39 results about "Brain computer interfacing" patented technology

Systems and methods are described herein for authentication and password security. The system may detect involuntary and voluntary brain signals of a user and measure the characteristics of those signals. The signals may be detected and analyzed using a wearable device comprising a plurality of sensors. The system may authenticate the identity of the user by triggering the user to imagine content or react to presented content. The content may comprise an image or movement, and brain signals of the user may indicate signals that are consistent for the user. The system may authenticate the user based on the brain signals based on data stored for the user or profile for the user. This determination may be performed by a machine learning model trained to classify users based on the brain signal data.

A system and method relating to a brain-computer interface in which 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. The visual stimulus includes a feedback element that varies according to a measure of attention on the or each overlaid object.

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.

Systems and methods associated with mind / brain-computer interfaces are disclosed. Certain implementations may include or involve processes of collecting and processing brain activity data, such as those associated with the use of a brain-computer interface that enables, for example, decoding and / or encoding a user's brain functioning, neural activities, and / or activity patterns associated with thoughts, including sensory-based thoughts, determining user attention and / or intentions during interactions within virtual environment and in other applications. Consistent with various aspects of the disclosed technology, systems and methods herein include and / or involve features and functionality enabling hands-free selection of UI elements in virtual environment or on other media.

An apparatus, system, and method of a brain computer interface in a headset including an augmented reality display, one or more sensors, a processing module, at least one biofeedback device, and a battery. The interface may include a printed circuit board that has the sensors to read bio-signals, provides biofeedback, and performs the processing, analyzing, and mapping of bio-signals into output. The output provides feedback via stimulation of multiple sensory brain systems of a user, including audio and visual on the augmented reality display, or audio and haptic in terms of vibration patterns that a human user may feel. All together this forms a closed-loop system, by detecting the bio-signal, then providing sensory-feedback, which in turn enhances the bio-signal.

Methods and systems for using a brain computer interface (BCI) system in a manner to enable only the BCI user to control digital devices or software via the digital motor output (DMO), thereby supporting the BCI user's autonomy by securing private use of a digital device to exclude or control the use of such devices by others.

One aspect of the present invention acquires data representing an action corresponding to an instruction recalled by a user, determines the type of the corresponding instruction on the basis of data representing the acquired action, selects a sensory stimulus previously associated with the determined type of the instruction, and executes processing for applying the selected sensory stimulus to the user. Meanwhile, the one aspect of the present invention acquires a brain wave pattern of the user when the sensory stimulus is applied, generates an instruction estimation model by using a set of the inputted instruction type and the acquired brain wave pattern as learning data, and stores the generated instruction estimation model.

The present invention introduces an approach to neural tissue engineering by developing vascularized neurospheres (VNSs) through novel methods, systems, and kits. These VNSs are generated using a unique blend of neural, vascular, and supportive cells, cultured in specialized hydrogel-containing media. The VNSs feature an integrated vascular network, serving as a physiologically relevant in vitro model. The invention also offers a grafting technique for therapeutic applications in neural disorders and injuries, as well as facilitating brain-computer interfacing. Beyond therapeutics, the VNSs have broad applications in drug development, synthetic biology, disease modeling, and more. The invention also includes kits for VNS generation and methods for interfacing VNSs with probes for applications of brain-computer interfacing. Overall, this patent represents a significant leap in the field, bridging the gap between in vitro and in vivo systems and opening new avenues in research, therapeutics, and bioengineering.

A brain computer interface for interfacing with a brain of a subject is provided. The brain computer interface includes one or more shanks. Each shank includes an array of pixels and output traces. Each pixel includes an electrode and a front-end circuit positioned at a site of the electrode. The front-end circuit is configured to reduce noise in signals recorded by the electrode, and further configured to multiplex the signals. A density of power consumption of the each pixel is equal to or less than 1 μW per area of 50 μm by 50 μm. The output traces are electrically coupled with the array of pixels. A number of output traces is less than a number of pixels in the array due to multiplexing. The one or more shanks are configured to be inserted on and / or into a brain of a subject.

An apparatus, system, and method of a brain computer interface in a headset including an augmented reality display, one or more sensors, a processing module, at least one biofeedback device, and a battery. The interface may include a printed circuit board that has the sensors to read bio-signals, provides biofeedback, and performs the processing, analyzing, and mapping of bio-signals into output. The output provides feedback via stimulation of multiple sensory brain systems of a user, including audio and visual on the augmented reality display, or audio and haptic in terms of vibration patterns that a human user may feel. All together this forms a closed-loop system, by detecting the bio-signal, then providing sensory-feedback, which in turn enhances the bio-signal.

A system and method relating to a brain-computer interface in which a visual stimulus overlaying one or more objects is provided, 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 method allows the interface to discriminate between merely viewing of a display object and deliberate selection of that display object (for example, as a trigger to a further action).

This invention provides a brain-computer interface (BCI) testing cap and monitoring system for sleep disorder monitoring, belonging to the field of BCI medical technology. The BCI testing cap for sleep disorder monitoring includes a cap body, a flexible electrode array, an integrated signal acquisition unit, an electrode status self-test module, a wireless transmission and local storage module, and a power management module. The cap body is flexible, and a monitoring controller is mounted on its side wall. This invention provides a BCI testing cap and monitoring system for sleep disorder monitoring, achieving low-interference home acquisition of medical-grade multimodal sleep physiological signals, and constructing a closed-loop process from signal acquisition, intelligent analysis, accurate classification to intervention decision-making, providing a complete technical solution for the precise diagnosis and treatment of insomnia disorders.

The invention relates to a detector and a method for detecting the frequency of a neural signal, and to a device and a method for reproducing paged contents, in particular digital books. Another subject of the invention is a device for reproducing paged contents, the device comprising: - a permanent visual neural stimulator at a given frequency, the given frequency being associated with a given reproduction device command, the permanent visual neural stimulator being positioned on the reproduction device according to the associated given command. The user's interaction with the pages of the content is thus a hands-free solution that makes it possible to improve the user's experience. Such a reproduction device, in particular an e-reader, then takes advantage of the advances in brain computer interface (BCI) technologies by integrating this technology.

The invention provides a computer-implemented method for providing a neural network for analyzing EEG data for use in providing a brain computer interface, the method comprising obtaining training data from EEG data of a subject and movement reference data associated with the EEG data, the EEG data and movement reference data acquired concurrently and while a subject carries out motions and / or imagines motions of a body part directed at controlling a software, wherein obtaining the training data comprises automatic classification of the movement reference data and automatically labeling at least part of the EEG data based on the classification of the movement reference data associated with the EEG data; and using the training data to train a neural network to predict, from EEG data, a motion of the body part imagined and / or carried out by the subject.

Systems and methods associated with brain computer interfaces (BCIs) are disclosed. Embodiments herein include features related to one or more of optical-based brain signal acquisition and / or processing, decoding / encoding modalities, brain-computer interfacing, AR / VR content interaction, and / or electronic wave (E-wave) detection, determination and / or processing, among other features. Certain example implementations may include or involve aspects or processes such as monitoring the gaze and brain activity of a user (e g., via a. BCI, etc.) interacting with a user interface (UI), detecting that the gaze of the user is directed at an element of the UI, detecting or processing an E-wave in temporal conjunction with the gaze at the element, determining that the users intends to interact with the element, and / or triggering an interaction with the element. Further, aspects of present systems and methods may be configured to leverage BCIs and / or non-invasive wearable device features to provide enhanced user interactions for various devices.

Systems and methods associated with mind / brain-computer interfaces are disclosed. Certain implementations may include or involve processes of collecting and processing brain activity data, such as those associated with the use of a brain-computer interface that enables, for example, decoding and / or encoding a user's brain functioning, neural activities, and / or activity patterns associated with thoughts, including sensory -based thoughts. Consistent with various aspects of the disclosed technology, systems and methods herein include functionality whereby a user can hands-free select or click UI elements in a mixed reality environment or on a 2D computer, phone or other such screen. In some embodiments, the disclosed technology may involve a brain-interface that can decode where the user is looking (i.e., determine their visual attention) and also decode their intention to select or 'click' the UI element desired. The user's intention to select or 'click' the UI element (the 2nd step) can be achieved through an imagined movement or an imagined word.

An embodiment for executing a driver's expectations in view of contextual data and driver expectation data to improve driver safety. The embodiment may automatically establish a connection between an autonomous vehicle and a brain computer interface (BCI) device worn by a driver of the autonomous vehicle. The embodiment may automatically utilize sensors and the BCI device to gather contextual data, automatically process the contextual data, and conduct predictive analysis of correlated driver behavior. The embodiment may automatically detect a traffic context and generate a predicted driver expectation for the traffic context. The embodiment may, in response to detecting that the predicted driver expectation for the traffic context is safe, automatically execute the predicted driver expectation. The embodiment may, in response to detecting that the predicted driver expectation is unsafe, automatically execute a safety remedial action.

Methods and systems for using a brain computer interface (BCI) system in a manner to enable only the BCI user to control digital devices or software via the digital motor output (DMO), thereby supporting the BCI user's autonomy by securing private use of a digital device to exclude or control the use of such devices by others.

Systems and methods are provided, which may be associated with brain computer interface (BCI), and which may use brain activity monitoring data in determining output for presenting a message or implementing a command on a device or system. Two or more signals may be obtained from a user, each generated by the user at least in part by the user engaging in specific thinking to cause value(s) of parameter(s) relating to the brain activity of the user to meet specified condition(s), where that which is thought in the specific thinking is not required to be related to the message or the command. The signals may be used in determining data values that may be used in determining a set of data values. The set of data values may be used in determining the message for presentation or the command for implementation on a system or device.

Devices, systems, software, and methods are provided for volitional neurostimulation using neurofeedback for treatment of neurological and psychiatric disorders. Methods are provided for training patients to self-modulate a neural signal that can be used to volitionally control their neurostimulation. The methods use a brain computer interface connected to a display, wherein magnitude of a neural signal is represented as the height of a graphical object on the display. The subject increases or decreases the amplitude of the stimulation delivered by adjusting the vertical positioning of the graphical object on the display to reach a selected target. This volitional neurostimulation technique should be applicable across a range of different neurological and psychiatric conditions to support personalized control of neurostimulation.

Disclosed are methods, systems and non-transitory computer readable memory for an assistive brain computer interface. For instance, a system may include a set of sensors, wherein the set of sensors are configured to obtain sensor data; a stylus configured to collect stroke data; and a user device. The user device may be configured to: receive the stroke data from the stylus and the sensor data from the set of sensors; generate renders of strokes based on the stroke data and the sensor data; and display the renders of the strokes to a user.

An apparatus, system, and method of a brain computer interface in a headset including an augmented reality display, one or more sensors, a processing module, at least one biofeedback device, and a battery. The interface may include a printed circuit board that has the sensors to read bio-signals, provides biofeedback, and performs the processing, analyzing, and mapping of bio-signals into output. The output provides feedback via stimulation of multiple sensory brain systems of a user, including audio and visual on the augmented reality display, or audio and haptic in terms of vibration patterns that a human user may feel. All together this forms a closed-loop system, by detecting the bio-signal, then providing sensory-feedback, which in turn enhances the bio-signal.

Systems and methods to implement a wearable brain computer interface (BCI) device for transmitting communications via a telecommunications network. One system includes a processing system of a wearable BCI device that is configured to receive an electrical signal related to neurological activity. The processing system may be configured to provide the electrical signal to a machine learning model, the machine learning model to extract a pattern from the electrical signal and determine, based on the pattern, an intent of the electrical signal. The processing system may be configured to receive, from the machine learning model, intent data related to the intent of the electrical signal. The processing system may be configured to encode the intent data to generate encoded data compatible for transmission via a 5G NR network. The processing system may be configured to transmit the encoded data via the 5G NR network to a recipient user equipment.

Systems, devices and methods are provided for conducting a therapy session, which includes performing a BCI training session utilizing feedback, via a feedback device, to train a subject to generate defined brain signals.

The present disclosure relates to a monolithic thin-film lead assembly and methods of microfabricating a monolithic thin-film lead assembly. Particularly, aspects of the present disclosure are directed to a monolithic thin-film lead assembly that includes a cable having a proximal end, a distal end, a supporting structure that extends from the proximal end to the distal end, and conductive traces formed on a portion of the supporting structure. The supporting structure includes one or more layers of dielectric material. The monolithic thin-film lead assembly further includes an interface formed on the supporting structure at the distal end of the cable. The interface includes electrodes and / or sensors in electrical connection with the conductive traces, and the supporting structure has at least one curved portion disposed between a first set of electrodes and a second set of electrodes, and / or between a first set of sensors and a second set of sensors.

A system and method relating to a brain-computer interface in which 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. The visual stimulus includes a feedback element that varies according to a measure of attention on the or each overlaid object.

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.

An adaptive state-observation model based on reinforcement learning (RL) is presented for implementing a brain-machine interface (BMI) for decoding brain signals to determine motor actions and control a machine to perform the motor actions. In the model, the brain signals are processed by a neural network (NN) for applying a nonlinear mapping defined by NN weights to the brain signals to thereby produce transformed brain signals. The NN learns the nonlinear mapping by RL to allow the weights to be adaptively and continuously updated to follow the nonlinearities and nonstationarities of the brain signals. The transformed brain signals are processed by a Kalman filter (KF) to produce control signals for controlling the machine to perform the motor actions, thereby utilizing the KF to provide smooth generation of the control signals while reducing adverse effects of the nonlinearities and nonstationarities of the brain signals on the KF.

The present disclosure provides a minimally invasive implantable needle for brain-computer interface, and a preparation method thereof and related products. The method comprises: providing a metal substrate made of biocompatible metal, performing first overall electrochemical polishing on the metal substrate to provide a clean and smooth initial surface; using an ultrafast laser to scan and cut an implantable needle array pattern on the polished metal substrate, the cutting depth being 85% to 95% of the thickness of the metal substrate, the scanned and cut metal substrate comprising a plurality of implantable needle rudiments connected to the substrate body by micro-mechanical connection points; performing second overall electrochemical polishing on the scanned and cut metal substrate until the micro-mechanical connection points are dissolved so that the implantable needle rudiments are naturally separated from the substrate body, thereby obtaining the implantable needle. The present disclosure can achieve a product yield close to 100%, ensure that all implantable needles have perfect geometric shapes and intact surfaces, and can be produced in a wafer-level batch manner.