1. A system for electroencephalogram (EEG)-based brain-computer interfacing, comprising: a neuro capture module designed to obtain and preprocess EEG data from a user; a deep mind net processor configured to apply deep learning algorithms on the pre-processed EEG data; an interface bridge module for translating the processed data into actionable commands; a feedback loop component designed to adaptively refine the deep learning algorithms based on user feedback; and a synapse storage unit to store user-specific models and patterns derived from the EEG data.

2. The system of claim 1, wherein the neuro capture module comprises a multi-electrode array with adaptive impedance matching for optimal EEG data capture.

3. The system of claim 1, wherein the deep mind net processor employs a convolutional neural network for feature extraction from the EEG data.

4. The system of claim 1, further comprising a user calibration interface allowing for the calibration of the system based on specific user needs and preferences.

5. The system of claim 1, wherein the feedback loop component uses real-time monitoring to dynamically adjust the sensitivity and specificity of the deep mind net processor.

6. A method for EEG-based brain-computer interfacing, comprising the steps of: capturing EEG data using a neuro capture module; preprocessing the captured EEG data; applying deep learning algorithms on the pre-processed data using a deep mind net processor; translating the processed data into actionable commands via an interface bridge module; and storing user-specific models and patterns using a synapse storage unit.

7. The method of claim 6, further comprising the step of adaptively refining the deep learning algorithms based on user feedback using a feedback loop component.

8. The method of claim 6, wherein the step of applying deep learning algorithms involves the use of convolutional neural networks for feature extraction.

9. The method of claim 6, further comprising a step of calibrating the system based on user-specific needs and preferences via a user calibration interface.

10. The method of claim 6, wherein the step of translating processed data into actionable commands uses a probabilistic mapping mechanism to ensure the highest probability of accurate command execution. Deep Learning Approaches for EEG Based BrainComputer Interfaces Abstract The present invention discloses a system for EEG-based brain-computer interfacing, comprising a neuro capture module for obtaining and preprocessing EEG data, a deep mind net processor for applying deep learning algorithms on the preprocessed EEG data, an interface bridge module translating processed data into actionable commands, a feedback loop component refining deep learning algorithms based on user feedback, and a synapse storage unit to store user-specific models and EEG-derived patterns. This system offers an integrated solution for real-time interaction by capturing, processing, and interpreting EEG signals, enabling efficient and personalized brain-computer communication with the ability to adapt and refine algorithms based on user input. , Claims:Claims :

1. A system for electroencephalogram (EEG)-based brain-computer interfacing, comprising: a neuro capture module designed to obtain and preprocess EEG data from a user; a deep mind net processor configured to apply deep learning algorithms on the pre-processed EEG data; an interface bridge module for translating the processed data into actionable commands; a feedback loop component designed to adaptively refine the deep learning algorithms based on user feedback; and a synapse storage unit to store user-specific models and patterns derived from the EEG data.

2. The system of claim 1, wherein the neuro capture module comprises a multi-electrode array with adaptive impedance matching for optimal EEG data capture.

3. The system of claim 1, wherein the deep mind net processor employs a convolutional neural network for feature extraction from the EEG data.

4. The system of claim 1, further comprising a user calibration interface allowing for the calibration of the system based on specific user needs and preferences.

5. The system of claim 1, wherein the feedback loop component uses real-time monitoring to dynamically adjust the sensitivity and specificity of the deep mind net processor.

6. A method for EEG-based brain-computer interfacing, comprising the steps of: capturing EEG data using a neuro capture module; preprocessing the captured EEG data; applying deep learning algorithms on the pre-processed data using a deep mind net processor; translating the processed data into actionable commands via an interface bridge module; and storing user-specific models and patterns using a synapse storage unit.

7. The method of claim 6, further comprising the step of adaptively refining the deep learning algorithms based on user feedback using a feedback loop component.

8. The method of claim 6, wherein the step of applying deep learning algorithms involves the use of convolutional neural networks for feature extraction.

9. The method of claim 6, further comprising a step of calibrating the system based on user-specific needs and preferences via a user calibration interface.

10. The method of claim 6, wherein the step of translating processed data into actionable commands uses a probabilistic mapping mechanism to ensure the highest probability of accurate command execution.

Description:Deep Learning Approaches for EEG Based BrainComputer Interfaces
Field of the Invention
[0001] The present invention relates generally to the realm of brain-computer interfaces (BCIs). More specifically, the invention pertains to advanced methods and systems that employ deep learning techniques for processing, analyzing, and interpreting electroencephalogram (EEG) data. The primary focus of this invention is to enhance the accuracy, speed, and robustness of BCIs by leveraging the intricate architectures and adaptive capabilities of deep learning models, thereby facilitating more efficient communication between the human brain and external devices or systems.
[0001]
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] Brain-Computer Interfaces (BCIs) have gained significant attention as a cutting-edge technology that facilitates direct communication between the human brain and external devices. Electroencephalography (EEG) is a non-invasive method commonly employed in BCIs to record and analyze the electrical activity of the brain. However, extracting meaningful information from EEG signals is challenging due to their noisy and complex nature. Deep Learning (DL) techniques have emerged as powerful tools for extracting relevant features and patterns from EEG signals, enabling more robust and accurate BCIs.
[0004] CNNs, originally designed for image analysis, have been adapted to EEG data for feature extraction. By treating EEG data as multi-channel images, CNNs can learn spatial-temporal patterns that correspond to different brain activities. For instance, in a study by Schirrmeister et al. (2017), a deep CNN architecture was proposed to decode motor imagery tasks from EEG data. The network learned spatial filters that enhanced discriminative information, improving classification accuracy.
[0005] EEG signals exhibit temporal dependencies crucial for understanding cognitive processes. RNNs and its variants, such as Long Short-Term Memory (LSTM) networks, capture these temporal dynamics. An example is the work by Bashivan et al. (2015), where LSTM-based models were employed to decode cognitive states from EEG signals during naturalistic tasks like watching movies.
[0006] GANs have been utilized to address the problem of limited EEG training data. These networks generate synthetic EEG samples that resemble real EEG data, which can be combined with the original data for training. A study by Esteban et al. (2017) employed a GAN-based approach to augment EEG datasets for improving the performance of emotion recognition BCIs.
[0007] EEG signals exhibit subject-specific variability, making it challenging to generalize models across different individuals. Transfer learning mitigates this issue by transferring knowledge from a source subject to a target subject. In a study by Lotte et al. (2019), transfer learning was used to improve the performance of motor imagery BCIs across multiple subjects by leveraging shared spatial patterns.
[0008] Attention mechanisms enhance the interpretability of deep learning models by highlighting relevant temporal or spatial regions in EEG data. In a study by Zhang et al. (2020), an attention-based model was proposed to decode motor imagery tasks. The attention mechanism highlighted brain regions crucial for the classification task, providing insights into neural activation patterns.
[0009] Deploying EEG-based BCIs in real-world scenarios introduces additional challenges due to varying recording conditions and noise sources. Domain adaptation techniques enable models trained in controlled settings to adapt to more challenging environments. A study by Kwon et al. (2021) explored domain adaptation methods to improve the robustness of EEG-based BCIs for practical applications.
[00010] In conclusion, deep learning approaches have demonstrated their efficacy in enhancing EEG-based BCIs by enabling accurate feature extraction, temporal dynamics modeling, data augmentation, cross-subject adaptation, interpretability, and domain adaptation. These prior art examples collectively showcase the diverse applications and contributions of deep learning techniques in advancing the field of brain-computer interfaces, bridging the gap between neuroscience and technology.
[00011]
[00012] 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.
[00013] 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
[00014] 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.
[00015] The following paragraphs provide additional support for the claims of the subject application.
[00016] The present invention relates generally to the realm of brain-computer interfaces (BCIs). More specifically, the invention pertains to advanced methods and systems that employ deep learning techniques for processing, analyzing, and interpreting electroencephalogram (EEG) data. The primary focus of this invention is to enhance the accuracy, speed, and robustness of BCIs by leveraging the intricate architectures and adaptive capabilities of deep learning models, thereby facilitating more efficient communication between the human brain and external devices or systems.
[00017]
[00018] The revealed system is a groundbreaking advancement in Electroencephalogram (EEG)-based brain-computer interfacing, paving the way for a seamless connection between the human brain and technology. This innovative system orchestrates a symphony of cutting-edge components that collectively enable intuitive and adaptive brain-computer interactions.
[00019] At its heart, the system's efficacy is embodied in its dynamic framework. The neuro capture module initiates the journey, purposefully designed to procure and preprocess EEG data from the user. This fundamental step sets the stage for the subsequent stages, where raw neural signals are meticulously transformed into actionable insights.
[00020] The deep mind net processor, a technological marvel, takes the spotlight. This processor operates as a neural powerhouse, applying deep learning algorithms to the pre-processed EEG data. Employing advanced techniques like convolutional neural networks, this processor unearths intricate patterns and features within the EEG data that are essential for understanding user intent.
[00021] The interface bridge module emerges as a critical bridge between neural insights and technological actions. This module deftly translates the processed EEG data into actionable commands, enabling users to control devices, applications, and environments using their neural signals.
[00022] One of the system's remarkable facets is its adaptability. The feedback loop component showcases this adaptability by continually refining the deep learning algorithms based on user feedback. This real-time refinement process ensures that the system attunes itself to the user's unique neural patterns and intentions, enhancing accuracy and user experience over time.
[00023] Further enriching the user experience, the system introduces a synapse storage unit. This unit serves as a repository for user-specific models and patterns derived from the EEG data. By capturing and storing these patterns, the system becomes personalized, reflecting each user's neural signature and preferences.
[00024] The system's excellence is fortified through the incorporation of specific features. The neuro capture module is fortified with adaptive impedance matching, optimizing EEG data capture through a multi-electrode array. The deep mind net processor's utilization of convolutional neural networks amplifies its feature extraction capabilities, unearthing the essence of neural signals.
[00025] Intricacy is met with practicality, as the system includes a user calibration interface. This interface allows users to calibrate the system according to their specific needs and preferences, further enhancing the system's personalization.
[00026] The feedback loop component's real-time monitoring brings a dynamic touch, allowing the system to fine-tune the sensitivity and specificity of the deep mind net processor. This dynamic calibration ensures that the system attains an optimal balance between precision and adaptability.
[00027] In essence, the system for EEG-based brain-computer interfacing redefines the boundaries of human-technology interaction. By seamlessly integrating data capture, deep learning, translation, adaptive refinement, and personalized storage, it establishes a comprehensive framework that fuses the complexities of neural signals with technological potential. This system not only bridges the gap between mind and machine but also heralds an era of user-centric brain-computer interfaces that adapt and evolve in tandem with users' neural journeys.
[00028] The method unveiled pioneers an innovative realm of brain-computer interfacing through Electroencephalogram (EEG) technology, forging a seamless connection between neural activity and technological actions. With meticulous precision, this method outlines a sequence of steps that together form a sophisticated symphony of human cognition and machine interaction.
[00029] The journey commences with the capture of EEG data, orchestrated by a neuro capture module. This module serves as the gateway to neural insights, capturing the intricate electrical signals emanating from the brain. The subsequent preprocessing of this data is an essential refinement step that prepares the neural signals for the ensuing stages.
[00030] The crux of the method lies in the deep mind net processor, a technological marvel that employs deep learning algorithms. This processor is akin to a neural interpreter, extracting complex patterns and features from the preprocessed EEG data. It employs advanced techniques like convolutional neural networks, unveiling the subtleties of neural signals with unmatched precision.
[00031] The interface bridge module emerges as a translator between neural insights and actionable commands. This pivotal module deftly transforms the processed EEG data into tangible actions, allowing users to exert control over devices and environments using their neural signals.
[00032] Central to the method is the storage of user-specific models and patterns, meticulously captured through the synapse storage unit. This repository serves as a testament to the system's adaptability, personalizing the experience by capturing and storing each user's unique neural signature and preferences.
[00033] Furthering the method's innovation, adaptability is enhanced through the feedback loop component. This component dynamically refines the deep learning algorithms based on user feedback, ensuring that the system evolves in tandem with user intentions and preferences. This adaptive calibration process optimizes the system's accuracy and user experience.
[00034] The method's sophistication is fortified by its utilization of convolutional neural networks for feature extraction, enabling the deep mind net processor to decode neural nuances with remarkable acuity. Additionally, a user calibration interface provides a practical touch, allowing users to tailor the system to their specific needs and preferences.
[00035] The method's bridge between neural insights and actionable commands is characterized by a probabilistic mapping mechanism. This mechanism ensures that the translation of processed data into commands is guided by probabilities, maximizing the accuracy of command execution.
[00036] In essence, the method for EEG-based brain-computer interfacing epitomizes the fusion of human cognition and technological prowess. By meticulously navigating EEG data capture, preprocessing, deep learning, translation, and personalized storage, it crafts a comprehensive framework that transcends the boundaries of thought and action. This method not only empowers users with control but also heralds an era of personalized, adaptable brain-computer interfaces that bridge the gap between mind and machine.
[00037]
Brief Description of the Drawings
[00038] 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:
[00039] FIG. 1 represents an architectural overview of a system for electroencephalogram (EEG)-based brain-computer interfacing, according to some embodiments of the present disclosure.

[00040] FIG. 2 shows an exemplary detailed schematic flow diagram of a method for electroencephalogram (EEG)-based brain-computer interfacing, according to some embodiments of the present disclosure.
[00041]
Detailed Description
[00042] 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.
[00043] 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.
[00044] 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.
[00045] The present invention relates generally to the realm of brain-computer interfaces (BCIs). More specifically, the invention pertains to advanced methods and systems that employ deep learning techniques for processing, analyzing, and interpreting electroencephalogram (EEG) data. The primary focus of this invention is to enhance the accuracy, speed, and robustness of BCIs by leveraging the intricate architectures and adaptive capabilities of deep learning models, thereby facilitating more efficient communication between the human brain and external devices or systems.
[00046]
[00047] 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.
[00048] Brain-Computer Interfaces (BCIs), which enable direct communication between the brain and external devices, have attracted tremendous attention in the scientific and tech communities over the past decades. BCIs have the potential to revolutionize numerous applications, from medical interventions for paralyzed patients to novel methods of human-computer interaction. The system 100 described here employs Electroencephalogram (EEG) as the primary method for capturing brain signals and employs a sophisticated processing architecture to provide efficient and user-adaptive interfacing capabilities.
[00049] According to a pictorial portrayal in FIG. 1, illustrating an architectural setup of the system 100 for electroencephalogram (EEG)-based brain-computer interfacing, comprising a neuro capture module 102 designed to obtain and preprocess EEG data from a user, a deep mind net processor 104 configured to apply deep learning algorithms on the pre-processed EEG data, an interface bridge module 106 for translating the processed data into actionable commands, a feedback loop component 108 designed to adaptively refine the deep learning algorithms based on user feedback, and a synapse storage unit 110 to store user-specific models and patterns derived from the EEG data.
[00050] In an exemplary embodiment, the foundation of this system is the neuro capture module. The primary role of this module is to obtain EEG data, which represents electrical activity in the brain. This data is captured via electrodes placed on the scalp. For example, consider a person wearing a cap studded with numerous sensors. Each sensor detects voltage fluctuations caused by the firing of groups of neurons in the brain. These signals are usually of very low amplitude, and hence, need preprocessing to remove noise and improve the signal quality.
[00051] To enhance the EEG data capture, the system integrates a multi-electrode array with adaptive impedance matching. This feature ensures optimal capture by dynamically adjusting the resistance between the electrodes and the scalp, accounting for varying scalp conditions, hair density, and potential external disturbances. For example, consider a user has just taken a shower; the wetness of the hair might affect the electrode's contact with the scalp. With adaptive impedance matching, the system automatically adjusts to this condition, ensuring a consistent quality of data capture.
[00052] Once the EEG data is captured and pre-processed, it's forwarded to the deep mind net processor. This processor is configured to apply deep learning algorithms on the EEG data, extracting meaningful patterns and making sense out of the otherwise abstract signals. One of the primary algorithms used by the deep mind net processor is the Convolutional Neural Network (CNN). CNNs are particularly well-suited for feature extraction from data with spatial structures, like images or time-series data such as EEG. Consider the EEG data as a series of waves over time. A CNN can efficiently detect certain wave patterns or oscillations that might correspond to specific thoughts or intentions of the user. It could recognize, for instance, patterns that correspond to the intention of moving a limb or blinking an eye.
[00053] Once the deep mind net processor has interpreted the EEG data, the interface bridge module takes over. It translates the processed data into actionable commands that can be understood by external devices or software interfaces. For example, a user thinks about moving a cursor to the right. The EEG data associated with this thought is captured, processed, and then translated by the bridge module into a command that a computer understands, causing the cursor to move right on the screen.
[00054] A crucial aspect of any dynamic system 100 is its ability to learn and improve over time. The feedback loop component serves this purpose. It adaptively refines the deep learning algorithms based on user feedback. By monitoring the system's output and receiving feedback—either implicitly from observing actions or explicitly from user inputs—the algorithms can be fine-tuned. This is especially important for BCIs, where individual variability is high, and one-size-fits-all solutions are rarely effective. For instance, the feedback loop component uses real-time monitoring to adjust the sensitivity and specificity of the deep mind net processor, ensuring optimal performance for individual users. If a user finds that the cursor often moves to the right when they intended it to go left, they can provide this feedback. The system then adjusts its internal algorithms, reducing the likelihood of such mistakes in the future.
[00055] The synapse storage unit is a dedicated storage system for user-specific models and patterns derived from the EEG data. Given the high individual variability in EEG patterns, personalized models are essential for optimal performance. Over time, as the BCI system learns more about a particular user's brain patterns, it might develop a 'profile' for that user, which is stored in the synapse storage unit. When the user next uses the system, it can retrieve this profile, allowing for faster and more accurate interfacing.
[00056] Beyond the main components, the system also includes a user calibration interface. This interface allows users to calibrate the system based on their specific needs and preferences. Some users might have specific neural conditions or unique EEG patterns. The calibration interface could include a series of tasks or exercises that help the system understand and adapt to these individual characteristics.
[00057] Referring to one or more preceding embodiments, the described EEG-based BCI system 100 is a holistic combination of cutting-edge technologies and user-centric designs. By leveraging advanced algorithms, adaptive feedback mechanisms, and personalized storage, it offers an efficient and customizable brain-computer interaction experience. As the field advances, systems like these pave the way for more seamless integration of our minds with technology.
[00058] Brain-Computer Interfaces (BCIs) stand at the intersection of neuroscience, artificial intelligence, and human-computer interaction. The potential of BCIs to translate thoughts into actionable commands has sparked significant interest in the realm of medical science, gaming, and beyond. This guide dives deep into a method 200 for EEG-based BCI, illustrating its step-by-step process, components, and nuances.
[00059] Figuratively depicted in FIG. 2, representing a flow diagram of the method 200, comprising the steps of (at step 202) capturing EEG data using a neuro capture module, (at step 204) preprocessing the captured EEG data, (at step 206) applying deep learning algorithms on the pre-processed data using a deep mind net processor, (at step 208) translating the processed data into actionable commands via an interface bridge module, and (at step 210) storing user-specific models and patterns using a synapse storage unit.
[00060] Electroencephalography (EEG) is a non-invasive method 200 to measure the electrical activity of the brain. By placing electrodes on the scalp, one can detect voltage fluctuations resulting from the synchronized activity of thousands of neurons. The neuro capture module is essential in this step. Equipped with a set of electrodes, it is responsible for capturing raw EEG signals that represent the user's brain activity. Imagine someone wearing a sensor-embedded cap. As they think about lifting their arm, specific brain regions show activity. The electrodes detect these minute electrical changes, providing raw EEG data that gives insights into the ongoing brain processes.
[00061] Raw EEG data is often contaminated with noise and artifacts. This noise can arise from various sources such as muscular activity, eye movements, or external electrical interference. Preprocessing aims to clean this data, enhancing the signal-to-noise ratio. Typical preprocessing steps include filtering that involves eliminating unwanted frequency components, artifact removal that involves identifying and removing abnormal patterns, and segmentation which includes breaking down continuous EEG data into meaningful epochs. Consider an EEG trace disturbed by eye blinks, producing sharp spikes in the data. Through preprocessing, these spikes are identified and either removed or corrected, resulting in a smoother and cleaner EEG trace.
[00062] Deep learning is a subset of machine learning inspired by the structure of the human brain, specifically neural networks. The deep mind net processor uses deep learning algorithms to identify patterns and features within the pre-processed EEG data, turning abstract signals into interpretable information. One of the core algorithms employed in this step is the Convolutional Neural Network.
[00063] Designed initially for image processing, CNNs have found utility in EEG data analysis due to their prowess in handling spatial structures. They excel in extracting hierarchical features, making them apt for detecting intricate patterns within EEG signals. Imagine the EEG data showing a repetitive wave-like pattern every time a user thinks about moving forward. Using CNNs, the system can recognize this particular wave pattern, associating it with the "move forward" command.
[00064] The extracted information from the EEG data, although meaningful, is not directly usable by external devices or software. The interface bridge module serves the vital role of translating this information into actionable commands that a computer or machine can execute. To ensure accuracy, this translation isn't deterministic but probabilistic. By assessing the highest likelihood of a command's intent, the system minimizes errors, ensuring a seamless user experience. From the processed data, the system might determine a 90% probability that the user intended to "turn left" and only a 10% probability of "turn right." Instead of making a binary decision, the system, leveraging the probabilistic mapping, confidently chooses the "turn left" command.
[00065] BCI systems benefit immensely from personalization. As each individual's brain patterns can differ, it's valuable to store user-specific data models. The synapse storage unit is a dedicated space to save these personalized models and patterns. Over time, as the system learns more about a user's specific brain patterns, it can deliver more precise and tailored responses. Two users might have slightly different EEG patterns when thinking about the same action, say, "jump." By storing individual profiles, the BCI system can remember and recognize these nuances, ensuring accurate command translation for both users during subsequent sessions.
[00066] One of the most dynamic aspects of this BCI method is its capacity for self-improvement. The feedback loop component continually refines the system's performance based on user feedback. Whether a command is executed correctly or an error occurs, this feedback is invaluable for fine-tuning the algorithms, enhancing accuracy over time. Consider, a user notes that the system often misinterprets their intention to "run" as "walk." By providing this feedback, the system adjusts its internal algorithms, making it less likely to repeat this mistake in future sessions.
[00067] Beyond generic feedback, users might have specific needs or preferences, necessitating the need for calibration. The user calibration interface allows for such adjustments, ensuring the system aligns perfectly with individual requirements. A user with a neurological condition might have unique EEG patterns. Through the calibration interface, they can undergo a series of exercises or tasks, aiding the system in understanding and adapting to these specific characteristics.
[00068] Referring to one or more preceding embodiments, the described method 200 for EEG-based BCI stands as a testament to the advancements in neurotechnology and artificial intelligence. Through a structured series of steps, it captures the essence of human intention, translates it into machine-readable commands, and continually refines itself for an optimized user experience. As research and development continue, such methodologies promise a future where our thoughts seamlessly intertwine with technology.
[00069] 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.
[00070] 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.
[00071] 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).
[00072] 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.
[00073] 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.
[00074] 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 system for electroencephalogram (EEG)-based brain-computer interfacing, comprising:
a neuro capture module designed to obtain and preprocess EEG data from a user;
a deep mind net processor configured to apply deep learning algorithms on the pre-processed EEG data;
an interface bridge module for translating the processed data into actionable commands;
a feedback loop component designed to adaptively refine the deep learning algorithms based on user feedback; and
a synapse storage unit to store user-specific models and patterns derived from the EEG data.
2. The system of claim 1, wherein the neuro capture module comprises a multi-electrode array with adaptive impedance matching for optimal EEG data capture.
3. The system of claim 1, wherein the deep mind net processor employs a convolutional neural network for feature extraction from the EEG data.
4. The system of claim 1, further comprising a user calibration interface allowing for the calibration of the system based on specific user needs and preferences.
5. The system of claim 1, wherein the feedback loop component uses real-time monitoring to dynamically adjust the sensitivity and specificity of the deep mind net processor.
6. A method for EEG-based brain-computer interfacing, comprising the steps of:
capturing EEG data using a neuro capture module;
preprocessing the captured EEG data;
applying deep learning algorithms on the pre-processed data using a deep mind net processor;
translating the processed data into actionable commands via an interface bridge module; and
storing user-specific models and patterns using a synapse storage unit.
7. The method of claim 6, further comprising the step of adaptively refining the deep learning algorithms based on user feedback using a feedback loop component.
8. The method of claim 6, wherein the step of applying deep learning algorithms involves the use of convolutional neural networks for feature extraction.
9. The method of claim 6, further comprising a step of calibrating the system based on user-specific needs and preferences via a user calibration interface.
10. The method of claim 6, wherein the step of translating processed data into actionable commands uses a probabilistic mapping mechanism to ensure the highest probability of accurate command execution.

Deep Learning Approaches for EEG Based BrainComputer Interfaces
Abstract
The present invention discloses a system for EEG-based brain-computer interfacing, comprising a neuro capture module for obtaining and preprocessing EEG data, a deep mind net processor for applying deep learning algorithms on the preprocessed EEG data, an interface bridge module translating processed data into actionable commands, a feedback loop component refining deep learning algorithms based on user feedback, and a synapse storage unit to store user-specific models and EEG-derived patterns. This system offers an integrated solution for real-time interaction by capturing, processing, and interpreting EEG signals, enabling efficient and personalized brain-computer communication with the ability to adapt and refine algorithms based on user input. , Claims:Claims
I/We Claim:
1. A system for electroencephalogram (EEG)-based brain-computer interfacing, comprising:
a neuro capture module designed to obtain and preprocess EEG data from a user;
a deep mind net processor configured to apply deep learning algorithms on the pre-processed EEG data;
an interface bridge module for translating the processed data into actionable commands;
a feedback loop component designed to adaptively refine the deep learning algorithms based on user feedback; and
a synapse storage unit to store user-specific models and patterns derived from the EEG data.
2. The system of claim 1, wherein the neuro capture module comprises a multi-electrode array with adaptive impedance matching for optimal EEG data capture.
3. The system of claim 1, wherein the deep mind net processor employs a convolutional neural network for feature extraction from the EEG data.
4. The system of claim 1, further comprising a user calibration interface allowing for the calibration of the system based on specific user needs and preferences.
5. The system of claim 1, wherein the feedback loop component uses real-time monitoring to dynamically adjust the sensitivity and specificity of the deep mind net processor.
6. A method for EEG-based brain-computer interfacing, comprising the steps of:
capturing EEG data using a neuro capture module;
preprocessing the captured EEG data;
applying deep learning algorithms on the pre-processed data using a deep mind net processor;
translating the processed data into actionable commands via an interface bridge module; and
storing user-specific models and patterns using a synapse storage unit.
7. The method of claim 6, further comprising the step of adaptively refining the deep learning algorithms based on user feedback using a feedback loop component.
8. The method of claim 6, wherein the step of applying deep learning algorithms involves the use of convolutional neural networks for feature extraction.
9. The method of claim 6, further comprising a step of calibrating the system based on user-specific needs and preferences via a user calibration interface.
10. The method of claim 6, wherein the step of translating processed data into actionable commands uses a probabilistic mapping mechanism to ensure the highest probability of accurate command execution.