1. A non-invasive brain-computer interface system for diagnosing neurological disorders, comprising: a multi-array EEG sensor unit configured to detect and record neural signals with enhanced spatial resolution; a machine learning-based signal processing module designed to distinguish pathological neural patterns from healthy ones; a real-time feedback mechanism to inform the user of detected anomalies, addressing the delayed diagnostic feedback issues in previous systems; a cloud-based data storage with encrypted security for remote expert consultations; and an integrated calibration protocol that adjusts to individual neurological baselines, addressing variability in prior systems.

2. The system of claim 1, wherein the EEG sensor unit incorporates flexible materials for better conformability and comfort, addressing discomfort issues observed with rigid sensors in prior art.

3. The system of claim 1, wherein the machine learning-based signal processing module uses a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) for enhanced temporal and spatial pattern recognition.

4. The system of claim 1, further comprising an adaptive noise-cancellation mechanism that utilizes ambient sound sensors to filter environmental interferences, addressing the noise contamination challenge of earlier designs.

5. The system of claim 1, wherein the real-time feedback mechanism includes a haptic alert system, enabling notifications even when auditory or visual feedback may be impaired.

6. A method for diagnosing neurological disorders using a non-invasive brain-computer interface, comprising: placing a multi-array EEG sensor unit on a user's scalp; recording neural signals with enhanced spatial resolution; processing these signals using a machine learning algorithm to identify pathological patterns; providing real-time feedback to the user regarding any detected anomalies; and adjusting the system calibration based on individual neurological baselines.

7. The method of claim 6, further including: employing a flexible EEG sensor to ensure user comfort during the diagnostic process, addressing physical discomfort issues from prior methods.

8. The method of claim 6, wherein the neural signal processing step involves: utilizing a combination of CNN and RNN algorithms to analyze both spatial and temporal patterns in the neural data, offering a comprehensive diagnostic perspective.

9. The method of claim 6, further comprising: employing an adaptive noise-cancellation mechanism that filters out environmental noises using ambient sound sensors, ensuring cleaner neural data recordings.

10. The method of claim 6, wherein upon detection of a potential neurological disorder pattern, the method involves: triggering a haptic alert to the user, ensuring notification accessibility even in scenarios with visual or auditory challenges. Non-Invasive Brain-Computer Interfaces for Diagnosis of Neurological Disorders Abstract Introducing a groundbreaking non-invasive brain-computer interface system, poised to revolutionize the diagnostic landscape of neurological disorders. At its forefront is a multi-array EEG sensor unit, masterfully calibrated to discern and chronicle neural signals, boasting unparalleled spatial fidelity. Seamlessly interfaced with this is a machine learning-imbued signal processing module, meticulously engineered to delineate pathological neural manifestations from normative patterns. Addressing historical diagnostic lags, an immediate feedback apparatus is integrated, apprising users of detected neural aberrations in real-time. Elevating its diagnostic prowess, a cloud-anchored data repository, fortified with state-of-the-art encryption, facilitates remote consultations with neurological experts. A standout feature is its bespoke calibration protocol, adeptly tailoring the system to individual neurological baselines, rectifying the variability constraints of antecedent systems. Collectively, this interface signifies a paradigm shift in neuro-diagnosis, melding precision with immediacy and individualization. , Claims:Claims :

1. A non-invasive brain-computer interface system for diagnosing neurological disorders, comprising: a multi-array EEG sensor unit configured to detect and record neural signals with enhanced spatial resolution; a machine learning-based signal processing module designed to distinguish pathological neural patterns from healthy ones; a real-time feedback mechanism to inform the user of detected anomalies, addressing the delayed diagnostic feedback issues in previous systems; a cloud-based data storage with encrypted security for remote expert consultations; and an integrated calibration protocol that adjusts to individual neurological baselines, addressing variability in prior systems.

2. The system of claim 1, wherein the EEG sensor unit incorporates flexible materials for better conformability and comfort, addressing discomfort issues observed with rigid sensors in prior art.

3. The system of claim 1, wherein the machine learning-based signal processing module uses a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) for enhanced temporal and spatial pattern recognition.

4. The system of claim 1, further comprising an adaptive noise-cancellation mechanism that utilizes ambient sound sensors to filter environmental interferences, addressing the noise contamination challenge of earlier designs.

5. The system of claim 1, wherein the real-time feedback mechanism includes a haptic alert system, enabling notifications even when auditory or visual feedback may be impaired.

6. A method for diagnosing neurological disorders using a non-invasive brain-computer interface, comprising: placing a multi-array EEG sensor unit on a user's scalp; recording neural signals with enhanced spatial resolution; processing these signals using a machine learning algorithm to identify pathological patterns; providing real-time feedback to the user regarding any detected anomalies; and adjusting the system calibration based on individual neurological baselines.

7. The method of claim 6, further including: employing a flexible EEG sensor to ensure user comfort during the diagnostic process, addressing physical discomfort issues from prior methods.

8. The method of claim 6, wherein the neural signal processing step involves: utilizing a combination of CNN and RNN algorithms to analyze both spatial and temporal patterns in the neural data, offering a comprehensive diagnostic perspective.

9. The method of claim 6, further comprising: employing an adaptive noise-cancellation mechanism that filters out environmental noises using ambient sound sensors, ensuring cleaner neural data recordings.

10. The method of claim 6, wherein upon detection of a potential neurological disorder pattern, the method involves: triggering a haptic alert to the user, ensuring notification accessibility even in scenarios with visual or auditory challenges.

Description:Non-Invasive Brain-Computer Interfaces for Diagnosis of Neurological Disorders
Field of the Invention
[0001] The present invention relates generally to brain-computer interfaces (BCIs). More specifically, the invention pertains to non-invasive brain-computer interface systems and methods designed for the diagnosis of neurological disorders by capturing, analyzing, and interpreting neural signals from an individual's brain without the need for surgical interventions. The described interfaces leverage advanced signal processing, machine learning algorithms, and adaptive feedback systems to enhance accuracy and reliability in the detection, differentiation, and monitoring of various neurological conditions.
Background
[0002] The background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[0003] Understanding and diagnosing neurological disorders present a significant challenge in the field of medical science. Traditional diagnostic methods often require invasive procedures, limiting patient comfort and accessibility. Non-Invasive Brain-Computer Interfaces (BCIs) have emerged as a revolutionary approach to diagnose neurological disorders by directly interfacing with the brain's activity. This exploration delves into the concept of non-invasive BCIs for diagnosing neurological disorders, their implications, and real-world applications.
[0004] Neurological disorders, ranging from epilepsy to Alzheimer's disease, manifest through complex brain activity patterns. Diagnosing these disorders traditionally involves techniques like electroencephalography (EEG) or magnetic resonance imaging (MRI), which can be time-consuming, costly, and invasive. Non-invasive solutions are sought to enable early diagnosis and personalized treatment plans.
[0005] Non-invasive BCIs utilize sensors to detect and record brain activity without the need for surgical interventions. These sensors capture electrical signals, neural patterns, and brainwaves, offering insights into the brain's functioning. By analyzing these patterns, anomalies associated with neurological disorders can be identified.
[0006] Advanced signal processing algorithms analyze the captured brain signals to extract meaningful patterns. Machine learning and AI techniques are employed to recognize abnormal brain activity patterns associated with specific neurological disorders. These patterns can then be used for accurate diagnosis.
[0007] Non-invasive BCIs provide real-time feedback to healthcare professionals. They enable dynamic monitoring of brain activity, facilitating quick responses to changes and abnormalities. This real-time aspect is particularly crucial for conditions like epilepsy, where immediate intervention is necessary to prevent seizures.
[0008] A study conducted at the University of Pittsburgh developed a non-invasive Brain-Computer Interface (BCI) to assist patients with motor disorders. The BCI decoded brain signals related to movement intentions, enabling patients to control robotic limbs. This technology has implications for disorders like amyotrophic lateral sclerosis (ALS) and spinal cord injuries.
[0009] Researchers at MIT developed a non-invasive BCI to detect early signs of Alzheimer's disease. By analyzing brainwave patterns during memory tasks, the BCI identified subtle cognitive impairments that could indicate Alzheimer's disease, enabling early intervention and treatment.
[00010] Non-invasive BCIs have been used to diagnose epilepsy and predict seizures. A study published in the journal "Epilepsy & Behavior" demonstrated that analyzing brainwave patterns using non-invasive BCIs could accurately identify patients with epilepsy and predict seizures, improving patient care and quality of life.
[00011] Non-invasive BCIs have enabled communication for patients with locked-in syndrome, a condition where individuals are conscious but unable to move or communicate. The BCI interprets their brain signals to generate text or speech, providing a means of communication and enhancing their quality of life.
[00012] Non-invasive BCIs represent a revolutionary approach to diagnosing neurological disorders. Prior art examples from motor disorders, Alzheimer's disease, epilepsy diagnosis, and communication for locked-in patients demonstrate the diverse applications of these interfaces. As technology continues to evolve, the potential to accurately diagnose neurological disorders early and provide personalized treatment plans is promising, revolutionizing the field of neuroscience and patient care.
[00013]
[00014] All references, including publications, patent applications, and patents, cited herein are hereby incorporated by reference to the same extent as if each reference were individually and specifically indicated to be incorporated by reference and were set forth in its entirety herein.
[00015] It also shall be noted that as used herein and in the appended claims, the singular forms “a”, “an”, and “the” include plural referents unless the context clearly dictates otherwise. This invention can be achieved by means of hardware including several different elements or by means of a suitably programmed computer. In the unit claims that list several means, several ones among these means can be specifically embodied in the same hardware item. The use of such words as first, second, third does not represent any order, which can be simply explained as names.
Summary
[00016] The following presents a simplified summary of various aspects of this disclosure in order to provide a basic understanding of such aspects. This summary is not an extensive overview of all contemplated aspects, and is intended to neither identify key or critical elements nor delineate the scope of such aspects. Its purpose is to present some concepts of this disclosure in a simplified form as a prelude to the more detailed description that is presented later.
[00017] The following paragraphs provide additional support for the claims of the subject application.
[00018] The present invention relates generally to brain-computer interfaces (BCIs). More specifically, the invention pertains to non-invasive brain-computer interface systems and methods designed for the diagnosis of neurological disorders by capturing, analyzing, and interpreting neural signals from an individual's brain without the need for surgical interventions. The described interfaces leverage advanced signal processing, machine learning algorithms, and adaptive feedback systems to enhance accuracy and reliability in the detection, differentiation, and monitoring of various neurological conditions.
[00019] The quest for improved diagnostic techniques for neurological disorders has long been a pivotal focus in the realm of neuroscience. The introduction of a state-of-the-art non-invasive brain-computer interface system marks a significant evolution in this endeavor. With its groundbreaking features and comprehensive design, this system stands poised to revolutionize the detection and analysis of neurological anomalies.
[00020] Central to this system is the multi-array EEG sensor unit. Unlike conventional models, this unit boasts enhanced spatial resolution, detecting neural signals with unparalleled precision. Notably, these EEG sensors integrate flexible materials, offering a departure from the discomfort associated with rigid sensors of yesteryears. This not only ensures optimal signal capture but also promotes user comfort, especially during extended diagnostic sessions.
[00021] Processing these neural signals is an advanced machine learning module. Ingeniously designed to discern pathological neural patterns from healthy ones, it employs a fusion of convolutional neural networks (CNN) and recurrent neural networks (RNN). This combination harnesses the strengths of both spatial and temporal pattern recognition, allowing the system to identify even subtle neurological anomalies with remarkable accuracy.
[00022] Addressing a longstanding challenge in neural diagnostics, the system offers a real-time feedback mechanism. In contrast to previous models where diagnostic feedback could be frustratingly delayed, users are instantly informed of detected irregularities. Amplifying its utility, the feedback mechanism is thoughtfully inclusive, incorporating a haptic alert system. This ensures that even if a user's auditory or visual faculties are impaired, vital feedback remains accessible.
[00023] A significant enhancement to the system's robustness is the adaptive noise-cancellation mechanism. By intelligently using ambient sound sensors, the system filters out environmental interferences. This directly confronts the noise contamination challenge that marred earlier designs, ensuring the purity and reliability of neural readings.
[00024] Data security and collaboration are also given paramount importance. The system integrates a cloud-based data storage solution that upholds the highest standards of encryption. This not only safeguards sensitive neurological data but also facilitates remote expert consultations. Such a feature is invaluable in cases where immediate specialist insights are warranted.
[00025] Lastly, recognizing the inherent variability in human neurological baselines, the system embraces an integrated calibration protocol. This tailors the diagnostic process to individual baselines, ensuring that readings and results are uniquely reflective of each user's neural state.
[00026] In essence, this non-invasive brain-computer interface system is not merely a diagnostic tool; it is a synthesis of cutting-edge technology, user-centric design, and medical research. With its promise of precise, comfortable, and rapid neurological diagnostics, the future of neurology looks brighter and distinctly more informed.
[00027] The world of neurological diagnostics has witnessed transformative progress with the advent of a new method using a non-invasive brain-computer interface. This approach seamlessly combines the prowess of advanced hardware with cutting-edge machine learning techniques, offering an effective, comfortable, and responsive means of diagnosing neurological disorders.
[00028] Central to this diagnostic method is the deployment of a multi-array EEG sensor unit. Placed directly on the user's scalp, this sensor plays a pivotal role in capturing neural signals. Distinctly setting itself apart from traditional models, the system boasts enhanced spatial resolution, ensuring neural readings of unparalleled precision. Recognizing the importance of user comfort during this critical process, the method notably employs flexible EEG sensors. This addresses the discomfort issues of past methodologies, making the diagnostic process considerably more user-friendly.
[00029] Once the neural signals are secured, the method's true technological finesse comes to the fore. Leveraging sophisticated machine learning algorithms, it processes these neural signatures to distinguish pathological patterns indicative of potential disorders. Particularly commendable is the system's utilization of a blend of convolutional neural networks (CNN) and recurrent neural networks (RNN). This ensures a holistic examination of neural data, capturing both spatial and temporal patterns, and providing a comprehensive diagnostic perspective.
[00030] Amplifying the reliability of this method is an adaptive noise-cancellation mechanism. This feature, which employs ambient sound sensors, filters out potential environmental interferences that could compromise the clarity of neural recordings. By ensuring cleaner and more accurate neural data, it significantly enhances the diagnostic accuracy.
[00031] A standout feature of this method is its emphasis on real-time responsiveness. Upon processing neural signals and identifying potential anomalies, the system provides instantaneous feedback to users. This proactive approach rectifies the lag seen in previous diagnostic models, ensuring users are apprised immediately of any concerns. Moreover, in scenarios where users might face auditory or visual challenges, the method has the foresight to trigger a haptic alert. This ensures that critical feedback remains accessible, regardless of situational constraints.
[00032] Recognizing the unique neurological baselines inherent to each individual, the method also incorporates a dynamic calibration feature. This ensures that the system adjusts and aligns itself to individual neurological patterns, making the diagnostic process tailor-made for every user.
[00033] In summation, this method presents a monumental leap in the realm of neurological diagnostics. By merging user comfort, advanced data analysis, real-time feedback, and adaptive calibration, it paves the way for a future where neurological disorders are diagnosed swiftly, accurately, and non-invasively. With such at the helm, the future of neurological care appears both promising and enlightened.
[00034]
Brief Description of the Drawings
[00035] The features and advantages of the present disclosure would be more clearly understood from the following description taken in conjunction with the accompanying drawings in which:
[00036] FIG. 1 represents an architectural overview of a non-invasive brain-computer interface system for diagnosing neurological disorders, according to some embodiments of the present disclosure.
[00037] FIG. 2 shows an exemplary detailed schematic flow diagram of a method for diagnosing neurological disorders using a non-invasive brain-computer interface, according to some embodiments of the present disclosure.
[00038]

Detailed Description
[00039] In the following detailed description of the invention, reference is made to the accompanying drawings that form a part hereof, and in which is shown, by way of illustration, specific embodiments in which the invention may be practiced. In the drawings, like numerals describe substantially similar components throughout the several views. These embodiments are described in sufficient detail to claim those skilled in the art to practice the invention. Other embodiments may be utilized and structural, logical, and electrical changes may be made without departing from the scope of the present invention. The following detailed description is, therefore, not to be taken in a limiting sense, and the scope of the present invention is defined only by the appended claims and equivalents thereof.
[00040] The use of the terms “a” and “an” and “the” and “at least one” and similar referents in the context of describing the invention (especially in the context of the following claims) are to be construed to cover both the singular and the plural, unless otherwise indicated herein or clearly contradicted by context. The use of the term “at least one” followed by a list of one or more items (for example, “at least one of A and B”) is to be construed to mean one item selected from the listed items (A or B) or any combination of two or more of the listed items (A and B), unless otherwise indicated herein or clearly contradicted by context. The terms “comprising,” “having,” “including,” and “containing” are to be construed as open-ended terms (i.e., meaning “including, but not limited to,”) unless otherwise noted. Recitation of ranges of values herein are merely intended to serve as a shorthand method of referring individually to each separate value falling within the range, unless otherwise indicated herein, and each separate value is incorporated into the specification as if it were individually recited herein. All methods described herein can be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided herein, is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention unless otherwise claimed. No language in the specification should be construed as indicating any non-claimed element as essential to the practice of the invention.
[00041] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00042] The present invention relates generally to brain-computer interfaces (BCIs). More specifically, the invention pertains to non-invasive brain-computer interface systems and methods designed for the diagnosis of neurological disorders by capturing, analyzing, and interpreting neural signals from an individual's brain without the need for surgical interventions. The described interfaces leverage advanced signal processing, machine learning algorithms, and adaptive feedback systems to enhance accuracy and reliability in the detection, differentiation, and monitoring of various neurological conditions.
[00043] Pursuant to the "Detailed Description" section herein, whenever an element is explicitly associated with a specific numeral for the first time, such association shall be deemed consistent and applicable throughout the entirety of the "Detailed Description" section, unless otherwise expressly stated or contradicted by the context.
[00044] Brain-computer interfaces (BCIs) have revolutionized the way we comprehend, diagnose, and treat neurological disorders. By non-invasively mapping brain activities, these interfaces provide a window into the intricate dance of neurons inside the human brain. One particular system stands out by leveraging a multitude of advancements in technology, neuroscience, and data science to diagnose neurological disorders. Here's a detailed exploration of this non-invasive brain-computer interface system and its unique features.
[00045] According to a pictorial portrayal in FIG. 1, illustrating an architectural setup of the non-invasive brain-computer interface system 100 for diagnosing neurological disorders, comprising a multi-array EEG sensor unit 102 configured to detect and record neural signals with enhanced spatial resolution, a machine learning-based signal processing module 104 designed to distinguish pathological neural patterns from healthy ones, a real-time feedback mechanism 106 to inform the user of detected anomalies, addressing the delayed diagnostic feedback issues in previous systems, a cloud-based data storage 108 with encrypted security for remote expert consultations, and an integrated calibration protocol 110 that adjusts to individual neurological baselines, addressing variability in prior systems.
[00046] At the heart of this brain-computer interface system 100 lies the multi-array EEG sensor unit. EEG, or electroencephalography, is a method used to record electrical activity of the brain. Traditional EEG systems employ a set of electrodes placed on the scalp to detect brain waves. However, the this system is the deployment of a multi-array setup.
[00047] This configuration offers enhanced spatial resolution, capturing the minutest neural signals with precision. Consider a situation where a patient may have a minute irregularity localized in a small region of the brain. With standard EEG systems, this irregularity might go unnoticed, leading to a missed diagnosis. However, with the multi-array setup in this BCI system, the higher resolution can pinpoint such irregularities with commendable accuracy.
[00048] The raw neural signals captured by EEG sensors are rich in data, but deciphering meaningful insights from them requires sophisticated analysis. This is where the machine learning-based signal processing module comes into play. The module is trained to distinguish between healthy neural patterns and pathological ones. This distinction is crucial, as it paves the way for accurate diagnoses. Imagine the neural patterns of a person with a history of seizures versus someone without any neurological ailment. The system's machine learning module can identify even subtle deviations in neural patterns, flagging potential concerns.
[00049] Delving deeper, this module combines the prowess of convolutional neural networks (CNN) and recurrent neural networks (RNN). While CNNs excel in identifying spatial patterns, which are crucial for recognizing features in EEG data, RNNs shine in recognizing temporal patterns, capturing the sequential essence of brain signals. Together, they provide an unprecedented depth of analysis.
[00050] Diagnostic systems in the past have often suffered from delayed feedback, prolonging anxiety and uncertainty for the patient. However, this BCI system introduces a real-time feedback mechanism. The instant the system identifies an anomaly in the neural signals, the user is informed.
[00051] This immediacy holds immense significance in clinical settings. For instance, a patient undergoing a monitoring session can be immediately alerted if an irregular neural pattern, indicative of an impending seizure, is detected. Such prompt alerts can help in timely interventions, potentially averting medical emergencies.
[00052] Furthermore, the feedback mechanism isn't restricted to conventional auditory or visual signals. Recognizing the challenges some patients might have, especially those with sensory impairments, the system integrates a haptic alert system. This means the user can be alerted through tactile sensations, like vibrations, ensuring that critical notifications are never missed.
[00053] Given the sensitive nature of neurological data, its storage and security become paramount. The BCI system employs a cloud-based data storage solution. This not only allows for vast amounts of data to be stored without physical constraints but also facilitates remote access. For instance, if a neurologist based in New York wishes to consult with a colleague in London about a particular case, the cloud-based system allows for seamless sharing of data. However, to ensure that the patient's privacy is uncompromised, the system incorporates robust encrypted security protocols. This ensures that the data remains accessible only to authorized individuals, keeping potential cyber threats at bay.
[00054] Every individual's brain is unique. This means that neurological baselines can vary significantly from one person to another. Recognizing this, the system boasts an integrated calibration protocol. Before any diagnostic session, the system calibrates itself to the user's individual neurological baseline. Consider twins, Jane and Mary. Even though genetically identical, their life experiences and neurological responses might differ. What's a normal neural pattern for Jane might be an anomaly for Mary, and vice versa. The calibration protocol ensures that the system adjusts to these individual nuances, making the diagnostic process more personalized and accurate.
[00055] A notable improvement over previous BCIs is the introduction of flexible materials in the EEG sensor unit. Traditional rigid sensors, while effective, often led to discomfort during prolonged sessions. The flexible materials in this system, on the other hand, ensure better conformability to the human scalp. This not only enhances the quality of the neural signal captured but also significantly boosts user comfort.
[00056] One challenge that has perennially plagued EEG systems is environmental noise contamination. The electrical signals from the surroundings, even from devices like cell phones or monitors, can interfere with the brain signals being recorded. To counter this, the BCI system introduces an adaptive noise-cancellation mechanism. This mechanism employs ambient sound sensors that continuously monitor environmental interferences. The system then filters out these contaminations, ensuring the purity of the recorded neural signals. In a practical scenario, imagine a patient undergoing an EEG session in a busy hospital with numerous electronic devices around. The adaptive noise-cancellation ensures that despite the bustling surroundings, the patient's brain signals are captured without any external interference.
[00057] Referring to one or more preceding embodiments, the non-invasive brain-computer interface system 100 signifies a leap in neurological diagnostics. By amalgamating advanced sensor technology with cutting-edge machine learning, it promises unparalleled accuracy. Whether it's the comfort of flexible sensors or the security of encrypted data storage, every facet of this system underscores a commitment to enhancing patient experience and diagnostic precision.
[00058] The realm of neuroscience has witnessed significant advancements in recent years, particularly in the development of tools and methodologies designed to understand and diagnose neurological disorders. A method 200, leveraging a non-invasive brain-computer interface, is revolutionizing the way practitioners diagnose neurological anomalies. Let's embark on a comprehensive exploration of this diagnostic procedure, highlighting its strengths, mechanics, and key differentiators from previous methodologies.
[00059] Figuratively depicted in FIG. 2, representing a flow diagram of the method 200 for diagnosing neurological disorders using a non-invasive brain-computer interface, comprising steps of (at step 202) placing a multi-array EEG sensor unit on a user's scalp, (at step 204) recording neural signals with enhanced spatial resolution, (at step 206) processing these signals using a machine learning algorithm to identify pathological patterns, (at step 208) providing real-time feedback to the user regarding any detected anomalies and (at step 210) adjusting the system calibration based on individual neurological baselines.
[00060] The method's inaugural step involves the placement of a multi-array EEG sensor unit on a user's scalp. EEG, short for electroencephalography, is a technique that measures the electrical activity of the brain. Unlike traditional EEG systems, which feature a limited number of sensors, this multi-array unit comprises a dense cluster of electrodes. This ensures the capture of a broad spectrum of neural signals, even those emanating from deeper and more secluded regions of the brain. For instance, a pianist playing a complicated piece on a grand piano. Each key pressed generates a unique note, contributing to the overall melody. Similarly, various regions of our brain are continually producing unique electrical signals. The multi-array configuration of the sensor unit ensures that no 'note' or neural signal is missed, capturing the complete symphony of the brain's activity.
[00061] With the sensors in place, the method proceeds to record the brain's electrical signals. The multi-array setup offers an added advantage – enhanced spatial resolution. This means the system can precisely pinpoint the origin of specific signals within the brain. In essence, not only can it 'hear' the brain's melody but also discern which 'key' or region of the brain produced each note. Take, for instance, a situation where a user's temporal lobe generates an irregular signal pattern. The enhanced spatial resolution allows practitioners to ascertain with certainty that the anomaly arose from the temporal region, aiding in targeted diagnostic and therapeutic endeavors.
[00062] Once captured, the raw neural signals undergo processing using a sophisticated machine learning algorithm. The primary objective here is to distinguish between healthy and pathological neural patterns. Given the complexity and variability of brain signals, manual analysis can be time-consuming and prone to errors. This is where machine learning comes into play, automating the process and enhancing diagnostic accuracy. Consider a vast library containing millions of books (neural patterns). Identifying a single misprinted book manually would be akin to finding a needle in a haystack. However, the machine learning algorithm, trained on vast datasets of both healthy and pathological neural patterns, can swiftly identify anomalies, making the task infinitely more manageable.
[00063] One of the method's defining features is its ability to provide instantaneous feedback to users. Upon identifying a neural anomaly indicative of a potential neurological disorder, the system promptly alerts the user. This immediacy is pivotal, especially when monitoring conditions like epilepsy, where timely intervention can prevent an imminent seizure. For example, envision a user undergoing a monitoring session. The system detects an irregular neural pattern, suggestive of an impending epileptic episode. The immediate alert allows the user or caregivers to take preventative measures, possibly averting a full-blown seizure.
[00064] The method understands that each brain is unique. As such, before diving into the diagnostic process, the system calibrates itself based on the user's individual neurological baseline. This personalized approach ensures that the method can discern between what's a 'normal' neural pattern for one user versus an 'anomalous' pattern for another. Consider twins, Alice and Bob. Although genetically similar, their life experiences, memories, and neural patterns might be distinct. The system's calibration ensures it comprehensively understands each user's neural 'melody' before attempting to spot any 'off-notes'.
[00065] Traditional EEG sensors, while functional, often posed comfort issues, especially during extended monitoring sessions. Recognizing this, the method incorporates flexible EEG sensors. These pliant sensors conform better to the scalp's contours, ensuring optimal signal capture without compromising user comfort. Imagine wearing a rigid hat for several hours versus a soft, flexible one. The latter, undoubtedly, would be more comfortable. Similarly, the flexible EEG sensors ensure users remain at ease throughout the diagnostic process.
[00066] The method doesn't restrict itself to a singular machine learning algorithm. Recognizing the intricate nature of neural signals, which contain both spatial and temporal information, the method employs a combination of Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN). While CNNs are adept at identifying spatial features in the EEG data, RNNs excel in capturing temporal or sequential patterns. This dual approach ensures a holistic analysis of the captured signals, leaving no stone unturned in the quest for anomalies.
[00067] A persistent challenge with EEG recordings is the interference from environmental noises. Electromagnetic signals from nearby electronic devices or even electrical installations can contaminate the neural recordings. To combat this, the method integrates an adaptive noise-cancellation mechanism. By leveraging ambient sound sensors, this feature detects and filters out environmental interferences, ensuring the neural recordings remain pristine. Think of this as trying to listen to a soft melody in a noisy room. The adaptive noise-cancellation acts as an 'audio filter,' muting the room's cacophony, allowing the melody to be heard clearly.
[00068] Ensuring the user is promptly alerted of any detected anomalies is crucial. But what if the user has auditory or visual impairments? To circumvent such challenges, the method incorporates a haptic alert system. Upon identifying a potential neurological disorder pattern, the system triggers tactile notifications, like vibrations, ensuring the user is promptly informed regardless of auditory or visual challenges.
[00069] Referring to one or more preceding embodiments, the method 200 for diagnosing neurological disorders using a non-invasive brain-computer interface is a testament to the fusion of neuroscience and technology. By marrying advanced EEG sensor technology with machine learning, it offers an accurate, efficient, and user-friendly approach to neurological diagnostics. Whether it's the adaptability of the flexible sensors, the precision of the machine learning algorithms, or the inclusivity of the haptic alerts, every facet of this method underscores its potential to reshape the future of neurological diagnostics.
[00070] Example embodiments herein have been described above with reference to block diagrams and flowchart illustrations of methods and apparatuses. It will be understood that each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, can be implemented by various means including hardware, software, firmware, and a combination thereof. For example, in one embodiment, each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations can be implemented by computer program instructions. These computer program instructions may be loaded onto a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions which execute on the computer or other programmable data processing apparatus create means for implementing the functions specified in the flowchart block or blocks.
[00071] Throughout the present disclosure, the term ‘Artificial intelligence (AI)’ as used herein relates to any mechanism or computationally intelligent system that combines knowledge, techniques, and methodologies for controlling a bot or other element within a computing environment. Furthermore, the artificial intelligence (AI) is configured to apply knowledge and that can adapt it-self and learn to do better in changing environments. Additionally, employing any computationally intelligent technique, the artificial intelligence (AI) is operable to adapt to unknown or changing environment for better performance. The artificial intelligence (AI) includes fuzzy logic engines, decision-making engines, preset targeting accuracy levels, and/or programmatically intelligent software.
[00072] Throughout the present disclosure, the term ‘processing means’ or ‘microprocessor’ or ‘processor’ or ‘processors’ includes, but is not limited to, a general purpose processor (such as, for example, a complex instruction set computing (CISC) microprocessor, a reduced instruction set computing (RISC) microprocessor, a very long instruction word (VLIW) microprocessor, a microprocessor implementing other types of instruction sets, or a microprocessor implementing a combination of types of instruction sets) or a specialized processor (such as, for example, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a digital signal processor (DSP), or a network processor).
[00073] The term “non-transitory storage device” or “storage” or “memory,” as used herein relates to a random access memory, read only memory and variants thereof, in which a computer can store data or software for any duration.
[00074] Operations in accordance with a variety of aspects of the disclosure is described above would not have to be performed in the precise order described. Rather, various steps can be handled in reverse order or simultaneously or not at all.
[00075] While several implementations have been described and illustrated herein, a variety of other means and/or structures for performing the function and/or obtaining the results and/or one or more of the advantages described herein may be utilized, and each of such variations and/or modifications is deemed to be within the scope of the implementations described herein. More generally, all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teachings is/are used. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific implementations described herein. It is, therefore, to be understood that the foregoing implementations are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, implementations may be practiced otherwise than as specifically described and claimed. Implementations of the present disclosure are directed to each individual feature, system, article, material, kit, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, kits, and/or methods, if such features, systems, articles, materials, kits, and/or methods are not mutually inconsistent, is included within the scope of the present disclosure.

Claims
I/We Claim:
1. A non-invasive brain-computer interface system for diagnosing neurological disorders, comprising:
a multi-array EEG sensor unit configured to detect and record neural signals with enhanced spatial resolution;
a machine learning-based signal processing module designed to distinguish pathological neural patterns from healthy ones;
a real-time feedback mechanism to inform the user of detected anomalies, addressing the delayed diagnostic feedback issues in previous systems;
a cloud-based data storage with encrypted security for remote expert consultations; and
an integrated calibration protocol that adjusts to individual neurological baselines, addressing variability in prior systems.
2. The system of claim 1, wherein the EEG sensor unit incorporates flexible materials for better conformability and comfort, addressing discomfort issues observed with rigid sensors in prior art.
3. The system of claim 1, wherein the machine learning-based signal processing module uses a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) for enhanced temporal and spatial pattern recognition.
4. The system of claim 1, further comprising an adaptive noise-cancellation mechanism that utilizes ambient sound sensors to filter environmental interferences, addressing the noise contamination challenge of earlier designs.
5. The system of claim 1, wherein the real-time feedback mechanism includes a haptic alert system, enabling notifications even when auditory or visual feedback may be impaired.
6. A method for diagnosing neurological disorders using a non-invasive brain-computer interface, comprising:
placing a multi-array EEG sensor unit on a user's scalp;
recording neural signals with enhanced spatial resolution;
processing these signals using a machine learning algorithm to identify pathological patterns;
providing real-time feedback to the user regarding any detected anomalies; and
adjusting the system calibration based on individual neurological baselines.
7. The method of claim 6, further including:
employing a flexible EEG sensor to ensure user comfort during the diagnostic process, addressing physical discomfort issues from prior methods.
8. The method of claim 6, wherein the neural signal processing step involves:
utilizing a combination of CNN and RNN algorithms to analyze both spatial and temporal patterns in the neural data, offering a comprehensive diagnostic perspective.
9. The method of claim 6, further comprising:
employing an adaptive noise-cancellation mechanism that filters out environmental noises using ambient sound sensors, ensuring cleaner neural data recordings.
10. The method of claim 6, wherein upon detection of a potential neurological disorder pattern, the method involves:
triggering a haptic alert to the user, ensuring notification accessibility even in scenarios with visual or auditory challenges.

Non-Invasive Brain-Computer Interfaces for Diagnosis of Neurological Disorders
Abstract
Introducing a groundbreaking non-invasive brain-computer interface system, poised to revolutionize the diagnostic landscape of neurological disorders. At its forefront is a multi-array EEG sensor unit, masterfully calibrated to discern and chronicle neural signals, boasting unparalleled spatial fidelity. Seamlessly interfaced with this is a machine learning-imbued signal processing module, meticulously engineered to delineate pathological neural manifestations from normative patterns. Addressing historical diagnostic lags, an immediate feedback apparatus is integrated, apprising users of detected neural aberrations in real-time. Elevating its diagnostic prowess, a cloud-anchored data repository, fortified with state-of-the-art encryption, facilitates remote consultations with neurological experts. A standout feature is its bespoke calibration protocol, adeptly tailoring the system to individual neurological baselines, rectifying the variability constraints of antecedent systems. Collectively, this interface signifies a paradigm shift in neuro-diagnosis, melding precision with immediacy and individualization. , Claims:Claims
I/We Claim:
1. A non-invasive brain-computer interface system for diagnosing neurological disorders, comprising:
a multi-array EEG sensor unit configured to detect and record neural signals with enhanced spatial resolution;
a machine learning-based signal processing module designed to distinguish pathological neural patterns from healthy ones;
a real-time feedback mechanism to inform the user of detected anomalies, addressing the delayed diagnostic feedback issues in previous systems;
a cloud-based data storage with encrypted security for remote expert consultations; and
an integrated calibration protocol that adjusts to individual neurological baselines, addressing variability in prior systems.
2. The system of claim 1, wherein the EEG sensor unit incorporates flexible materials for better conformability and comfort, addressing discomfort issues observed with rigid sensors in prior art.
3. The system of claim 1, wherein the machine learning-based signal processing module uses a combination of convolutional neural networks (CNN) and recurrent neural networks (RNN) for enhanced temporal and spatial pattern recognition.
4. The system of claim 1, further comprising an adaptive noise-cancellation mechanism that utilizes ambient sound sensors to filter environmental interferences, addressing the noise contamination challenge of earlier designs.
5. The system of claim 1, wherein the real-time feedback mechanism includes a haptic alert system, enabling notifications even when auditory or visual feedback may be impaired.
6. A method for diagnosing neurological disorders using a non-invasive brain-computer interface, comprising:
placing a multi-array EEG sensor unit on a user's scalp;
recording neural signals with enhanced spatial resolution;
processing these signals using a machine learning algorithm to identify pathological patterns;
providing real-time feedback to the user regarding any detected anomalies; and
adjusting the system calibration based on individual neurological baselines.
7. The method of claim 6, further including:
employing a flexible EEG sensor to ensure user comfort during the diagnostic process, addressing physical discomfort issues from prior methods.
8. The method of claim 6, wherein the neural signal processing step involves:
utilizing a combination of CNN and RNN algorithms to analyze both spatial and temporal patterns in the neural data, offering a comprehensive diagnostic perspective.
9. The method of claim 6, further comprising:
employing an adaptive noise-cancellation mechanism that filters out environmental noises using ambient sound sensors, ensuring cleaner neural data recordings.
10. The method of claim 6, wherein upon detection of a potential neurological disorder pattern, the method involves:
triggering a haptic alert to the user, ensuring notification accessibility even in scenarios with visual or auditory challenges.