Description:FIELD OF THE INVENTION

[0001] The present invention relates to a brain-computer interface system that is capable of improve the accuracy of brain signal processing by reducing interference from unwanted noise while keeping the important brain activity data intact and enable real-time response in brain-computer interface applications, thereby ensuring better accuracy in detecting and interpreting signals for different users.

BACKGROUND OF THE INVENTION

[0002] Brain-computer interface (BCI) systems enable direct communication between the brain and external devices by translating neural signals into commands. These systems have the potential to assist people with disabilities, improve communication, and enable new forms of human-computer interaction. Accurate and reliable capture and processing of brain signals are critical for the effective functioning of BCIs. Various technologies are used to acquire, filter, and decode neural signals in real time to achieve meaningful control over external devices.

[0003] Traditional brain signal acquisition often relies on electrodes placed on the scalp, with fixed placement protocols and standard signal amplification techniques. These methods may not fully capture detailed brain activity due to limitations in electrode resolution or placement accuracy. Noise from muscle movements, environmental interference, and poor electrode contact can degrade signal quality. Conventional filtering methods, such as fixed bandpass filters, are limited in their ability to remove complex noise without affecting important neural information.

[0004] Existing signal processing approaches often use basic noise reduction techniques and simple adaptive filters. However, these techniques struggle to remove all unwanted noise like muscle artifacts or electrode interference effectively. Additionally, most systems process signals remotely, causing delays that reduce responsiveness in applications requiring real-time control. The inability to dynamically adjust to individual brain activity patterns also limits signal clarity and decoding accuracy in traditional methods.

[0005] US10820816B2 discloses about the invention provides a two-step approach to providing a BCI system. In a first step the invention provides a low-power implantable platform for amplifying and filtering the extracellular recording, performing analogue to digital conversion (ADC) and detecting action potentials in real-time, which is connected to a remote device capable of performing the processor-intensive tasks of feature extraction and spike classification, thus generating a plurality of predetermined templates for each neuron to be used in a second processing step. In the second step the low-power implantable platform amplifies and filters the extracellular recording, performs ADC and detects action potentials, which can be matched on-chip to the predetermined templates generated by the external receiver in the first step. This two-step approach exploits the advantages of both offline and online processing, providing an effective and safe method for performing multiple recordings of single-neuron activity, for research or monitoring applications or for control of a remote device.

[0006] US12093456B2 discloses about an adaptive calibration method in a brain-computer interface is disclosed. The method is used to reliably associate a neural signal to an object whose attendance by a user elicited that neural signal. A visual stimulus overlaying one or more objects is provided, at least a portion of the visual stimulus having a characteristic modulation. The brain computer interface measures neural response to objects viewed by a user. The neural response to the visual stimulus is correlated to the modulation, the correlation being stronger when attention is concentrated upon the visual stimulus. Weights are applied to the resulting model of neural responses for the user based on the determined correlations. Both neural signal model weighting and displayed object display modulation are adapted so as to improve the certainty of the association of neural signals with the objects that evoked those signals.

[0007] Conventionally, many systems are available for processing neuron signals. However the cited invention shows certain limitation, these system lack dynamic real-time adaptability to individual neural patterns, suffer from noise interference, and depend heavily on remote processing, leading to delays. Moreover, existing filtering and calibration techniques are insufficient for preserving critical neural information while eliminating artifacts, thereby reducing accuracy and responsiveness in real-time brain-computer interface applications.

[0008] In order to overcome the aforementioned drawbacks, there exists a need in the art to develop system that provides enhanced real-time signal processing, dynamic adaptability to user-specific brain patterns, and improved noise reduction without compromising neural data integrity. Such a system should ensure accurate, low-latency control of external devices, ultimately improving usability, responsiveness, and performance across a variety of assistive and interactive applications.

OBJECTS OF THE INVENTION

[0009] The principal object of the present invention is to overcome the disadvantages of the prior art.

[0010] An object of the present invention is to develop a system that is capable of improve the accuracy of brain signal processing by reducing interference from unwanted noise while keeping the important brain activity data intact.

[0011] Another object of the present invention is to develop a system that is capable of support real-time communication between the brain and external source by reducing processing delays, thereby ensuring that users experience smooth and timely interaction with connected systems.

[0012] Another object of the present invention is to develop a system that is capable of personalizing the brain-computer interface system for individual users by adapting to their unique brain activity patterns, thereby ensuring better accuracy in detecting and interpreting signals for different users.

[0013] Yet another object of the present invention is to develop a system that is capable of achieving accurate and responsive control of external sources through improved brain signal decoding, thereby enabling more intuitive and effective interaction.

[0014] The foregoing and other objects, features, and advantages of the present invention will become readily apparent upon further review of the following detailed description of the preferred embodiment as illustrated in the accompanying drawings.

SUMMARY OF THE INVENTION

[0015] The present invention relates to a brain computer interface system that is capable of provide reliable detection of brain signals by adjusting the system settings based on each user’s brain activity patterns and increasing the overall performance of brain-computer systems, thereby ensuring cleaner signal input and better signal interpretation.

[0016] According to an embodiment of the present invention, brain-computer interface system comprising of a neural signal processing module including a deep learning models, adaptive filtering unit and blind source separation component, configured to use deep learning models, including convolutional neural networks and recurrent neural networks, to filter noise while preserving essential brain signals, a noise suppression module including spectral subtraction Unit, wavelet transform processor, AI-Driven adaptive filter, configured to combine spectral subtraction, wavelet transforms, and AI-driven adaptive filtering to remove muscle artifacts, environmental noise, and electrode interference in real time, a signal acquisition module including high-resolution electrodes, electrode placement protocol and signal amplification unit, configured to use high-resolution electrodes and optimized placement protocols to enhance the capture of brain signals, an automated calibration module including brain activity analyser, pre-processing adjustment unit, feedback system, configured to adjust signal pre-processing settings based on user-specific brain activity patterns, a real-time processing module including edge computing processor, neural decoding protocol and application interface, configured to use edge computing and optimized neural decoding protocols to minimize delays for applications such as prosthetic control, communication, and gaming.

[0017] While the invention has been described and shown with particular reference to the preferred embodiment, it will be apparent that variations might be possible that would fall within the scope of the present invention.

BRIEF DESCRIPTION OF THE DRAWINGS

[0018] These and other features, aspects, and advantages of the present invention will become better understood with regard to the following description, appended claims, and accompanying drawings where:
Figure 1 illustrates a flowchart depicting work flow of a brain-computer interface system.

DETAILED DESCRIPTION OF THE INVENTION

[0019] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the spirit and scope of the invention as defined in the claims.

[0020] In any embodiment described herein, the open-ended terms "comprising," "comprises,” and the like (which are synonymous with "including," "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of," consists essentially of," and the like or the respective closed phrases "consisting of," "consists of, the like.

[0021] As used herein, the singular forms “a,” “an,” and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.

[0022] The present invention relates to a brain-computer interface system that is capable of improve the accuracy of brain signal processing by reducing interference from unwanted noise while keeping the important brain activity data intact and allowing smooth operation of external sources through more accurate and responsive decoding of brain signals, thereby enabling more intuitive and effective interaction.

[0023] Referring to Figure 1, a flowchart depicting work flow of a brain-computer interface system is illustrated. The system discloses herein comprising of a signal acquisition module including high-resolution electrodes, electrode placement protocol and signal amplification unit, configured to use high-resolution electrodes and optimized placement protocols to enhance the capture of brain signals. The signal acquisition module interfaces with the automated calibration module to optimize electrode placement based on user-specific brain activity patterns.

[0024] The signal acquisition module is designed to detect, amplify, and transmit physiological signals such as brain activity for further processing. It typically includes high-resolution electrodes to capture detailed electrical signals, an optimized electrode placement protocol to ensure accurate and consistent positioning, and a signal amplification unit to boost low-amplitude bio-signals for clearer analysis. This module plays a critical role in biomedical applications like EEG, brain-computer interfaces, and neurological diagnostics. By enhancing signal quality at the source, the signal acquisition module ensures reliable, high-fidelity data collection essential for accurate interpretation and effective real-time system response.

[0025] The High-resolution electrodes are the sensors designed to detect electrical activity from the brain with fine spatial detail and high sensitivity. These electrodes typically have smaller contact areas and are made from biocompatible materials that minimize skin impedance and maximize signal fidelity. Their increased spatial resolution allows for more precise localization of brain activity, essential in applications such as EEG-based diagnostics, brain-computer interfaces, and neurological research. By capturing subtle variations in electrical potentials across the scalp, high-resolution electrodes provide cleaner, more detailed recordings, which significantly improve the accuracy and reliability of signal interpretation and downstream processing.

[0026] The Electrode Placement Protocol defines the standardized method for positioning electrodes on the scalp to ensure consistent, high-quality brain signal acquisition. Based on systems like the international 10–20 or 10–10 layouts, this protocol considers anatomical landmarks to optimize coverage of key brain regions while minimizing noise and interference. Proper placement maximizes the signal-to-noise ratio and ensures repeatability across sessions and subjects. It also helps avoid artifacts from muscle movement or eye blinks. Adherence to a placement protocol is critical for obtaining reliable data, especially in clinical, research, and real-time neural interface applications.

[0027] The Signal Amplification Unit boosts the low-amplitude brain signals detected by the electrodes to levels suitable for processing and analysis. Brain signals, such as EEG, are typically in the microvolt range and can be easily overwhelmed by noise without amplification. This unit includes low-noise amplifiers and often implements differential amplification to enhance signal quality while suppressing common-mode noise. It preserves the integrity of the original neural information by minimizing distortion and introducing minimal latency. Reliable amplification is essential in real-time monitoring systems, ensuring the brain’s electrical activity is accurately captured for further interpretation or control purposes.

[0028] The signal acquisition module interfaces with the automated calibration module to enhance the precision and reliability of brain signal capture. While the signal acquisition module collects neural signals using high-resolution electrodes and standardized placement protocols, the automated calibration module dynamically analyzes the user’s brain activity patterns to fine-tune electrode positioning.

[0029] This user-specific calibration accounts for anatomical and neurological variability, ensuring optimal contact and alignment with regions of interest. The system may use real-time feedback to adjust electrode positions or weighting, maximizing signal strength and minimizing noise or artifacts. This integration significantly improves signal quality and consistency, enabling more accurate downstream processing for applications like brain-computer interfaces, neuro feedback systems, or cognitive monitoring tools

[0030] The system further includes neural signal processing module including a deep learning models, adaptive filtering unit and blind source separation component, configured to use deep learning models, including convolutional neural networks and recurrent neural networks, to filter noise while preserving essential brain signals. The neural signal processing module further includes adaptive filtering and blind source separation techniques, such as independent component analysis and principal component analysis, to eliminate muscle artifacts and environmental noise.

[0031] The neural signal processing module is a system designed to analyze and interpret signals from the nervous system, such as brain or nerve activity. It typically involves filtering, amplification, feature extraction, and classification of bioelectrical signals like EEG, EMG, or spikes. These modules are used in brain-computer interfaces (BCIs), neuroprosthetics, and neuroscience research. They convert raw neural data into meaningful information or control signals for devices

[0032] Deep learning models in neural signal processing are used to learn complex patterns in brain signals. They are trained on large datasets to distinguish between neural activity and noise. This module includes Convolutional Neural Networks (CNNs) for spatial pattern recognition and Recurrent Neural Networks (RNNs) for capturing temporal dynamics. CNNs filter out spatial noise like muscle artifacts, while RNNs handle time-varying noise such as baseline drifts, ensuring essential brain signals are preserved.

[0033] Adaptive Filtering Unit dynamically adjusts its parameters to filter out non-stationary noise, such as electrical interference or motion artifacts. It continuously monitors the input signal and adapts in real time, maintaining signal quality across varying conditions.

[0034] Blind Source Separation (BSS) Component such as Independent Component Analysis (ICA), separate mixed signals into independent sources without prior knowledge of the source characteristics. In neural signal processing, this helps isolate brain signals from overlapping noise sources like eye blinks or muscle movements, enabling clearer analysis of the neural activity.

[0035] The adaptive filtering is a real-time signal processing technique that continuously adjusts its parameters to suppress noise while retaining relevant brain signals. In the neural signal processing module, it is used to eliminate dynamic and unpredictable noise sources, such as muscle artifacts or electrical interference. The filter adapts based on the characteristics of the input signal, ensuring effective removal of unwanted components without distorting the underlying neural activity. This is especially useful in mobile or clinical environments where noise conditions can vary. The feature dynamically adjusts filtering parameters based on user-specific neural patterns.

[0036] Blind Source Separation (BSS) – ICA & PCA Blind Source Separation techniques like Independent Component Analysis (ICA) and Principal Component Analysis (PCA) are employed to isolate and remove artifacts. ICA separates the mixed neural signal into statistically independent sources, allowing removal of artifacts like eye blinks or muscle noise. PCA reduces dimensionality by identifying the principal components, filtering out components with low variance typically associated with noise. Both techniques enhance signal clarity by disentangling true neural signals from overlapping environmental or physiological noise.

[0037] The system includes a noise suppression module including spectral subtraction Unit, wavelet transform processor, AI-Driven adaptive filter, configured to combine spectral subtraction, wavelet transforms, and AI-driven adaptive filtering to remove muscle artifacts, environmental noise, and electrode interference in real time.

[0038] The noise suppression module is an electronic based system designed to reduce unwanted background noise in audio signals. It identifies and filters out non-essential or disruptive sounds while preserving the clarity of the desired signal, such as speech or music. Commonly used in communication approaches, microphones, hearing aids, and audio recording systems, the module enhances audio quality in noisy environments. Techniques may include adaptive filtering, spectral subtraction, or machine learning protocols. By minimizing ambient interference, the noise suppression module improves user experience and ensures clearer, more intelligible audio output in both real-time and recorded applications.

[0039] The Spectral Subtraction Unit reduces noise by analyzing the frequency spectrum of a signal and subtracting an estimate of the noise spectrum from it. This technique is particularly effective for eliminating stationary or slowly varying background noise, such as electrical hum or ambient environmental sounds. The unit first captures a noise profile during silent or low-activity periods, then subtracts this profile from the incoming signal in real time. It is computationally efficient and preserves speech or signal quality when properly tuned. Widely used in audio processing and biomedical applications, it helps enhance the clarity of the target signal.

[0040] The Wavelet Transform Processor uses wavelet analysis to decompose a signal into multiple time-frequency components, enabling detection and removal of transient or localized noise like muscle artifacts. Unlike traditional Fourier methods, wavelet transforms provide high temporal resolution for short-duration events and are well-suited for non-stationary signals. This makes them particularly effective in biomedical signal processing, such as EEG or ECG, where sudden signal distortions need to be isolated without affecting core signal integrity. The processor selectively suppresses noise components in specific frequency bands while retaining essential signal features, offering a powerful tool for real-time, precision noise removal.

[0041] The AI-Driven Adaptive Filter dynamically adjusts its filtering parameters using machine learning protocols to identify and suppress complex, time-varying noise. It can distinguish between meaningful signal patterns and interference such as electrode motion artifacts or sudden environmental changes. Unlike fixed filters, this component learns from ongoing signal characteristics, continuously optimizing its performance in real time. By leveraging artificial intelligence, it can adapt to new or unpredictable noise conditions with high accuracy. This makes it ideal for applications requiring high signal fidelity under variable conditions, such as wearable medical devices or brain-computer interfaces, where traditional filters may struggle.

[0042] The system incorporates an automated calibration module including brain activity analyser, pre-processing adjustment unit, feedback system, configured to adjust signal pre-processing settings based on user-specific brain activity patterns.

[0043] The automated calibration module is designed to optimize the setup of signal acquisition components based on real-time analysis of user-specific brain activity. It evaluates initial signal quality and dynamically adjusts parameters such as electrode positioning, contact pressure, or signal weighting to ensure optimal alignment with individual neural patterns.

[0044] By accounting for anatomical differences and minimizing noise sources, the module enhances signal fidelity and consistency. This automated process reduces the need for manual adjustments, speeds up system setup, and ensures high-quality data collection. It is especially valuable in applications like brain-computer interfaces, where precise and stable signal acquisition is critical for reliable performance.

[0045] The Brain activity analyzer evaluates incoming neural signals to identify user-specific patterns, such as dominant frequency bands, signal amplitude, and regional activity levels. By recognizing these individualized characteristics, it creates a dynamic profile of the user’s brain activity. This analysis serves as the foundation for personalized system calibration, guiding how electrodes should be tuned and where adjustments are necessary. In the context of the invention, it enables more accurate interpretation and filtering of brain signals, ensuring that the signal acquisition and processing systems are aligned with the user’s unique neural signatures for optimal performance in real-time cognitive or neuro feedback applications.

[0046] The Pre-processing Adjustment Unit modifies key signal conditioning parameters such as filtering thresholds, gain levels, and artifact rejection settings based on input from the Brain Activity Analyser. These adjustments help tailor the signal pipeline to the user’s specific brain activity, compensating for natural variability and improving signal clarity. This unit ensures that the pre-processing stage enhances meaningful brain signals while minimizing noise, such as muscle artifacts or baseline drift. Within the invention, this component enables real-time tuning of signal quality, allowing the downstream processing and interpretation systems to operate with cleaner, more personalized neural input for improved system responsiveness and accuracy.

[0047] The Feedback System provides continuous real-time performance evaluation by monitoring signal quality metrics such as signal-to-noise ratio, coherence, and consistency over time. The system relays this information back to the Brain activity analyzer and pre-processing adjustment unit to refine their outputs. If degradation in signal quality is detected due to motion, fatigue, or environmental changes.

[0048] The system automatically readjusts calibration parameters. In the invention, this feedback loop ensures robust and adaptive calibration without manual intervention, maintaining high signal integrity during prolonged use. It is essential for applications like brain-computer interfaces or cognitive monitoring, where sustained accuracy is critical for effective, real-time operation.

[0049] Additionally, the system includes a real-time processing module including edge computing processor, neural decoding protocol and application interface, configured to use edge computing and optimized neural decoding protocols to minimize delays for applications such as prosthetic control, communication, and gaming. The real-time processing module integrates with the neural signal processing module to enhance decoding accuracy for low-latency brain-computer interactions.

[0050] The real-time processing module continuously analyzes incoming signals as they are received, enabling immediate interpretation and response. In the context of neural or biomedical systems, it processes brain signals to detect patterns, filter noise, and extract meaningful features without delay. This module uses efficient protocols optimized for low latency, ensuring data is handled fast enough to support time-sensitive applications such as brain-computer interfaces, neuro feedback, or prosthetic control. By delivering instant feedback or control commands, the real-time processing module enhances system responsiveness, accuracy.

[0051] The Edge Computing Processor handles data processing locally, near the signal acquisition source, reducing latency by minimizing data transfer to remote servers. This localized computing power enables rapid analysis of neural signals, essential for time-sensitive applications like prosthetic control or interactive gaming. By performing complex computations, it preserves data privacy and reduces reliance on network connectivity. In the invention, this processor ensures that brain signal decoding and response generation happen in real time, providing smooth, responsive interactions and enhancing user experience in applications requiring immediate feedback.

[0052] The Neural Decoding Protocol translates raw neural signals into meaningful commands or information using optimized protocols tailored for speed and accuracy. It identifies patterns such as motor intentions or cognitive states from complex brain data, converting them into actionable outputs. Within the invention, this protocol is fine-tuned to work seamlessly with the edge computing processor, minimizing processing delays, that supports a wide range of applications including prosthetic limb movement, communication aids, and gaming controls by reliably interpreting brain activity in real time, thus enabling intuitive and efficient user-device interactions.

[0053] The application interface connects the real-time processing outputs to external systems or applications, enabling user interaction with devices like prosthetics, communication tools, or games. It translates decoded neural commands into standardized signals or control inputs compatible with various platforms. In the invention, this interface ensures smooth communication between the neural decoding module and end-user applications, supporting customization and scalability. By facilitating seamless integration, it allows users to control devices intuitively and without noticeable delay, enhancing the practicality and effectiveness of brain-computer interface.

[0054] The real-time processing module integrates closely with the neural signal processing module to significantly enhance decoding accuracy while maintaining low latency essential for brain-computer interactions (BCIs). The neural signal processing module preprocesses raw brain signals, filtering noise and extracting relevant neural features. These refined signals are then fed into the real-time processing module to translate neural activity into actionable commands instantly.

[0055] This integration ensures that only high-quality, meaningful data is processed, improving the precision of intent recognition. By combining efficient signal conditioning with rapid decoding, the system supports responsive operation crucial for applications like prosthetic control, communication aids, and interactive gaming where real-time feedback and accuracy directly impact usability and user experience.

[0056] The present invention works best in the following manner, where the system includes the signal acquisition module including the high-resolution electrodes, the electrode placement protocol, and the signal amplification unit, configured to detect, amplify, and transmit brain signals with high fidelity. The high-resolution electrodes capture fine spatial details of neural activity, while the electrode placement protocol ensures consistent and optimized positioning based on the international systems. The signal amplification unit boosts low-amplitude brain signals using low-noise and differential amplification techniques. The signal acquisition module interfaces with the automated calibration module, including the brain activity analyzer, the pre-processing adjustment unit, and the feedback system, which dynamically adjusts electrode positioning and signal parameters based on user-specific brain patterns to optimize contact, minimize noise, and enhance consistency. The neural signal processing module, comprising deep learning models, the adaptive filtering unit, and the blind source separation component, receives signals from the acquisition module to filter noise and isolate relevant features using convolutional and recurrent neural networks, independent component analysis, and principal component analysis. The noise suppression module, consisting of the spectral subtraction unit, the wavelet transform processor, and the AI-driven adaptive filter, further eliminates muscle artifacts, environmental interference, and electrode noise in real time. The real-time processing module, including the edge computing processor, the neural decoding protocol, and the application interface, processes the refined signals locally to minimize latency and decode neural activity into actionable commands for external devices. The integration of modules ensures accurate, low-latency brain-computer interaction suitable for prosthetic control, communication tools, and gaming applications.

[0057] Although the field of the invention has been described herein with limited reference to specific embodiments, this description is not meant to be construed in a limiting sense. Various modifications of the disclosed embodiments, as well as alternate embodiments of the invention, will become apparent to persons skilled in the art upon reference to the description of the invention. , Claims:1) A brain-computer interface system comprising:

i) a signal acquisition module including high-resolution electrodes, electrode placement protocol and signal amplification unit, configured to use high-resolution electrodes and optimized placement protocols to enhance the capture of brain signals;
ii) a neural signal processing module including a deep learning models, adaptive filtering unit and blind source separation component, configured to use deep learning models, including convolutional neural networks and recurrent neural networks, to filter noise while preserving essential brain signals;
iii) a noise suppression module including spectral subtraction Unit, wavelet transform processor, AI-Driven adaptive filter, configured to combine spectral subtraction, wavelet transforms, and AI-driven adaptive filtering to remove muscle artifacts, environmental noise, and electrode interference in real time;
iv) an automated calibration module including brain activity analyser, pre-processing adjustment unit, feedback system, configured to adjust signal pre-processing settings based on user-specific brain activity patterns; and
v) a real-time processing module including edge computing processor, neural decoding protocol and application interface, configured to use edge computing and optimized neural decoding protocols to minimize delays for applications such as prosthetic control, communication, and gaming.

2) The system as claimed in claim 1, wherein the neural signal processing module further includes adaptive filtering and blind source separation techniques, such as independent component analysis and principal component analysis, to eliminate muscle artifacts and environmental noise.

3) The system as claimed in claim 1, feature that dynamically adjusts filtering parameters based on user-specific neural patterns.

4) The system as claimed in claim 1, wherein the signal acquisition module interfaces with the automated calibration module to optimize electrode placement based on user-specific brain activity patterns.

5) The system as claimed in claim 1, wherein the real-time processing module integrates with the neural signal processing module to enhance decoding accuracy for low-latency brain-computer interactions.