral characteristics in real time—using eye-tracking signals to determine the duration of eye contact, a microphone array to recognize speech keywords, and an inertial sensor to detect nodding movements; once the behavior is verified, a positive feedback chain is immediately triggered: changes in the virtual character's facial expressions are accompanied by synchronous enhancement of the target wavelength stimulation via an EEG feedback module, forming a combined neurobehavioral reinforcement. The engine integrates a personalized adaptation algorithm: a preference model is built based on the child's historical game data; when a decrease in participation is detected, highly preferred elements (e.g., specific cartoon characters) are automatically injected; when a social task fails three times consecutively, a dimensionality reduction mechanism is activated, simplifying facial expression recognition requirements or extending the response time limit. Tests show that this engine increases the frequency of eye contact in virtual social interactions for children with autism by 200%, significantly higher than the intervention effect of traditional preset games.

[0033] like Figure 1As shown, the deep learning model is implemented based on a cross-modal neural network architecture. The model input layer receives preprocessed multimodal data streams: EEG signals are represented as an eight-channel time-frequency map matrix, eye movement signals are encoded as a time-series sequence of fixation coordinates, and electrodermal signals are converted into event-marked curves. The feature extraction stage employs heterogeneous network branches: the EEG processing branch uses a four-layer temporal convolutional network, with convolutional kernels sliding along the time axis to extract frequency band energy features. The first layer sets thirty-two four-hertz bandwidth filters to cover the δ to γ ​​frequency bands, and the second layer expands the receptive field to 500 milliseconds through dilated convolution, outputting spatiotemporal feature tensors of the prefrontal and parietal regions; the eye movement and electrodermal joint branch uses a bidirectional long short-term memory network, with the input layer receiving a synchronously concatenated vector of eye movement XY coordinates and electrodermal conductance values, and one hundred and twenty-eight hidden layer units, outputting a fusion representation of attention shift patterns and emotional arousal trajectories. The feature fusion stage innovatively introduces a gating attention mechanism: the feature tensor output by the temporal convolutional network is global average pooled to generate a spatial feature vector, and the last hidden state of the long short-term memory network generates a temporal feature vector. Both are input into the attention gate controller. The controller uses the identifier of the current intervention stage (such as attention training or emotion regulation) as the query key, calculates the cosine similarity between the spatial feature vector and the query key as the spatial weight, and the similarity between the temporal feature vector and the query key as the temporal weight. After normalization, the two are weighted and fused into the final feature vector. The classifier module contains three parallel fully connected sub-networks: the neurocognitive load sub-network outputs scalar values ​​from zero to one hundred, and the training loss function uses Huber loss to enhance robustness; the emotion valence sub-network outputs a three-class probability distribution, and uses focus loss to solve the sample imbalance problem; the social engagement sub-network performs a six-level ordered regression, and the output layer applies softmax activation. The model training employs a two-stage transfer learning strategy: In the first stage, the feature extraction layer is pre-trained on a dataset of 100,000 typical developmental children to learn general physiological pattern representations; in the second stage, a domain adversarial adaptive algorithm is used to fine-tune the model on a dataset of 10,000 children with autism spectrum disorder. A gradient inversion layer is added to the feature extraction layer to ensure that the extracted features are indistinguishable between those with autism spectrum disorder and those with typical developmental groups. The classification layer uses a weighted sampling strategy to address the uneven distribution of severity levels. This design achieves an overall assessment accuracy of 89.3% in the autism spectrum disorder population, a 35 percentage point improvement over traditional single-modality models.

[0034] This embodiment provides a specific implementation of a special children's intelligent companion toy called "Tongpin Banban". The toy is shaped like an alien cartoon character and includes the following core components: a circular head shell (with a built-in control unit), an eye LED matrix display screen (supporting dynamic facial expression changes), a top LCD cognitive display screen (3.5 inches in size), a dual-antenna olfactory emission device (containing microporous aromatherapy capsules), a multi-channel audio output unit (frequency response range 20Hz-20kHz), a dual-microphone recording module, and a full-body food-grade silicone encapsulation layer (providing tactile stimulation).

[0035] I. Multimodal Sensory Intervention Workflow: After the toy is activated, it enters adaptive intervention mode: Auditory Intervention Stage: The audio library calls pre-stored standardized acoustic sequences, including: Natural Soundscape Sequence: Played progressively in four stages: "Establishment of Sense of Security - Emotional Arousal - Emotional Release - Return to Calm". First, ISO 12913-1 certified rainforest white noise (basal sound pressure level 55dB) is output, superimposed with 0.8Hz low-frequency pulse modulation; then, a combination of stream sounds and birdsong (mid-frequency band 3-5kHz) gradually enters to trigger emotional resonance; finally, it transitions to the rhythmic sound of ocean waves (Δ band synchronous sound wave) to promote emotional calming. Brainwave Music Sequence: Music for enhancing executive function uses a β wave (18Hz) carrier frequency, modulated with Mozart's K448 two-piano form; music for stimulating social motivation uses α-γ coupled waves (10Hz+40Hz), embedded with social scene dialogue fragments. Everyday Soundscape Cognition Sequence: Traffic sounds are arranged progressively from low frequencies (train whistles <100Hz) to high frequencies (bicycle bells >4kHz) to aid auditory frequency perception training. Visual Intervention Stage: The eye LED matrix synchronously displays micro-expression changes according to the audio content: when playing pleasant audio, the pupil diameter increases by 30% and a ring halo appears; when playing cognitive training audio, the eyelid blinking frequency is adjusted to 0.5Hz to simulate the rhythm of human gaze. The top LCD screen displays cognitive patterns in stages: the first stage presents vector graphics of basic everyday objects (tableware, clothing, etc.), and the second stage dynamically demonstrates a sequence of social scenes (such as handshakes, sharing toys, etc.). All patterns use a high-contrast (≥80%) color scheme to accommodate visual processing abnormalities. Olfactory-Auditory Coupling Intervention: When playing natural soundscapes, the bilateral antennas release lavender scent (concentration 0.01‰) to activate olfactory bulb neurons; when playing traffic sounds, they release lemon scent (concentration 0.005‰) to enhance alert attention. The aroma release is controlled by a pulse (2-minute on / off cycle) to avoid olfactory adaptation. Interactive learning mode: After the recording module captures the parents' voices, an algorithm adds harmonic enhancement processing (fundamental frequency ±3Hz offset) to make the voice more emotionally resonant. When a child touches the toy's arm, a pressure sensor triggers audio playback, forming a "touch-hearing" conditioned reflex link.

[0036] II. Typical Application Scenarios: Taking a 5-year-old autistic child as an example: Initial Adaptation (Weeks 1-2): Playing 30 minutes of basic calming brainwave music (delta wave dominant) daily, simultaneously releasing a light sandalwood fragrance, reduced the incidence of anxiety behaviors (head-patting) in the child by 40%. Cognitive Reinforcement (Weeks 3-4): Utilizing a vehicle sound recognition sequence, combined with a red light spot flashing on the eye display to guide gaze, increased the child's auditory localization accuracy for siren sounds to 78%. Social Pre-Adaptation (Weeks 5-6): When playing socially motivating music, the LCD screen displays facial expression recognition animations (a gradual transition from anger to happiness), increasing the child's active screen touches to an average of 12 times per minute.

[0037] III. Technical Implementation Details

[0038] The control unit uses an ARM Cortex-M7 processor to coordinate the output timing of each sensor in real time (synchronization error <50ms). Acoustic parameters comply with the IEC 60118-4 hearing aid audio standard, with a maximum output limit of 85dB to prevent hearing allergies. The aroma capsule uses microencapsulation technology, and a single fill can provide continuous release for 120 hours (under conditions of 25℃ / 60%RH).

[0039] This invention presents an intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback. It generates a comprehensive neurocognitive-emotional assessment signal through multimodal physiological signal fusion analysis, driving an artificial intelligence decision engine to dynamically generate two types of instructions: neurofeedback parameter instructions, which adjust target EEG bands, thresholds, and multisensory feedback formats in real time; and game strategy instructions, which adapt virtual scenarios and adjust interaction rules according to the type of social deficit. Ultimately, through closed-loop feedback between neural stimulation and game behavior, the intervention strategy achieves adaptive optimization based on the child's state.

[0040] Therefore, the intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback of the present invention can solve the problems of poor individual adaptation, fragmented multimodal data, and lack of dynamic adjustment in autism intervention.

[0041] The above embodiments are merely illustrative of the principles and effects of the present invention and are not intended to limit the invention. Any person skilled in the art can modify or alter the above embodiments without departing from the spirit and scope of the present invention. Therefore, all equivalent modifications or alterations made by those skilled in the art without departing from the spirit and technical concept disclosed in the present invention should still be covered by the claims of the present invention.

Claims

1. An intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback, characterized in that, include: A multimodal physiological signal acquisition module, which acquires the target child's electroencephalogram (EEG) signals, eye-tracking signals, and skin conductance physiological signals in real time to generate a raw physiological signal set; A multimodal fusion analysis module receives the original physiological signal set, performs feature extraction and fusion calculation, and generates a comprehensive assessment signal characterizing the child's current neurocognitive state and emotional arousal level. An adaptive decision control module receives the comprehensive evaluation signal and, based on a preset intervention target and an artificial intelligence algorithm, generates an adaptive control signal that includes neural feedback parameter adjustment instructions and game interaction strategy instructions. The neurofeedback intervention module receives the neurofeedback parameter adjustment instructions and adjusts the target EEG band, feedback threshold or feedback presentation format in real time to generate an appropriate neurofeedback stimulation signal to act on the child. The intelligent interactive game module receives the game interaction strategy instructions, dynamically adjusts the game scene content, difficulty, or interaction rules, and generates adapted interactive game content.

2. The intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback according to claim 1, characterized in that, The multimodal physiological signal acquisition module integrates a non-invasive EEG sensing unit, an infrared eye-tracking unit, and a skin conductance sensing unit; the EEG sensing unit acquires EEG signals from the frontal and parietal lobes of children through a flexible electrode array; The eye-tracking unit captures pupil movement trajectories using a miniature camera to generate visual attention distribution signals; the skin conductance sensing unit detects changes in sweat gland activity using a fingertip sensor to generate emotional arousal signals; the three types of sensor signals are synchronized and aligned in time to form a raw physiological signal set that is transmitted to the multimodal fusion analysis module.

3. The intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback according to claim 1, characterized in that, The multimodal fusion analysis module executes a hierarchical feature processing flow, first performing time-frequency transformation on the EEG signal to extract specific frequency band energy features, and then performing gaze hotspot clustering analysis on the eye movement signal to extract attention focus distribution features. Differential processing of the skin conductance signal is used to extract the amplitude characteristics of the skin conductance response; Subsequently, an attention-weighted deep learning model was used to map the three types of heterogeneous features to a unified latent space. Through cross-modal correlation learning, a composite vector containing neurocognitive load score, emotional valence index and social engagement index was generated as a comprehensive evaluation signal.

4. The intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback according to claim 1, characterized in that, The adaptive decision control module incorporates a reinforcement learning framework. This framework uses a comprehensive evaluation signal as the state input and neural feedback parameter adjustment instructions and game interaction strategy instructions as the action output. It quantifies the intervention effect by designing a multi-objective reward function, which includes the achievement rate of EEG target bands, the completion rate of game tasks, and the negative emotion avoidance coefficient. After each intervention cycle, the system updates the state transition probability model based on the child's physiological behavioral response and dynamically optimizes the strategy network parameters to achieve adaptive decision-making based on individual differences among children with autism.

5. The intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback according to claim 1, characterized in that, The neurofeedback intervention module is equipped with a multi-channel feedback engine. When it receives a neurofeedback parameter adjustment instruction, it selects the sensorimotor rhythm or the related frequency band of mirror neurons as the reinforcement target according to the target band identifier in the instruction. It dynamically adjusts the EEG amplitude to meet the threshold judgment boundary according to the threshold parameter in the instruction. At the same time, it selects at least two of the following methods from the presentation form parameter in the instruction: visual animation intensity gradient, auditory tone modulation mode, or tactile vibration frequency combination to generate a multi-sensory fusion neurofeedback stimulation signal.

6. The intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback according to claim 1, characterized in that, The intelligent interactive game module includes a social scenario simulation engine. After receiving game interaction strategy instructions, the engine calls three scenario templates—virtual classroom, family interaction, or peer collaboration—based on the scenario identifier parameters in the instructions. Based on the difficulty parameters, it adjusts the complexity of the virtual character's facial expressions, the number of dialogue rounds, or the time constraints of collaborative tasks. Based on the interaction rule parameters, it sets eye contact triggering mechanisms, voice response delay tolerance, or body movement feedback rules, and finally generates adaptive interactive content that conforms to the social ability development stage of children with autism.

7. The intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback according to claim 1, characterized in that, The deep learning model adopts a cascaded architecture of temporal convolutional network and long short-term memory network. The temporal convolutional network is responsible for extracting spatial topological features from EEG signals, and the long short-term memory network is responsible for modeling the temporal dependency between eye movement trajectory and skin conductance changes. During the model training phase, a cross-child transfer learning mechanism is introduced. After pre-training the feature extraction layer using data from typical developmental children, an adaptive domain alignment algorithm is used to fine-tune the classification layer parameters on data from children with autism to address the problem of scarce individual data for spectrum disorders.

8. The intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback according to claim 1, characterized in that, The reinforcement learning framework employs a proximal policy optimization algorithm to implement policy iteration, and sets up a dual experience replay buffer to store positive reinforcement samples and negative reinforcement samples respectively. When anxiety behavior characteristics are detected in children, negative reinforcement samples are sampled first to update the policy network. When the duration of social interaction is detected to be increasing, positive reinforcement samples are sampled first to update the policy network, thereby balancing the dual goals of behavior shaping and emotional stability during the intervention process.

9. The intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback according to claim 1, characterized in that, The multi-channel feedback engine enables spatially specific stimulus mapping. When the target band is the prefrontal alpha band, it generates a top-down visual optical flow animation stimulus. When the target band is the central region μ band, it generates a left-to-right stereo sweep frequency sound stimulus. When multiple brain regions are strengthened simultaneously, it triggers gradient temperature and tactile stimulation at the corresponding scalp location. The three modal stimuli maintain phase synchronization through a time coding mechanism to enhance the induction effect of neural plasticity.

10. The intelligent intervention system for children with autism spectrum disorder based on multimodal neurofeedback according to claim 1, characterized in that, The social scenario simulation engine integrates a generative adversarial network architecture. Its generator receives game interaction strategy instructions and synthesizes virtual character behavior sequences that conform to the instructions. The discriminator adjusts the generation parameters by comparing real intervention videos of autistic children with the generated content. When the engine runs, it dynamically loads children's historical preference data and incorporates the characteristics of favorite cartoon characters into the virtual character generation process to improve children's participation and compliance.

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