Wearable brain-computer semantic synchronization device

Abstract

The application discloses a wearable brain-computer semantic synchronization device for medical rehabilitation. The device collects language-related electroencephalogram (EEG) through a head-mounted EEG collector, extracts semantic fragments through an intention decoding module, manages context and determines output timing through a semantic synchronization engine, triggers the generation of coherent text / speech through personalized NLG, and realizes real-time alignment of brain intention and language expression. The device is composed of three units: EEG collection, main processing and output. It cooperates with embedded computing or mobile terminals through a head-mounted electrode array. The system has emotion recognition, tone style control, semantic security verification and emergency interruption. It supports long-term wearing, 8-hour endurance and voice / text dual-channel output. It can be connected to rehabilitation training applications, iteratively personalized models through continuous learning, and balance privacy and security, significantly improving the communication efficiency and rehabilitation experience of patients with aphasia, freezing and other diseases.

Description

Technical Field

[0001] This invention belongs to the field of intelligent wearable devices and medical rehabilitation aids, and relates to a wearable brain-computer semantic synchronization device. Specifically, it involves the integrated application of brain-computer interface (BCI) technology and natural language processing technology. Through EEG signal acquisition, semantic intent decoding, semantic mapping, and natural language generation, it achieves real-time synchronization between the user's brain intent and external language expression. In terms of application, this invention serves patients who have lost their normal speaking ability due to paralysis, aphasia, or other conditions, assisting them in daily communication and language rehabilitation training. The device integrates multiple disciplines such as biomedical signal processing, machine learning, and large language models, and has broad application prospects in the fields of intelligent brain-computer communication and medical rehabilitation interaction. Background Technology

[0002] Existing artificial intelligence systems suffer from numerous limitations in semantic understanding and autonomous consciousness. Traditional AI cognitive architectures mostly employ a hierarchical, sequential processing model (such as the classic DIKW pyramid model), lacking a closed-loop feedback between high-level semantics and low-level data, making timely self-correction and contextual awareness difficult. In contrast, the Data-Information-Knowledge-Wisdom-Intention (DIKWP) model is a semantic model of artificial consciousness proposed in recent years. The DIKWP model adds the key element of "Intention" and breaks away from the unidirectional hierarchical structure of the classic DIKW pyramid, instead adopting a non-linear network architecture: the five elements are interconnected, achieving bidirectional flow and feedback through 25 semantic transformation modules. This fully connected cognitive loop allows high-level intelligence and goals to influence low-level perception, while low-level data processing in turn affects high-level decision-making, achieving adaptive cognitive adjustment. This theory provides a new framework for achieving machine cognition similar to human consciousness.

[0003] Furthermore, the "relativism of consciousness" hypothesis states that whether an entity is considered conscious depends on whether the observer can understand its output. Different observers, due to differences in knowledge background and comprehension, may interpret the same information drastically, leading to different judgments about whether the entity possesses "consciousness." In other words, consciousness is not an absolute attribute, but rather a matter of understanding relative to different observers. This view emphasizes that AI systems need to consider the relativity of semantic understanding, enabling them to interpret input information from multiple perspectives and align intents based on different external observers or environments, ensuring that their behavior is understood and accepted by humans.

[0004] Another related theory is the "Consciousness Bug Theory." This theory compares the human brain to a machine constantly playing a word game, arguing that consciousness is merely an accidental byproduct (i.e., an illusion) arising from limited cognitive resources. In the human brain, most information processing is automatically completed subconsciously, while conscious thinking often results from occasional deviations due to system load bottlenecks. This theory overturns the traditional view of consciousness as a carefully crafted, orderly product of evolution, explaining various irrational phenomena and cognitive biases in human consciousness. For artificial intelligence, this means that anomalies or deviations similar to "bugs" (such as incorrect reasoning or misinterpreting intentions) are inevitable in complex cognitive processes. How to detect and correct these cognitive anomalies, ensuring the reliability and consistency of AI decisions, has become an important topic in artificial consciousness research.

[0005] In summary, existing AI hardware and models rarely incorporate the aforementioned cutting-edge theories of artificial consciousness. Current intelligent chips are primarily geared towards numerical computation and perceptual reasoning, lacking explicit semantic cognitive structures in their processing and, more importantly, mechanisms for monitoring and responding to cognitive errors.

[0006] This makes it difficult for AI systems to achieve a deep semantic understanding of external information, and they are unable to recognize flaws or intentional drift in their own reasoning, resulting in opaque and unreliable decision-making. In complex scenarios (such as medical diagnosis and legal judgment), AI often faces the "three no's problem" of incomplete, inaccurate, and inconsistent information without realizing it, which may lead to cognitive biases or even serious errors.

[0007] Therefore, there is an urgent need for an innovative hardware solution that directly implements the DIKWP mesh cognitive architecture and consciousness bug monitoring mechanism at the chip level to support AI's relative semantic understanding of external information, proactive bug tracking and feedback of internal anomalies, and enable the system to adaptively evolve according to intention deviations, thereby significantly improving the interpretability, reliability and intelligence level of the AI ​​system. Summary of the Invention

[0008] In view of the aforementioned background problems, the primary objective of this invention is to provide a wearable brain-computer interface device for rehabilitation patients, enabling the synchronous output of the patient's internal semantic intentions and external language expression. Through this device, the "inner thoughts" of paralyzed, aphasic, and other patients can be converted into machine-generated sound or text with almost no delay, thereby shortening the gap between the patient's internal thoughts and external expression. Here, "semantic synchronization" refers to synchronization in time (outputting as real-time as possible to reduce waiting time), content (accurately expressing the patient's true meaning), and emotion (the output tone and emotion are consistent with the patient's inner thoughts). Only when these requirements are met simultaneously can the communication conducted by the patient using the device approach the natural fluency of normal conversation. This invention aims to overcome the limitations of existing rehabilitation communication aids in terms of speed, content, and emotional expression through innovative hardware and software design, enabling individuals with severe language impairments to regain high-quality communication skills and social confidence.

[0009] This invention provides a wearable brain-computer interface (BCI) semantic synchronization device, specifically designed to improve the quality and efficiency of human-computer communication in medical rehabilitation scenarios. The device consists of an EEG signal acquisition unit, a main processing unit, and an output unit. The main processing unit integrates core functional components such as an intent decoding module, a semantic synchronization engine, and a natural language generation (NLG) module. During operation, the device acquires the user's brain signals via non-invasive EEG electrodes. The intent decoding module in the main processing unit uses a specific algorithm to extract key information (i.e., semantic fragments) from the real-time EEG. The semantic synchronization engine then temporarily stores these fragments and assesses the completeness of the sentences, triggering the NLG module at an appropriate time. Based on the decoded set of semantic fragments and the context, the NLG module automatically expands and generates complete sentences that conform to human language habits. Finally, the output unit outputs these sentences in the form of speech synthesis or text display. Through this process, patients only need to generate vague thought fragments, and the system can assist in refining them into coherent speech, enabling patients to communicate fluently in a manner close to normal language.

[0010] The device adopts a lightweight wearable form, such as a head-mounted EEG device or other inconspicuous wearing methods, allowing for extended daily use without discomfort. The entire system is also optimized for medical rehabilitation scenarios, providing safety controls and personalized adjustment functions to ensure appropriate output in different environments and to suit the user's individual style. Its main innovations and features include:

[0011] 1. Brain-Computer Intent Capture Module: This module non-invasively acquires and decodes semantic intent from brain signals in real time. It uses highly sensitive portable EEG electrodes (such as headband or patch electrodes) to collect the user's brain signals and employs an embedded intent decoding algorithm to extract the core semantic information the user wants to express. Unlike traditional brain-computer typing that inputs character by character, this module focuses on directly extracting semantic information, capturing the "core" of the user's intended message rather than specific words. For example, if the user thinks "it's hot," the decoding module might output a semantic fragment as "weather + hot," which, while not forming a complete natural language sentence, already contains the main meaning. The module detects language-related brain activity patterns, such as semantically relevant potential changes in the frontal and temporal cortex, or characteristic signals appearing in the Broca's area when the user plans to speak. Building upon existing clinical applications of non-invasive BCI, this invention further optimizes the signal processing algorithm for language intent decoding. For example, by employing filtering and feature extraction methods more suitable for semantic recognition, even in signals with low signal-to-noise ratios like scalp EEG, it is possible to more accurately distinguish EEG features associated with specific semantic categories. The output of this module can be a set of semantic labels or keywords, along with confidence information to indicate the reliability of the decoding result.

[0012] 2. Natural Language Generation (NLG) Module: This module expands the decoded, concise semantic content to generate natural language sentences. The device incorporates a finely tuned NLG model (e.g., a local version of a compressed large language model) that can generate complete sentences or even multi-sentence dialogues that conform to human everyday language habits based on semantic fragments provided by the intent capture module. The NLG model combines contextual information and the user's personal language style to ensure that the output is both coherent and consistent with the user's speaking habits. Since users may have limited vocabulary in medical rehabilitation scenarios, the NLG module automatically completes details when necessary, making the expression clearer and richer. For example, continuing the previous example, the corresponding semantic fragment "weather + hot" might be expanded into the sentence: "It's very hot today, and I feel a little unwell." In this way, patients don't need to conceive complete sentences in their minds; they only need to provide the core idea, and the AI ​​fills in the rest, significantly improving the intelligibility and information content of the output. Simultaneously, the NLG model supports multilingual output and can adjust the language according to the patient's cultural background to serve users from different native languages ​​or regions. This is particularly useful for cross-language communication or for immigrant patients, as the device can directly generate sentence output in the target language based on their intent. The NLG module itself can run a small model on a local embedded device, or send key signals via a secure connection to a powerful language model service in the cloud for generation and return. This invention preferably uses a local model while ensuring privacy, to reduce latency and protect user data security. In summary, the introduction of advanced NLG significantly improves communication speed and the naturalness of expression, eliminating the need for patients to type verbatim.

[0013] 3. Semantic Synchronization Engine: The core software module that coordinates brain signal decoding and language generation to achieve synchronous and coherent output with the user's thought process. The Semantic Synchronization Engine comprises three sub-functions: Semantic Buffer, Context Management, and Synchronization Control. First, the Semantic Buffer temporarily stores and updates fragments of intent information continuously generated by the user, essentially acting as a working memory that accumulates the content of the user's conceived speech in real time. Regardless of whether the semantics output by the decoding module is vocabulary or conceptual labels, they are sequentially placed into the buffer and supplemented or replaced as needed. Second, Context Management maintains the background of the current dialogue, such as the topic of the previous sentence and the speaker. Context helps constrain the decoding and generation process, improving accuracy and coherence. For example, if the previous sentence discussed the weather, the ambiguous content decoded in the next sentence is more likely to be related to the weather, thus improving the accuracy of guessing. The Context Manager provides summaries of recent dialogue rounds to the NLG module as additional input, ensuring the generated results connect with the dialogue context. Finally, Synchronization Control dynamically determines the output timing based on brain signals. For example, as the user gradually forms a sentence in their mind, the intent decoding module might sequentially output fragments such as "The weather is hot...don't go out," indicating that the user may be conceiving the sentence "It's very hot, don't go out." At this point, the synchronization engine combines these fragments into a more complete semantic representation. However, it doesn't immediately trigger output but continues to monitor subsequent brain signals to determine if the user has anything more to add. If the synchronization engine detects a brief pause in the user's thought process (e.g., the EEG enters a "blank" mode, indicating no new words are emerging) or captures a signal characteristic of the end of a complete intention (e.g., a specific brainwave rhythm shifts from activation to relaxation), it considers the sentence to be complete. The engine then immediately triggers the NLG module to generate the sentence output, preventing unnecessary delays in communication. If the user continues to provide new semantic fragments in their mind, as if "talking to themselves," the system can simultaneously acquire new ideas. Figure 1 The output is spaced out, simulating the continuous and coherent process of normal human speech. In addition to content synchronization, the semantic synchronization engine also ensures emotional synchronization: by monitoring the user's emotion-related brain signals, it adjusts the tone and intonation of the NLG (Natural Language Generator) to ensure the synthesized speech expresses the user's current emotional state. For example, the engine can determine whether the user is happy or sad based on patterns of excitement or calmness in the EEG, thus instructing the NLG to use corresponding words and punctuation—using exclamation marks and light language at the end of sentences when happy, and slowing down the tone and using more subtle language when sad. This allows the listener to feel the patient's emotions from the output speech, achieving a more natural communication effect. The timing diagram of the entire semantic synchronization process is shown below. Figure 1 As shown: The user's mental intention signal gradually forms, the decoding module extracts keywords to fill the buffer -> a pause in thought is detected -> the synchronization engine triggers NLG to generate a complete sentence -> output complete.

[0014] 4. Patient Semantic Model and Personalization: A semantic mapping module based on each user's unique language style for personalized learning and customization. This invention considers that different patients have their own habitual ways of expressing themselves, such as word preferences, tone, etc., and therefore introduces a personalized semantic model into the system for continuous training and optimization of NLG. Specifically, the device has a built-in semantic mapping submodule that establishes an association mapping between the user's commonly used phrases, catchphrases, etc., and their corresponding brain signal patterns. For example, when a patient expresses gratitude, the concept of "thank you" comes to mind, but he usually prefers to say a more casual and friendly phrase like "thank you." After a period of learning, the system will find that whenever the user wants to express gratitude (the concept node "thank you" is activated), he is more inclined to choose "thank you" rather than "thank you very much." Therefore, the semantic model will assign a higher association weight between the concept of "thank you" and the phrase "thank you" (e.g., ...). Figure 2 As shown in the diagram, the NLG module biases the output towards the latter. This mechanism ensures that the user's language style is faithfully preserved in the output, reflecting "semantic sovereignty." Even when the machine phrases the sentences, it tries to use the user's own speaking style as much as possible. The personalized model is trained by continuously collecting the correspondence between "user's brain signal pattern and user's expected output": whenever a user successfully expresses a sentence using the device, the system records the brainwave characteristics at that time and the final wording used, and incorporates it into the user-specific model for iterative updates. The longer the time, the more accurate the model's grasp of user preferences becomes. For privacy reasons, this personalized data and model are stored only on the user's local device or personal account, encrypted and protected, ensuring that the exclusive semantic model belongs only to the user and is not misused or obtained by others. Through the personalization function, the sentences output by the device will be closer to the user's own language style, making the user and their relatives and friends feel familiar and natural.

[0015] 5. Wearable Hardware Integration: Hardware structure design oriented towards daily wearability. The device's hardware form can be designed as a thin and light smart cap, brain-computer interface headband / earphone, collar, etc., striving not to affect the user's daily activities. It integrates a low-power processor and can also obtain computing power support via wireless connection to a smartphone when necessary. The device connects to output devices wirelessly via Bluetooth or other methods, such as connecting to a phone speaker to play voice or connecting to AR glasses to display text. In short, the hardware design emphasizes concealment, portability, and comfort, allowing communication and interaction to occur naturally without being abrupt. Existing technologies have proven that integrating EEG acquisition devices and computing units into head-mounted devices is feasible, such as the Microsoft HoloLens solution combined with OpenBCI. This invention further optimizes hardware materials and structure for rehabilitation scenarios, using medical-grade skin-friendly materials and employing elastic support and conductive gel reduction design to ensure that the electrodes remain comfortable and secure for long-term wear while maintaining stable signal acquisition. The entire device weighs less than a few hundred grams, and the built-in battery can support continuous operation for no less than 8 hours, meeting daily usage needs. The modular structure facilitates device cleaning and the maintenance and upgrading of various components. For example, the electrode plates can be disassembled and replaced, and the processing modules can be plugged in and upgraded to extend the service life of the equipment.

[0016] 6. Enhanced Rehabilitation Interaction Functions: In addition to assisting daily communication, this device also features additional functional modules to promote rehabilitation training and ensure safety. Through an open interface, the device can interface with rehabilitation training applications, allowing users to interact with the training software using their thoughts, such as selecting answers and controlling the start / stop of exercises, increasing the fun and initiative of training. Utilizing semantic synchronization, therapists can also understand patients' inner feedback more promptly: for example, if a patient finds a question "too difficult" during training, the device will immediately output this thought, allowing the therapist to adjust the training difficulty accordingly. Furthermore, the device is equipped with context-aware and safety control modes to adapt to different usage scenarios. For example, in quiet environments such as hospital examinations, the system can switch to pure text output mode to avoid disturbing others; in noisy public environments, it can automatically switch to text or increase the volume to ensure effective information delivery. A one-button (or thought-based) toggle switch is also provided, allowing users to manually select between voice and text output as needed. The device also features an emergency interruption mechanism; users can pre-set a specific brain signal pattern as a stop command, and once triggered, the system immediately stops the current output. For example, if a user discovers an error in the generated sentence, they can quickly visualize a specific image or perform a pre-defined mental activity, essentially "pressing" the undo button in their brain. Upon detecting this pattern, the system will immediately stop the playing audio or retract any unsent text to prevent the spread of errors. Furthermore, the device can be equipped with a physical emergency stop button for the user or caregiver to press, ensuring that the user has a way to terminate the output in any situation. All these design features guarantee the system's safe and reliable use in various scenarios, preventing embarrassing or dangerous situations caused by AI misunderstandings or changes in the environment.

[0017] In summary, this invention, based on traditional communication aids, introduces an innovative combination of brain-computer interface and natural language generation, achieving synchronous conversion between the user's brain semantics and external language expression. It significantly reduces the difficulty for severely disabled patients to express their thoughts, enabling them to participate in more complex and richer dialogues and social activities, which is of great significance for rebuilding their social roles and improving their quality of life. Its novelty lies in the unprecedented human-computer collaborative communication method and wearable implementation, possessing ample inventiveness and patent value. Compared with existing technologies, this invention has the following beneficial effects:

[0018] Real-time and efficient expression of thoughts: Achieving near-real-time conversion of brain intentions into language, significantly improving patients' communication speed and bridging the time gap between inner thoughts and spoken words. For example, output has increased from less than 10 words per minute to near-normal conversational levels, making communication no longer intermittent and slow.

[0019] Semantic-level decoding reduces the burden: Employing semantic-level information extraction avoids the inefficient process of letter-by-letter spelling. The system automatically completes details and utilizes context, making expression more coherent and intelligent, greatly reducing the cognitive and operational burden on patients. Compared to the traditional P300 spellboard, this invention reduces selection steps by more than half.

[0020] Synchronized output of emotion and tone: The device can recognize and convey the patient's emotional state, generating speech and text that carries the user's emotional coloring and tone. In this way, even if the patient cannot speak in person, they can "speak with emotion" through the device, enhancing the emotional resonance and authenticity of communication.

[0021] Personalized Language Style: By continuously learning patients' habitual expressions, the system's output gradually adapts to the individual's speaking style and vocabulary preferences. Each user has a customized language model, ensuring personalized expression. Patients will feel as if they are speaking themselves when they hear or see the words output by the device, and their family and friends will also feel familiar and comfortable.

[0022] Convenient and easy to wear for everyday life: The device is small, lightweight, discreet, and aesthetically pleasing, allowing for all-day wear. Wireless connectivity and low power consumption make it suitable for various settings such as homes, hospitals, and outdoors, without interfering with daily activities. Compared to bulky devices requiring fixed locations, this invention allows patients to engage in brain-computer interface communication anytime, anywhere.

[0023] Effects of Assisted Rehabilitation Training: While assisting communication, the device continuously stimulates and utilizes the patient's language center, thus playing a role in rehabilitation training. With long-term use, the patient's brain's language circuitry may be reconstructed and strengthened to some extent. Some patients may gradually reduce their dependence on the device after rehabilitation, and regain the ability to speak or utter some words, demonstrating the potential value of this invention as a rehabilitation device.

[0024] In summary, the wearable brain-computer semantic synchronization device provided by this invention improves the communication ability of severely disabled patients while taking into account both safety and personalized needs, demonstrating significant innovative advantages and practical value. The technical solution of this invention is described in detail below. Attached Figure Description

[0025] Figure 1 This is a schematic diagram of the timing flow of brain intent decoding and NLG collaboration in this invention;

[0026] Figure 2 This is a schematic diagram of the personalized semantic mapping of the present invention;

[0027] Figure 3 This is a schematic diagram of the timing flow of brain intent decoding and NLG collaboration in this invention. Detailed Implementation

[0028] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the following embodiments are for illustrative purposes only and are not intended to limit the scope of the invention. Without departing from the spirit of the invention, those skilled in the art can make several modifications or equivalent substitutions, which should also be considered within the scope of protection of the present invention.

[0029] Overall structure and working process of the device

[0030] like Figure 3 As shown, the wearable brain-computer semantic synchronization device of the present invention comprises three parts: an EEG signal acquisition module, a main processing module, and an output module. Wherein:

[0031] (a) EEG Acquisition Module: Several microelectrodes are arranged on a wearable carrier (e.g., the lining of a hat, a forehead patch, or a headband), mainly covering language-related brain regions (frontal lobe, temporal lobe, etc.) to acquire highly relevant brain signals. This embodiment uses dry electrodes in conjunction with a high input impedance amplifier chip to reduce reliance on conductive gel and maintenance frequency, while an elastic support ensures good contact between the electrodes and the scalp. The acquisition module has a built-in multi-stage filtering circuit (e.g., 50Hz notch filter, 0.5-45Hz bandpass filter, etc.) and a preamplifier to preprocess and perform preliminary feature extraction on the raw EEG signal, and then sends the digitized EEG data stream to the main control processing module for further analysis.

[0032] (b) Main Control Processing Module: A small embedded computing unit integrated into wearable devices (e.g., fixed to the back of a head-mounted device) or performing operations via a wirelessly connected mobile terminal (such as a smartphone). The main control module incorporates an intent decoding algorithm and an NLG model, serving as the core of the entire device's information processing. It receives data from the EEG acquisition module in real time, analyzes and decodes it, generates semantic fragments, manages context, and triggers language generation. The main control module can utilize an embedded AI chip to perform neural network inference locally to reduce latency; for complex deep learning models, the data can be encrypted and sent to the user's mobile phone for processing using its computing power or cloud computing power, before returning the results locally. The software architecture of the main control module is described in detail below.

[0033] (c) Output and Interaction Module: This module includes both audio output and text display, as well as status indicators and input control interfaces. Audio output preferably utilizes a miniature speaker, integrated into the head-mounted device or connected via Bluetooth to the user's mobile phone / tablet speaker to play synthesized speech. Text display can be achieved through a mobile app interface, displaying the generated text as large subtitles. If necessary, it can also connect to AR glasses or a projector to generate a visual "human-generated subtitle" in front of the user for those around them to read. In addition, the module has a few buttons or switches for mode switching and emergency stop; it also includes LED indicators to display the device's operating status (such as battery level, connection status, current output mode, etc.). The entire device is battery-powered, with a battery capacity ensuring more than 8 hours of continuous operation. In this embodiment, the device weighs approximately 200 grams, allowing the user to wear it comfortably without significant burden.

[0034] In terms of hardware connectivity, the EEG acquisition module connects to the main control processing module via flexible cables or integrated circuits (if the main control is inside the headset), or transmits data to the main control via low-power wireless (if the main control is on a mobile phone). The results processed by the main control processing module are sent to a mobile app via Bluetooth for display, and can also output speech through the same device or a separate speaker. The entire system is primarily wireless, reducing the constraints of wires and increasing the freedom of wearing the device. Upon initial use, users need to adjust the device fit and calibrate the system: positioning the electrodes in the optimal position, adjusting the tightness, and checking the signal quality of each channel; then, a guided calibration procedure is initiated, allowing the user to sequentially imagine / recite several sets of commonly used words or simple sentences. The system records these brain signal patterns to train the initial intention decoding model. For example, during calibration, the user sees / hears words such as "hello," "yes," "no," and "thank you," and attempts to "say" these words mentally. The system simultaneously records the corresponding EEG features to establish an initial mapping between brain signals and basic semantics. Once calibration is complete, the device can enter normal operating mode.

[0035] The main control processing module's software primarily consists of an intent decoding module, a semantic synchronization engine, and an NLG generation module. Their collaborative workflow is as follows:

[0036] 1. Brain Intent Decoding Algorithm Flow: After receiving the real-time EEG data stream, the main control processing module first performs a series of preprocessing steps to extract effective features. These include: Fast Fourier Transform (FFT) or Wavelet Transform to extract power in each major frequency band, bandpass filtering (e.g., retaining 0.5-40Hz to remove DC drift, EMG, and high-frequency noise), and Independent Component Analysis (ICA) to remove artifacts such as eye movement and muscle activity. Subsequently, the cleaned EEG data segments are fed into a pre-trained intent decoding model. This model can consist of a shallow convolutional neural network (CNN) or a long short-term memory network (LSTM), used to classify and identify whether a certain semantic intent appears and the approximate category of the intent from EEG signals within a short time window. During training, guided examples are used to have users think of some common phrases and label the corresponding brain signals, thereby learning the mapping relationship between brain signal features and semantic labels. For example, when a user is about to speak, their EEG may show characteristic alpha wave suppression and beta wave energy bursts, which the model uses to determine that the user is beginning to express an intention. Similarly, when a user is thinking of a specific word, a specific electrical potential waveform is generated in the corresponding brain region, and the model can map this pattern to the corresponding word labels used during training. The model's output can be one or more possible semantic segments (e.g., words or stems), each with a confidence score. The model also determines whether the current stage is the beginning, middle, or end of an intention to assist the synchronization engine in decision-making. Considering the limited resolution of non-invasive EEG, this embodiment employs a hierarchical multi-level decoding strategy: first, a coarse classification of the general direction of the intention is made (e.g., determining whether it is a "statement" or "question," "expressing feelings," or "making a request," etc.), and then a refinement of identifying possible key information words within that scope. This coarse-to-fine approach improves robustness. The decoding process is highly context-dependent: if the previous sentence discussed the weather, then when ambiguous brain signals appear, the model will prioritize guessing from words related to "weather," thereby improving accuracy. Furthermore, this invention allows for the fusion of multimodal signals to assist decoding, such as combining the user's eye movement and electromyography (EMG) information to improve the accuracy of semantic judgment. For example, when a user's eyes are turned towards a window, they are likely interested in discussing the view outside; or if EMG signals are detected indicating that the user is attempting to speak, it suggests an urgent desire to output the current content. All this information is integrated and input into the decoding algorithm. The intent decoding module performs a judgment on the latest signal window every extremely short interval (e.g., 100 milliseconds), continuously outputting intermediate recognition results (which may be partial words or still unstable intent labels). These outputs are then passed to the semantic synchronization engine for further processing. If the confidence level of an output is too low or the result is unclear, the semantic synchronization engine can temporarily ignore it, waiting for a more definitive signal to confirm or supplement it.

[0037] 2. Semantic Synchronization Engine Processing: The semantic synchronization engine continuously monitors the output stream from the decoding module and sequentially fills the identified semantic fragments into its internal semantic buffer. The buffer can accumulate sentence components using a key-value structure or other representation methods. For example, if the previous stage decoded the keyword "hot," the buffer is updated to {topic: weather, attribute: hot}. If the decoding then identifies "don't go out," the buffer becomes {topic: weather, attribute: hot, suggestion: don't go out}. The semantic synchronization engine uses context management to determine that these fragments belong to the same sentence and appropriately combines and corrects the buffered content (e.g., removing obviously repetitive or conflicting information). The synchronization control mechanism continuously checks whether the output conditions are met according to rules: First, if a user's thought pause is detected for more than a certain time threshold (e.g., no new relevant intent signal is detected for more than 0.5 seconds); second, or a clear intent termination marker is detected (e.g., a K-complex or other specific pattern appears in the EEG, indicating the end of the sentence); third, or the semantic buffer already contains the core elements constituting a complete sentence (e.g., subject, predicate, object, etc.) and the confidence level of the decoded signals is high. If any condition is met, the current sentence is considered complete and output can be triggered. At this point, the engine packages the buffer contents and sends them to the NLG module, temporarily freezing (or clearing) the buffer for the next sentence. If the user begins another intention while the NLG is generating the final sentence (e.g., the decoding module outputs new words while the NLG is still generating), the synchronization engine can start a new buffer thread to temporarily store this content, achieving a degree of pipeline parallelism and making the user's thought process and system output continuous like an assembly line. The synchronization engine also determines the output format: selecting either speech or text mode based on the current context (see the context-aware section below). Throughout the semantic synchronization process, the engine references information provided by context management. For example, if the previous sentence was a question, the current sentence is likely an answer, and the engine expects semantic elements related to the answer to appear in the decoding result; similarly, if the current dialogue partner is different (doctor, family, friend), the synchronization engine can also provide different tone parameter settings for the NLG. Through this mechanism, the semantic synchronization engine ensures that the system's output rhythm keeps up with the user's train of thought, preventing situations where the user wants to speak but cannot hear anything, or where the system abruptly interrupts the user's thinking. Its effect is equivalent to making the speech speed of this aphasic patient almost synchronize with that of an ordinary person, so that what he thinks is what he says.

[0038] 3. NLG Generation and Output: When the semantic synchronization engine determines that output is needed, it passes the current semantic buffer content to the NLG module. The NLG module performs text generation tasks based on a pre-trained large-scale language model. In this embodiment, we use a small GPT model that has been fine-tuned for dialogue scenarios as the NLG engine. This model accepts two main inputs: the semantic fragment buffer content and contextual information (including recent dialogue text, the identity of the speaker, etc.). To ensure that the generated results are consistent with the user's personality, we also inject the user's corpus examples and style preferences as references during model inference. For example, we add sentences from the user's past expressions to the prompts through Few-Shot examples, allowing the model to learn the user's tone; we also pass user emotion parameters, such as excitement, happiness, or frustration, through additional control vectors, thereby affecting the wording and punctuation of the generated sentences (the acquisition of emotion parameters is discussed later). During the generation process, the model fully considers the continuity of the dialogue context: the preceding content provided by context management is added to the generated prompts, so that the model understands the connection between the current sentence and the preceding text, thereby avoiding irrelevant answers or repetition of the previous sentence. The model generates one or more candidate sentences and their corresponding confidence levels. The semantic synchronization engine can select candidates as needed: for example, in cases of uncertainty, the engine can allow the device to "read" multiple candidate sentences silently in the user's mind or display them on the screen using its internal synthesized voice, allowing the user to indicate which one they are satisfied with through brain signals (e.g., giving a positive thought to a satisfactory sentence in their mind, eliciting a specific EEG response), and then select the most suitable sentence to output. If the user does not provide any special feedback, the system defaults to outputting the sentence with the highest confidence level and content that fits the context. After selecting the text, it is sent to the TTS (Text-to-Speech) module to generate speech, or directly sent to the output module in text form. The entire generation and output process is highly optimized to reduce latency: ideally, the latency from when the user generates an idea to when the device outputs speech can be controlled within 1 to 2 seconds. This interval is almost equivalent to the thought pause of a normal person speaking (e.g., the time it takes for a person to think before answering a question), allowing the dialogue to proceed naturally. This embodiment compresses the single-sentence generation time to about 1 second through methods such as compressing the model size, accelerating the C++ inference engine, and local / cloud collaboration. In addition, the speech synthesis module pre-records the patient's own voice sample for customization, so that the output speech will use a voice close to the patient's real voice, which will improve the intimacy of communication.

[0039] 4. Context Awareness and Security Control: Throughout the system's operation, a parallel monitoring module is responsible for content security and environmental adaptation. First, a semantic security filtering mechanism checks the text generated by NLG. On one hand, it compares the generated text with the original semantic buffer content to ensure that the meaning of the output sentence matches the user's original intent, without misinterpretation or unauthorized additions. If a significant inconsistency is detected (such as NLG generating sentences unrelated to the previous semantics), the engine will refuse output, prompting the user to re-decode or retry the expression. On the other hand, the system has built-in rules for filtering inappropriate content, such as disallowing the output of insulting, discriminatory, or other inappropriate language, especially in medical scenarios, prohibiting sentences that conflict with specific medical instructions. These rules can be implemented based on keyword lists or machine learning content moderation models. If the NLG model accidentally generates content that is inconsistent with the patient's identity or inappropriate for the time being, the filtering module will intercept it and require regeneration, prioritizing content safety and appropriateness even if it is slower. Second, the environmental context awareness function adjusts the output mode by detecting external environmental information. The device can use a built-in microphone to monitor the ambient noise level or automatically switch between voice and text output based on the user's pre-set scenario mode. For example, when the surrounding environment is very quiet (such as a hospital ward or library late at night), the system will default to text mode, sending the output as a text message to the user's mobile phone or nurse call terminal without making any sound to avoid disturbing others. Conversely, in noisy environments (such as an outdoor market), the system will prioritize voice mode and appropriately increase the volume to ensure that the other party can hear clearly. Users can also manually set the current scene mode through a physical switch or mobile app interface. In some scenarios, text mode output will directly display the generated text on the user's mobile phone or connected display screen; in medical scenarios, it can also be simultaneously pushed to the terminal device at the nurse station, allowing medical staff to promptly check the patient's needs. Finally, an emergency interruption mechanism ensures that the user has ultimate control over the output. Users can customize a brain signal pattern that is easy to generate and unlikely to be accidentally triggered as a "cancel" command (e.g., imagine clenching your fist tightly for 3 seconds), and the system will continuously monitor this pattern in the background. Once the signal is detected, the current voice playback will be stopped immediately or the unsent text will be retracted. At the same time, the system will issue a prompt tone or vibration to confirm successful termination. This function can prevent the output of undesirable text due to system misunderstanding or user change of mind. When a user finds that the NLG-generated content is inaccurate or inappropriate, the output process can be quickly interrupted. The synchronization engine then clears the buffer or reverts to a previous stage, awaiting further brain signal input from the user for regeneration. These safety and contextual mechanisms make the device more practical and reliable in complex and ever-changing real-world environments, minimizing the risk of errors and misuse.

[0040] 5. Adaptive Learning and Optimization: This device possesses continuous adaptive learning capabilities during use, constantly optimizing the performance of each module to better meet user needs. This is mainly reflected in two aspects: firstly, online updates to the intent decoding model; and secondly, adjustments to user preferences in the language generation module. For the intent decoding model, the system records the relationship between the user's brain signals and the actual generated results before and after each output. If frequent errors in the decoding results are detected, for example, a certain brain signal pattern is always misidentified as word A but the user always corrects it to word B, the system will gradually adjust the weight of that pattern in the model, making it more inclined to B next time. This adjustment can be achieved through periodic batch updates or incremental learning; for example, a fine-tuning training is triggered every 10 minutes of accumulated interaction data. During training, the weights of the last few layers of the decoding network are adjusted step by step using recently collected data as samples, thereby calibrating the model's recognition accuracy for specific user brain signals. For the NLG module, the system updates the personalized language style library based on user feedback. If a user repeatedly expresses dissatisfaction with a certain type of output (either by explicitly clicking the app's error correction button or by detecting brain feedback signals such as frowning through EEG), the system will reduce the probability of such wording appearing in future outputs. Conversely, if a user reacts positively to certain outputs (e.g., brainwave features such as smiling or feeling happy are detected), the system will add the relevant expressions to its preference library for future generation. The system can even interact with the user for proofreading when necessary: ​​for example, when a generated sentence is detected to have low confidence, instead of directly broadcasting it aloud, the system will display the generated text on the phone screen and highlight the uncertain parts, waiting for the user to choose "accept" or "modify" through brain signals or eye movements. If the user chooses to modify, they can spell out the correct words and phrases letter by letter through a brain-computer interface or have a caregiver input them for them, and the system will record the correct version for future learning. This human-machine co-shaping process makes the device increasingly "understand" the user, continuously improving communication effectiveness. After a period of long-term use, each user's device will be trained with a highly personalized "semantic digital twin" model, capable of accurately mapping their brain signals and language expression habits. Ideally, the device can almost immediately and accurately express whatever the user thinks, achieving the state of "as fast as the mind, so fast as the words".

[0041] Through the aforementioned hardware and software co-design, the device of this invention can reliably interpret semantics in the user's mind and simultaneously output natural language, greatly improving the communication ability of patients with aphasia or paralysis. In different application scenarios, the various modules of the device will intelligently cooperate to achieve the best results. The following examples illustrate the implementation of this invention in specific usage scenarios.

[0042] Example 1: Everyday Conversation

[0043] A patient with advanced ALS (amyotrophic lateral sclerosis) wore this device during a family dinner. During the conversation, when someone mentioned the weather, he immediately thought, "It's so hot today, let's turn on the air conditioning." Because of his illness, he was completely unable to speak, but the device came into play: the intent decoding module first captured the conceptual signal about "weather"—this was because the patient had just heard a family member mention the weather, and his brain correspondingly produced the familiar P300 component (a characteristic waveform that appears in the brain when we recognize a stimulus of interest). Next, the decoding module extracted the keyword signal representing the feeling of "heat," because the patient had a sensation of heat in his mind. At this point, the semantic buffer had formed preliminary content: {Topic: Weather, Feeling: Hot, Suggestion: Turn on the air conditioning}. The semantic synchronization engine combined these fragments into complete semantics (determining that the patient wanted to express a suggestion about the hot weather and a desire to turn on the air conditioning) while continuing to listen for any new additions. About one second later, the engine detected that the patient's brainwaves gradually returned to a relaxed baseline state, and no new intent signals appeared. According to the synchronization control strategy, the engine determined that the expression was complete and immediately triggered the NLG module to generate the expression. NLG, referencing the context of the family's conversation (the topic was the weather, and they were discussing whether to turn on the air conditioning) and the patient's usual speaking style, generated a polite inquiry: "I feel so hot today, could you turn on the air conditioning?" The sentence not only contained the core meaning of "It's hot, turn on the air conditioning," but also incorporated polite questioning, consistent with the patient's usual expression habits—because in previous personalized training, the model learned that the patient spoke politely and tactfully, frequently using a tone of inquiry and discussion. The main controller then sent the generated text to the speech synthesizer. Since the patient had previously provided their own voice sample for customized TTS, the device's miniature speaker spoke the sentence in a voice close to their real voice. From the moment the thought flashed through the patient's mind to the words being "spoken," the entire process took less than two seconds. The family only felt a slight pause before hearing the patient "speak" and offer their suggestion; the delay was almost imperceptible. At that moment, the whole family was overjoyed to see that although the patient could not move or speak, they could communicate normally with them through thought. Previously, such natural communication scenes could only appear in science fiction movies; but now, through the device of this invention, patients' voices can be directly conveyed to their families without any detours for the first time, and the conversations at the dinner table have regained their long-lost natural rhythm and laughter.

[0044] Example 2: Medical Consultation

[0045] A stroke patient recovering from aphasia wore this device and accompanied her family to a follow-up appointment at the hospital. During the routine doctor's consultation, she was asked, "Where have you been feeling unwell lately?" Although the patient couldn't answer verbally, she understood the doctor's question. She began to organize her thoughts, thinking, "My stomach hurts a little, and I didn't sleep well last night." This actually contained two pieces of information: one was physical discomfort—stomach pain; the other was her lifestyle—insomnia the previous night. Because the patient's language center was damaged, she couldn't express herself fluently, but the brain-computer interface captured the relevant brain signal changes at that moment: First, when she recalled the stomach pain, abnormal rhythms appeared in the electrical waves representing the stomach area in the somatosensory cortex of the brain. Because imagining pain in a certain part of the body activates the corresponding somatosensory area, the device's intention decoding model, through training, had learned to recognize this pattern and mapped it to the semantic label "stomach pain." Almost simultaneously, the patient recalled the insomnia of the previous night, and characteristic slow-wave activity appeared in areas such as the hippocampus and frontal lobe—these are sleep-related brainwave signals. The decoding module also captured this pattern, outputting a conceptual label related to "poor sleep." The semantic synchronization engine merges the two segments to obtain a rough semantic content: "Stomach ache; didn't sleep well last night." The doctor is still waiting for a response, and all of this is completed within a second or two. Next, the NLG module generates the sentence based on the semantics: "I feel a bit of stomach pain, and I didn't sleep well last night." During generation, NLG takes into account the hospital setting and chooses a calm, declarative tone rather than everyday speech (because our style library has been optimized for more formal doctor-patient dialogue). After the speech output, the doctor accurately understands the patient's discomfort and immediately proceeds with targeted examinations—for example, focusing on her stomach and inquiring about her sleep problems. The entire consultation process becomes smooth and efficient thanks to this device: previously, doctors often had to rely on family members or have patients painstakingly write to guess the patient's condition; now, they can directly "hear" the patient's own description. During the same consultation, the doctor later asked her, "How have you been feeling lately?" In fact, the patient had been feeling quite down lately, but was too introverted to say so directly. She had negative thoughts like "not good" in her mind, but hesitated to show them. However, the device still detected a hint of disappointment in her heart—the EEG emotion analysis module detected negative emotional signals (possibly due to asymmetrical activity in the frontal lobe indicating negative emotions), and also decoded a vague word fragment, "bad." The semantic synchronization engine combined these clues to infer that the emotion she actually wanted to express was negative. Based on this, the NLG generated a more tactful response: "I'm feeling a bit down, I feel a bit depressed." The patient nodded slightly during the speech output, indicating her acceptance. The doctor understood her true psychological state and suggested arranging psychological counseling to help her regulate her emotions. Through this device, many complex inner thoughts that patients find difficult to express can be conveyed, helping doctors make comprehensive diagnoses and treatment plans.Without this equipment, the patient might not be able to clearly describe their pain and emotions, leading to diagnoses based on guesswork and significantly reduced effectiveness. This invention makes doctor-patient communication more direct and accurate, improving the healthcare experience.

[0046] Example 3: Group Communication and Entertainment

[0047] A young man with high-level paralysis regained his social and recreational life using this device. He loved playing online games, but in the past, he could only communicate with teammates slowly by typing, often falling behind the game's pace. Now, wearing a brain-computer interface (BCI) device, he can communicate strategies with teammates in real time via thought. In a shooting game, he noticed the enemy flanking from the left and mentally shouted, "Flank from the left!" Almost simultaneously, the device captured his urgent intention, rapidly decoding the keywords "left" and "flank." The semantic synchronization engine determined this was an immediate warning message and quickly triggered a brain-computer interface (NLG). Because of the fast-paced nature of the scenario, the NLG was intentionally set to a short, powerful sentence mode. The NLG was immediately generated and shouted through the microphone: "Someone is flanking from the left, let's attack them from both sides!" This series of words was even faster than his able-bodied teammates typing manually, promptly reminding them to take action. Seconds later, they successfully turned the tide of the game. Teammates were amazed by the player's composure and lightning-fast reflexes despite his paralysis. He himself experienced for the first time the joy of participating equally in fast-paced teamwork—an experience unimaginable in the past. Beyond communication, he used the device's interface to map brain signals into game commands, enabling him to "play games with his brain": for example, imagining pressing a button with his right hand to control firing in the game, or focusing on a specific area of ​​the screen to move the camera. These mappings were achieved through the device's API and game control module, allowing him to perform many actions previously requiring manual dexterity. In another scenario, the young man, wearing the device, attended a friend's offline gathering. Initially reserved, his friends gradually forgot his disability as he naturally joined in conversations and joked through the device, treating him like a normal person, chatting and teasing him. His jokes, delivered using the brain-computer interface, drew laughter from the entire party, fully immersing him in the joyful atmosphere. Someone remarked with emotion, "At first, we were worried you wouldn't be able to communicate easily, but we didn't expect you to 'speak' even more fluently than us now!" For this patient who had experienced long-term loneliness, being treated like an ordinary person almost brought him to tears. He later stated that this was not only entertainment but also a form of psychological rehabilitation—the device allowed him to rediscover the precious experience of fighting alongside friends and sharing laughter. The wearable nature of this invention enables him to interact naturally in both virtual game worlds and real-world social and learning scenarios, no longer confined to a wheelchair and keyboard. This is of great significance to his psychological recovery and restoration of self-confidence.

[0048] Example 4: Rehabilitation Training Assistance

[0049] A patient with moderate cognitive impairment used this device to practice language description during a rehabilitation training session. The training software displayed an image on the screen and asked her to describe what she saw in one sentence. This time, the image showed a kitten. The patient knew in her mind that it was a cute kitten, but due to her speech impairment, she could not speak a complete sentence fluently. She silently repeated "cute kitten" in her mind, but could only utter indistinct sounds. At this moment, the device's intention capture module captured the intention signal related to "cat" and the evaluative brain signal of "cute". In fact, we have specifically optimized the intention decoding model for common rehabilitation training vocabulary (colors, animals, adjectives, etc.), so even though this patient could not speak, the device could accurately identify the semantic meaning of keywords such as "cat" and "cute" that appeared in her mind. The semantic synchronization engine combined these keywords, and the NLG module generated a reasonable description: "This is a very cute kitten." The device then output this sentence verbally. The speech therapist sitting next to her nodded with satisfaction and said, "Very good, you described it correctly!" and immediately gave positive feedback and encouragement. Without this device, therapists might only see the patient opening her mouth but unable to speak, potentially mistaking her inability to understand images or express herself, thus missing opportunities to affirm her abilities. Through this device, the patient's true thoughts are directly presented to the therapist for the first time, helping experts more accurately assess her cognitive level and language comprehension, thereby adjusting the training plan. In subsequent training, whenever the patient wanted to express herself but couldn't, the device would speak for her. This experience subtly helped her practice her brain's language organization and expression functions—despite the aid of a machine, her brain's language-related areas were repeatedly activated and strengthened. After several months of training, her dependence on the device decreased, and she could even speak parts of some simple sentences independently. This demonstrates that the invention not only supports her current communication like a crutch but also acts as a rehabilitation training tool, helping her brain's language center re-establish connections and exert a therapeutic effect through continuous "semantic synchronization." The therapist exclaimed: "With this device, we not only know what she's thinking but also help her relearn how to speak." This was unimaginable in the past.

[0050] Example 5: Quiet Environment and Emergency Communication

[0051] A late-stage ALS patient, lying in a quiet ward at night, needed to call for a nurse's assistance but didn't want to wake his roommate who was resting. He used a brain-computer interface (BCI) device to achieve this. Considering the quiet environment and the late hour, the device automatically switched to silent text mode: without uttering a sound, it sent the output as a text message to the nurses' station. The patient thought, "I need help going to the restroom." The intent decoding module quickly recognized the key semantic signals such as "requesting help" and "going to the toilet." The semantic synchronization engine determined that this was an urgent request and, without waiting for further details, immediately triggered the numeric logic generator (NLG) to output the message. Since text mode had already been enabled for the hospital's nighttime setting, the NLG generated concise and clear text: "Please help me to the restroom." The system directly sent this text to the nurses' call terminal via the hospital's internal network. Almost simultaneously with the patient's thought, his request for help popped up on the screen at the nurses' station. The on-duty nurse immediately rushed to the ward and helped him to the restroom. The entire process was silent, resolving the patient's urgent need without disturbing the other patients in the room.

[0052] In the above process, the device's context-aware function played a crucial role: detecting a quiet, late-night environment, it automatically switched the output to text and notified caregivers through a specific channel. This ensures that patients can appropriately express their needs in various environments. In actual use, the patient also experienced the emergency interruption function. For example, once he was about to ask a nurse for medication, intending to say "medication," but the initial sentence generated by the NLG contained unnecessary information. He immediately realized the error and quickly triggered a pre-defined brain signal termination command (he set it to imagine himself suddenly closing his eyes as the termination trigger). Upon recognizing this, the system immediately canceled the text being prepared for output, without sending the erroneous content. Subsequently, he refocused his thoughts on the concept of "medication" and triggered the output again. This time, the system correctly generated the brief message: "Need medication," successfully notifying the nurse. This mechanism ensures that patients only send requests when they are satisfied, preventing misunderstandings or embarrassment caused by incorrect output. As can be seen from Example 5, the device of the present invention can automatically adopt an appropriate output method (such as text replacing speech) in special situations such as quiet places and nighttime, and combined with safety functions such as emergency interruption, to meet the communication needs of users in sensitive scenarios. When used in multi-bed wards, the device can also interface with the hospital nursing system to directly send the patient's thought requests to the on-duty nurse, improving call efficiency and response speed. This new communication method provides more humane and comprehensive service guarantees for critically ill aphasic patients.

[0053] The above embodiments demonstrate the significant effects of the wearable brain-computer semantic synchronization device of the present invention on users in different scenarios: enabling aphasic individuals to "speak" again, allowing paralyzed individuals to direct their actions with their thoughts, and allowing consciousness trapped within the body to express itself freely, greatly improving the quality of communication and interaction and boosting patients' confidence in rehabilitation. Its innovation lies in the interdisciplinary integration of brain-computer interface and natural language generation technology, achieving an unprecedented human-computer collaborative communication method, and being applied in a lightweight and wearable form. Each functional module of this device is based on existing mature technologies with improvements and integration, resulting in low development risk, while generating extremely high social benefits and commercial value after integration. In summary, the present invention possesses sufficient novelty, inventiveness, and industrial feasibility, and has broad application prospects in the field of medical rehabilitation.

Claims

1. A wearable brain-computer semantic synchronization device, comprising an electroencephalogram (EEG) signal acquisition unit, a main processing unit, and an output unit, characterized in that: The main processing unit has a built-in intention decoding module, a semantic synchronization engine, and a natural language generation (NLG) module. The intention decoding module processes the real-time acquired EEG signals and extracts semantic information fragments corresponding to the user's intention. The semantic synchronization engine caches the continuously extracted semantic information fragments and manages the context. When it is determined that the user has formed a complete expressive intention, it triggers the NLG module to automatically expand the cached semantic information into a complete natural language sentence. The output unit outputs the natural language sentence through speech synthesis or text display, realizing the synchronous conversion between the user's brain intention and external language expression.

2. The apparatus according to claim 1, characterized in that: The EEG signal acquisition unit adopts a head-mounted EEG electrode array, which includes multiple electrodes placed on the user's forehead and temporal region to acquire EEG signals from language-related brain regions; the main processing unit is an embedded computing module integrated into a wearable device, or performs calculations through a wirelessly connected mobile terminal; the output unit includes a miniature speaker and / or an interface connected to a mobile terminal display screen for outputting speech and text information.

3. The apparatus according to claim 1, characterized in that: The intent decoding module uses a pre-trained neural network model to identify EEG features of specific semantic categories. When a pattern corresponding to a predetermined semantic label appears in the user's brain signal, the module outputs the semantic information fragment and its confidence level. The semantic synchronization engine does not trigger output for semantic fragments with low confidence or ambiguity until they are confirmed or supplemented by subsequent brain signals. The semantic synchronization engine has a context maintenance function, which can limit the candidate range according to the dialogue background to improve the accuracy of intent decoding.

4. The apparatus according to claim 1, characterized in that: The natural language generation module uses a language model that has been personalized and optimized by the user to generate fluent text that matches the user's style based on the content of the semantic fragment buffer and contextual information. The NLG module accepts additional parameters to control the tone and style of the output, including user emotion parameters obtained from brain signal emotion analysis and language style parameters trained from the user's historical language preferences. The generated text undergoes semantic safety checks before output to ensure that it is consistent with the user's original intent and contains no inappropriate content.

5. The apparatus according to claim 1, characterized in that: The device has a continuous learning function. By recording the correspondence between the user's brain signals and the output results, it continuously updates the model parameters of the intent decoding module and the user style library of the NLG module. When the user corrects or confirms the output results (including expressing satisfaction or dissatisfaction through brain signals), the device uses this feedback to strengthen learning and adjustment, so that intent decoding and language generation are more in line with the user's true intentions and expression habits.

6. The apparatus according to claim 1, characterized in that: The device's structure and materials are suitable for long-term wear, featuring an electrode self-adaptive fit and conductive gel reduction design. The device is lightweight and has a battery life of no less than 8 hours. The device has a mode switching switch that can switch between voice output and text output to adapt to silent communication in quiet environments. The device is equipped with an emergency interrupt command, which allows the system to immediately stop the current output when the user generates a preset special brain signal pattern or presses the safety button, allowing the user to correct or cancel the output.

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