System and method for music recommendation for audio and nerve stimulation

Abstract

A method includes receiving one or more attributes of a patient and receiving a plurality of audio content from a data source associated with the patient. For at least some of the plurality of audio content, a prediction of the amplitude and affinity score of the brain response signal at the target frequency is generated. A first audio content is selected from the plurality of audio contents based on the prediction of the amplitude and the affinity score for the first audio content. The control signal is transmitted to the output device to cause the output device to present the first audio content to provide an audio stimulus to the patient.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application No. 63 / 434,603, filed on December 22, 2022, the contents of which are incorporated herein by reference in their entirety.

[0003] public domain

[0004] The present disclosure relates generally to neural stimulation, including but not limited to systems and methods for audio and music recommendations for neural stimulation.

[0005] background

[0006] Neural oscillations occur in humans and animals and include rhythmic or repetitive neural activity in the central nervous system. Neural tissue can generate oscillatory activity through mechanisms within individual neurons or through interactions between neurons. Oscillations can manifest as periodic fluctuations in membrane potential or rhythmic patterns of action potentials, which can produce oscillatory activation of postsynaptic neurons. The synchronized activity of a group of neurons can give rise to macroscopic oscillations, which can be observed by sensing electric or magnetic fields in the brain using techniques such as electroencephalography (EEG), intracranial EEG (iEEG) (also known as electrocorticography (ECoG)), and magnetoencephalography (MEG).

[0007] Overview

[0008] According to the systems and methods described herein, neural stimulation can be provided via rhythmic light stimulation presented simultaneously with auditory stimulation through music. The combination of music and light stimulation can induce neural oscillation effects or stimulation. The combined stimulation can adjust, control, or otherwise affect the frequency of neural oscillations to provide beneficial effects to one or more cognitive states, cognitive functions, the immune system, or inflammation, while mitigating or preventing adverse consequences to cognitive states or cognitive functions. For example, the systems and methods of the present technology can treat, prevent, prophylactically, or otherwise affect Alzheimer's disease or other cognitive diseases, such as Parkinson's disease, dementia, and the like.

[0009] Audio content can be recommended for delivery to a user based on the predicted therapeutic effect of the audio content. For example, the therapeutic effect can be a neural oscillation effect of a defined frequency or frequency range in one or more locations of interest in the brain. The audio content can include an effectiveness weighting indicating the therapeutic effect in response to the audio content. The recommendation engine can associate the audio content with the weight. The recommendation engine can select an audio signal (e.g., a song) based on the effectiveness weighting or attributes of the user. For example, songs can be selected based on the user's historical interests or the user's peer group. The user's peer group can be defined based on the user's age, social interaction, or cognitive function. For example, the peer group can be defined based on neurological condition or cognitive ability. The recommendation engine can select songs to maximize listening time or maximize the predicted therapeutic effect. The recommendation engine can select songs based on input from the user or another data entrant.

[0010] In various aspects, the present disclosure relates to systems and methods for music recommendation for audio neurostimulation. One or more processors may be configured to receive one or more attributes of a patient and receive a plurality of audio content from a data source associated with the patient. One or more processors may be configured to generate a prediction of the amplitude and affinity score of a brain response signal at a target frequency for at least some of the plurality of audio content. One or more processors may be configured to select a first audio content from the plurality of audio content based on the prediction of the amplitude and affinity score for the first audio content. One or more processors may be configured to transmit a control signal to an output device to cause the output device to present the first audio content, thereby providing audio stimulation to the patient.

[0011] In some embodiments, the affinity score of the first audio content is based on the user's social peers and the user's age. In some embodiments, the one or more processors may generate a prediction of the amplitude of the brain response signal and the affinity score for each of the plurality of audio content from the data source. In some embodiments, the data source includes a library associated with the patient, and wherein at least some of the plurality of audio content from the library are incorporated into the library by peers of the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] The accompanying drawings are not intended to be drawn to scale. Like reference numerals and names in the various drawings represent like elements. For clarity, not every component may be labeled in every figure.

[0014] Figure 1is a diagram illustrating frequencies associated with a specific basic music stimulus selected by an oscillation selection module (OSM) and a frequency range present in each frequency band according to an example embodiment of the present disclosure.

[0015] Figure 2 is a diagram showing, on the left, magnetoencephalographic (MEG) recordings of the human auditory cortex recorded while subjects listened to rhythmic auditory stimuli of two different tempos and highlighting, on the right, some of the brain regions that exhibited such responses.

[0016] Figure 3 is a block diagram of a system for providing neural stimulation according to an example embodiment of the present disclosure.

[0017] Figure 4 is a diagram showing an example embodiment according to the present disclosure Figure 3 Diagram of the operation of the system utilizing the resulting brain stimulation.

[0018] Figure 5-Figure 6 is a diagram showing an example embodiment according to the present disclosure Figure 3 Figure 3 shows example stimuli provided by the system using different songs, where panel A compares the auditory rhythmic frequency (i.e., onset spectrum) of the music with the frequency of auditory 40 Hz pulse trains, and panel B compares the visual frequency stimulated by the system with the frequency of visual 40 Hz pulse trains.

[0019] Figure 7 is a diagram of an output device for delivering visual stimuli according to an example embodiment of the present disclosure.

[0020] Figure 8 is a block diagram of an example system using supervised learning according to an example implementation of the present disclosure.

[0021] Figure 9 is a block diagram of a simplified neural network model according to an example embodiment of the present disclosure.

[0022] Figure 10 is a block diagram of an example computer system according to an example implementation of the present disclosure.

[0023] Detailed description

[0024] Before turning to the drawings showing certain embodiments in detail, it should be understood that the present disclosure is not limited to the details or methods set forth in the description or shown in the drawings. It should also be understood that the terminology used herein is for descriptive purposes only and should not be regarded as limiting.

[0025] Neural oscillations can be characterized by their frequency, amplitude, and phase. These signal characteristics can be observed from neural recordings using time-frequency analysis. For example, EEG can measure oscillatory activity in a group of neurons, and the measured oscillatory activity can be classified into the following frequency bands: delta activity corresponds to the frequency band of 0.5 Hz-4 Hz; theta activity corresponds to the frequency band of 4 Hz-8 Hz; alpha activity corresponds to the frequency band of 8 Hz-13 Hz; beta activity corresponds to the frequency band of 13 Hz-30 Hz; and gamma activity corresponds to the frequency band of 30 Hz and above.

[0026] Neural oscillations in different frequency bands can be associated with cognitive states or functions, such as perception, action, attention, reward, learning, and memory. Depending on the cognitive state or function, neural oscillations in one or more frequency bands may be involved. Furthermore, neural oscillations in one or more frequency bands can have beneficial or adverse consequences for one or more cognitive states or functions.

[0027] Neural entrainment occurs when the brain perceives an external stimulus of a specific frequency or combination of frequencies and triggers neural activity in the brain, causing neurons to oscillate at a frequency related to the specific frequency of the external stimulus. Thus, neural entrainment can refer to the use of an external stimulus to synchronize neural oscillations in the brain so that the neural oscillations occur at a frequency corresponding to the specific frequency of the external stimulus. Neural entrainment can also refer to the use of an external stimulus to synchronize neural oscillations in the brain so that the neural oscillations occur at frequencies that are harmonics, subharmonics, integer ratios, and combinations of the specific frequencies of the external stimulus. Models of neural oscillations and models of neural entrainment predict specific neural oscillation frequencies that can be observed in response to a set of external stimulus frequencies.

[0028] Cognitive functions such as learning and memory involve coordinated activity across distributed subcortical and cortical brain regions, including the hippocampus, cortical and subcortical association areas, sensory areas, and the prefrontal cortex. Across different brain regions, behaviorally relevant information is encoded, maintained, and retrieved through transient increases in the power of neural oscillations reflecting multiple activity frequencies and synchronization between these neural oscillations.

[0029] In particular, oscillatory neural activity in the theta and gamma frequency bands has been associated with encoding, maintenance, and retrieval processes during short-term, working, and long-term memory. Evoked gamma activity has been linked to working memory, with increases in both scalp-recorded gamma band activity and intracranial gamma band activity during working-memory maintenance. Increases in gamma activity power dynamically track the number of items maintained in working memory. Using electrocorticography (ECoG), one study found that working memory load, tracked by gamma power, was enhanced in the hippocampus and medial temporal lobe when participants maintained sequences of letters or faces in working memory. Finally, other evidence suggests that hippocampal gamma activity contributes to episodic memory, using distinct sub-gamma frequency bands corresponding to the encoding and retrieval stages.

[0030] Theta oscillations (4Hz-8Hz) have been implicated in working memory and episodic memory processes. Intracranial electroencephalogram (iEEG) recordings have shown that during working memory, theta oscillations gate on and off (i.e., increase and maintain amplitude, then rapidly decrease in amplitude) with the encoding, maintenance, and retrieval phases. Other work has observed increases in scalp-recorded theta activity during working memory maintenance. Some studies have concluded that scalp-recorded theta activity from frontal midline electrodes is the most powerful neural correlate of verbal working memory maintenance. Furthermore, frontal midline theta activity tracks working memory load, increasing and maintaining power according to the number of items maintained in working memory.

[0031] Several studies have found that gamma-frequency auditory-visual stimulation can improve biomarkers and pathophysiology associated with dementia or Alzheimer's Disease (AD), and may provide neuroprotection if administered during the early stages of disease progression.

[0032] Music induces and drives neural activity across multiple frequency ranges, and musical stimulation itself can induce and drive oscillatory neural activity associated with learning, memory, and cognition. In various embodiments of the present solution, the systems and methods described herein can detect, determine, identify, or otherwise exploit the brain's natural delta, theta, and gamma frequency responses to music by providing music as the sole auditory stimulus in systems and methods for treating, preventing, prophylactic, or otherwise affecting Alzheimer's disease, dementia, and / or other neurological or cognitive conditions. In some embodiments, audio stimulation is coupled with visual stimulation in the delta, theta, and / or gamma frequency bands, which are programmed to synchronize with the delta, theta, and / or gamma frequency bands of the brain's response to the audio stimulation to enhance the therapeutic effect. In some embodiments, stimulation can be targeted for additional frequencies and frequency bands to treat, prevent, and / or prophylactically affect Alzheimer's disease, dementia, and / or other neurological or cognitive conditions or diseases, such as Parkinson's disease.

[0033] Musical rhythms are organized into well-structured frequency combinations. For example, musical rhythms induce neural activity in the delta and theta frequency ranges by directly stimulating the brain within these frequency ranges. The frequency of the basic beat can correspond to neural activity in the delta band. Subdivisions of the beat typically correspond to neural activity in the theta band. Furthermore, musical rhythms can drive activity at delta and theta frequencies that are not explicitly present in the rhythm because they contain structured frequency combinations. Frequencies observed in brain activity can include harmonics, subharmonics, integer ratios, and combinations of frequencies present in musical rhythms and are predicted by modeling neural oscillations and neural intra- and extra-intra-neural coupling.

[0034] Musical rhythms can drive gamma neural activity in the brain in a manner that is distinct from the internal and external coupling of delta and theta activity. The amplitude of endogenous gamma neural oscillations is modulated so that peak amplitudes are synchronized with musical events (see Figure 2 ). The amplitude modulation of gamma neural activity reflects phase-amplitude coupling with lower frequency (e.g., delta and theta) neural activity.

[0035] Phase-amplitude coupling (PAC) can be or include a statistical dependence between the amplitude of oscillations in one frequency band and the phase of oscillations in another frequency band. For example, in theta-gamma phase-amplitude coupling, the peak of the gamma amplitude corresponds to a specific phase of the resulting theta activity. Thus, the gamma activity is driven by the resulting theta and delta activity.

[0036] The systems and methods described herein can provide feedback-based audio and / or visual stimulation by activating the brain's natural delta, theta, and gamma responses to music in a manner that does not interfere with musical enjoyment. Because enjoyment is critical to patient tolerance and completion of the regimen, the systems and methods described herein can motivate patient compliance with the treatment by avoiding the harsh and unpleasant sounds of added audio waves in the gamma frequency band.

[0037] In some embodiments, the systems and methods described herein can combine, generate, or otherwise provide visual stimulation in the delta, theta, and / or gamma frequency bands to enhance frequencies important in musical enjoyment. Such a solution can enhance the efficacy of stimulation because visual stimulation in the gamma band is less aversive than auditory stimulation in the gamma band. In some embodiments, gamma stimulation can be combined with delta and theta stimulation to produce visual stimulation that mimics the brain's natural response to musical rhythms.

[0038] In the systems and methods described herein, gamma stimulation can be amplitude modulated by phase-amplitude coupling with theta and / or v frequency oscillations to simulate auditory processing, thereby increasing the efficacy and extent of neural stimulation. In addition, the specific stimulation frequency is determined by the musical stimulation, so the stimulation frequency provided by the present solution will change within the stimulation session, reducing the potential for neural adaptation, thereby increasing the efficacy of stimulation. In some embodiments, the systems and methods described herein can combine music listening with delta, theta and / or gamma frequency visual stimulation to create an engaging and effective audio-visual stimulation for the patient. In some embodiments, additional frequency bands can be employed through audio or visual stimulation.

[0039] In some embodiments, the systems and methods described herein can output a set of improved stimuli that amplify the brain's natural delta, theta, and gamma responses to music in a manner that does not create neural interference between the brain's natural oscillatory response to music and the oscillatory auditory stimulus added within the same frequency band. Specifically, in some embodiments, the systems and methods described herein can use simulations of neural intra- and extra-corporeal coupling to determine the frequencies of the brain's natural delta, theta, and gamma responses to music. The system can then enhance and amplify the natural response to music by delivering the same delta, theta, and / or gamma frequencies in visual stimuli. The simulation can include delta-theta-gamma phase-amplitude coupling to faithfully simulate the brain's auditory response and amplify the effect. Therefore, the visual stimulation may not interfere with or offset the brain's natural oscillatory response to music. Instead, the visual stimulation may amplify the brain's natural oscillatory response to music.

[0040] The systems and methods described herein relate to output stimulation that induces neural stimulation via rhythmic light stimulation presented simultaneously with musical stimulation. The combination of music and rhythmic light pulses can induce brain wave effects or stimulation. The combined stimulation can adjust, control or otherwise affect the frequency of neural oscillations to provide beneficial effects to one or more cognitive states, cognitive functions, immune systems or inflammation (or other symptoms), while mitigating or preventing adverse consequences to cognitive states or cognitive functions, and maximizing enjoyment, processing tolerance and completion of treatment plans. For example, the systems and methods of the present technology can treat, prevent, prevent or otherwise affect Alzheimer's disease (or other cognitive diseases or ailments).

[0041] The frequency of the neural oscillations observed in the patient can be affected by or correspond to the frequency of the musical rhythm and the rhythmic light pulses. Therefore, the system and method of the present solution can induce neural intra- and extra-corporeal coupling by outputting multimodal stimulation, such as a musical rhythm and light pulses emitted at a frequency determined by analyzing the musical rhythm. This combined multimodal stimulation can synchronize the electrical activity between groups of neurons based on one or more frequencies caused and driven by the musical rhythm. Based on the aggregate frequency of oscillations generated by the synchronized electrical activity in the neuronal collection throughout the brain, neural intra- and extra-corporeal coupling can be observed.

[0042] In some embodiments, additional outputs from the system may also include one or more stimulation units for generating tactile, vibration, thermal, and / or electrical transcutaneous stimulation. Such stimulation units may include mobile devices, smart watches, gloves, or other devices capable of vibration. In some embodiments, the output device may include a stimulation unit for generating an electromagnetic field or current to deliver transcranial stimulation, such as an array of electromagnets or electrodes.

[0043] refer to Figure 1 , depicts a diagram showing frequencies associated with a specific basic musical stimulus and the frequency range present in each frequency band selected by an oscillation selection module (OSM) according to an example embodiment of the present disclosure. Figure 1 As shown, the graph can include a breakdown of four frequencies associated with fundamental music that can be selected by the systems and methods described herein, as well as the frequency ranges in which they exist. In some embodiments, the systems and methods described herein can select one or more harmonically related frequencies in the delta, theta, and lower gamma (30 Hz-50 Hz) frequency ranges. In some embodiments, the gamma amplitude is modulated by the theta frequency, simulating theta-gamma phase-amplitude coupling. Similarly, in some embodiments, the theta amplitude is modulated by one or more delta frequencies, simulating delta-theta phase-amplitude coupling. Overall, the above thus simulates a delta-theta-gamma oscillation hierarchy in the auditory cortex.

[0044] Continue to refer Figure 1 , shows an exemplary scheme for visual stimulus frequencies in the gamma, theta, and delta frequency bands generated by a system according to an aspect of the present disclosure. Panel A shows the time domain waveform of the musical stimulus over a 4-beat time interval, as well as the onset calculated during preprocessing. Panel B shows the delta-theta-gamma coupled variations in luminance provided by the systems and methods described herein, while Panel C shows the same variations in each frequency band.

[0045] Figure 2 Shown are MEG recordings of the human auditory cortex while subjects listened to rhythms of two different tempos. Figure 2Panel A is a time-frequency plot of signal power changes associated with a rhythmic stimulus presented every 390 ms (2.6 Hz), showing a periodic pattern of signal increases and decreases in the gamma band. Panel B shows the same measurements for a rhythmic stimulus presented every 585 ms (1.7 Hz). In the auditory cortex, gamma is amplitude-modulated by delta and theta, and this pattern is simulated by the systems and methods described herein.

[0046] Figure 1 Panel D shows the stimulation generated by the systems and methods described herein in the frequency domain. In summary, these figures illustrate that gamma oscillations are effectively stimulated by the output provided by the device within a frequency range surrounding the primary frequency. These additional frequencies, known as sidebands, are caused by the amplitude modulation of the theta and delta frequencies by the device and method. Furthermore, each song played by the systems and methods described herein results in a different selection of frequencies within the delta, theta, and gamma ranges. Thus, over the course of several songs played via the systems and methods described herein, the output stimulates a wide range of gamma frequencies.

[0047] Thus, the device simulates the amplitude modulation of stimulation provided in the gamma band by altering the phase of stimulation provided in the delta and theta bands, which mimics the brain's natural gamma-delta-theta phase-amplitude coupling response, thereby enhancing the tolerability and efficacy of the treatment. As described above, Figure 1 Panel D shows that gamma oscillations are efficiently excited in a range of frequencies (sidebands) around the main frequency. These sidebands are caused by the amplitude modulation of the theta and delta frequencies provided by the systems and methods described herein.

[0048] Furthermore, each piece of music played by the system can result in a different selection of frequencies within the delta, theta, and gamma ranges. Thus, over the course of a session, different gamma frequencies are stimulated. In contrast, some solutions may stimulate only a single frequency, often resulting in neural adaptation and reduced neural response. In some embodiments of the present system, varying the frequency can avoid neural adaptation and promote robust neural responses.

[0049] Now refer to Figure 3 and Figure 4 , depicts a block diagram of a system 300 for providing neural stimulation, and diagrams illustrating the operation of the system 300 utilizing the resulting brain stimulation, according to an example embodiment of the present disclosure. The system 300 may include an auditory analysis system (AAS) 302 configured to receive auditory input, filter acoustic signals, detect the onset of acoustic events (e.g., a musical note or drum beat), and adjust the gain of the resulting signal. In some embodiments, the AAS 302 may include a filtering module, an onset detection module, and an optional gain control module to filter the signal, detect the onset of acoustic events, and adjust the gain of the resulting signal, respectively.

[0050] In some embodiments, the AAS 302 can be configured to pre-process auditory stimuli, auditory input, or audio signal 304 to provide multi-channel rhythmic input (e.g., note onsets). In some embodiments, the auditory input or audio signal 304 is provided by the system, such as by or via a built-in audio playback system that can access a library of songs and / or other musical works. In some embodiments, the system 300 may also include a graphical display and input / output accessible to the user (e.g., patient or therapist) to allow the user to select from the library for playback. In other embodiments, in addition to or as an alternative to the built-in audio playback system, the system 300 may include an auxiliary audio input to allow the system 300 to receive input from a secondary playback system, such as a personal music playback device (e.g., iPod, MP3 player, smartphone, etc.). In some embodiments, in addition to or as an alternative to the above-mentioned auditory input, the system 300 may include a microphone or similar device to allow the system 300 to receive auditory input from ambient sounds, such as live musical performances or music broadcast from auxiliary speakers (such as the user's home stereo system). In embodiments where the system receives audio signal 304 through a built-in playback system or an auxiliary input (eg, through an MP3 player), the system may also include headphones or integrated speakers to allow the listener to hear audio signal 304 in real time.

[0051] System 300 may include a profile manager 306. Profile manager 306 may be or include a processor or internet-enabled software application that accesses non-transitory and / or random access memory that stores data related to one or more users or patients, such as identification information (e.g., name or patient ID number), stored information from previous treatments and / or a library of audio files, and various user preferences, such as song selections. Profile manager 306 may be communicatively coupled with AAS 302 to facilitate selecting, managing, or otherwise controlling auditory input or audio signals.

[0052] In some embodiments, profile manager 306 can provide a user interface to prompt the user to select his or her own personalized music preference as auditory stimulation. Such an embodiment can maximize the effectiveness of a given system by stimulating the auditory and reward systems of patients in the early stages of dementia and cognitive decline. The user interface can receive audio content history or preference input from a user or an associated service (e.g., a third-party streaming music service). For example, the user interface can include an interface for receiving music parameters such as genres, styles, date ranges, tempo, mode, brightness or musical instruments of music or other audio content. The user interface can include an application program interface (API) to connect with an associated service interface, such as accessing a list of songs or other audio content saved by the user, or a history of audio content listened to by the user, which can include the date or time of listening. The API can receive the interaction of the user with the audio content. For example, user interaction can include searching for audio content, skipping audio content, repeated audio content, preserving audio content or the audio content of ratings (e.g., giving a thumbs up). The user can input preferred music parameters. For example, the user interface can present the selection of music parameters and receive a response from the user.

[0053] The user interface can compare selected audio content items with the library of the audio content items enhanced, and the audio content items enhanced are such as the audio content items edited from the original form, to cause the reaction of enhancement at the interested frequency or position in the user's brain.The user interface can replace the audio content items of suggestion with the enhanced version of the audio content items, or the audio content items enhanced are transferred to the recommendation engine for exclusive recommendation, or improve weight by the recommendation engine.For example, the recommendation engine can recommend songs to maximize the total therapeutic effect of presenting or recommending to the user's multiple audio content items, and this can relate to the combination of enhancing content (for example, to increase therapeutic effect) and non-enhanced content (for example, to increase diversity or user affinity, this can prolong total session time and therefore increase total therapeutic effect).

[0054] The recommendation engine may also receive information about audio output devices. For example, headphones, speakers, subwoofers, or other audio output devices may include a low frequency response depending on the device type or model. Each audio output device (or characteristics thereof, such as a frequency response at a frequency of interest, including harmonics of another frequency of interest) may be input to a user interface so that recommendations may be made based on the audio output device. For example, the recommendation engine may recommend audio content with 26 Hz content in response to an audio output device having a frequency response greater than a 26 Hz threshold, and recommend audio content with 52 Hz content in response to an audio output device having a frequency response less than a 26 Hz threshold. The recommendation engine may be, include, or interface with one or more machine learning models, some of which reference Figure 8 and Figure 9 Further description.

[0055] The user interface can receive the indication of social peers. For example, the user interface can be connected to a social service (for example, a third-party social network) via user entry or API. The user interface can receive the indication of name, attribute, music preference or music preference via API. For example, the user interface can receive the age, region, association with one or more bands or music parameters or other demographic information about the user's peer group or the user. The user's peer group can include family, friends and other related personnel. The profile manager 306 can be configured to maintain the peer group and assign weights to each peer of the peer group according to distance, family relationship, communication frequency or content. One or more peers (for example, authorized peers, such as family members with access credentials or physical access to the device associated with the user interface) can recommend audio content or adjust the weight of audio content. The weight can be a function of the audio content, the user, the user's cognitive status, the user's cognitive function state or the location of interest of the brain. The profile manager 306 can recommend audio content based on the weight of the audio content and / or the peers providing the suggested audio content.

[0056] The user interface can receive information associated with one or more cognitive peers. For example, the user interface can receive information of various additional users (e.g., patients) for use by the present invention (e.g., Figure 8 、 Figure 9 or Figure 10) is processed by the systems and methods described in the foregoing to determine a peer group for comparison with the user. The information may include any medical information, such as neurological status, heart rate, blood work results, body temperature, blood oxygenation, etc. The user interface may receive (e.g., from the AAS 302) effectiveness information (e.g., treatment effects, such as gamma or theta brain wave activity observed in one or more locations of interest in the brain from the BOM 316) for various audio content. This information may be based on cognitive peer groups, or based on user-specific feedback (e.g., feedback from the BOM 316), or the rate of progression of cognitive function. The user interface may present information to the user. For example, various audio content may be presented together with effectiveness scores (e.g., weights) for patient selection.

[0057] System 300 may include an internal and external coupling simulator 308. ES 308 may receive and process (e.g., from AAS 302) received audio signals to simulate the processing in the human brain. ES 308 may simulate the processing of audio signals to suggest and output oscillation signals to enhance the received audio signals, thereby enhancing the therapeutic effect of the processing. In some embodiments, AAS 302 may be operably connected to ES 308 and provide data to ES 308 in the form of a start signal. In some embodiments, ES 308 may also interface with profile manager 306 to, for example, call patient data from a previous treatment. In some embodiments, ES 308 may simulate the frequency, phase, and amplitude of neural oscillations resulting from the neural response to music to predict humankind.

[0058] ES 308 may include one or more oscillatory neural networks designed to simulate neural intra- and extra-corporeal coupling. In an embodiment, the artificial oscillatory neural network receives pre-processed auditory stimuli (music) and causes simulated neural oscillations to predict the frequency, phase, and relative amplitude of human neural responses to music. In some embodiments, ES 308 may include a deep neural network, an oscillator network, a set of numerical formulas, an algorithm, or any other component configured to simulate an oscillatory neural network. ES 308 may be configured to predict the frequency, phase, and relative amplitude of oscillations caused and driven by any given musical stimulus in a typical human brain. ES 308 may be configured to predict responses in at least the δ (1Hz-4Hz), θ (4Hz-8Hz), and low γ (30Hz-50Hz) frequency bands.

[0059] The system 300 may include an oscillation selection module (OSM) 310. The OSM 310 may be communicatively coupled to the ES 308. The OSM 310 may receive input from the ES 308 and output one or more selected oscillation states as frequencies, amplitudes, and phases for visual stimulation. The OSM 310 may be configured to select the most significant oscillations for visual stimulation in one or more predetermined frequency ranges (in a preferred embodiment, the delta, theta, and gamma bands). In some embodiments, the OSM 310 may couple visual gamma frequency stimulation to the beat and rhythmic structure of the music through phase-amplitude coupling. The OSM 310 may select variable, music-based frequencies within the delta, theta, and gamma ranges for visual stimulation of the user, which stimulation is generated by a brain rhythm stimulator, as described below.

[0060] System 300 may include a brain rhythm stimulator (BRS) 312. BRS 312 may be configured to generate, produce, or otherwise provide a control signal for an output device 314 based on data from OSM 310, ES 308, and / or AAS 302 to provide audio and / or visual stimulation. BRS 312 may be configured to synchronize visual stimulation within a selected frequency range with the rhythm of music via output device 314 (such as an LED light ring) using simulated neural oscillations, as described below. In some embodiments, BRS 312 may output rhythmic visual stimulation to the user. BRS 312 may include a pattern buffer, a generation module, an adjustment module, and a filtering component, and may be operatively connected to an output device 314 comprising an apparatus for displaying rhythmic light stimulation. BRS 312 may also be interfaced with a profile manager 306 that stores data related to one or more users or patients. Therefore, in some embodiments, the information stored by profile manager 306 may also include preferences for previously captured or user-selected patterns, waveforms, or other stimulation parameters, such as user / patient preferred colors. For example, the profile manager may receive information from the user interface related to the user's interaction with the audio content. For example, the profile manager may receive a record of the user selecting, skipping, repeating, saving, or liking content, or the total listening time of the audio content. The profile manager may receive or store any personalized user preference input received by the user interface, indications of social peers, cognitive peers, etc. For example, Figure 8 Any data in the data repositories mentioned in may be stored or otherwise accessed by the profile manager.

[0061] Output device 314 may include an LED light, a computer monitor, a TV monitor, goggles, a virtual reality headset, augmented reality glasses, smart glasses, or other suitable stimulation output devices. In some embodiments, output device 314 may be a stimulation unit for generating tactile, vibrational, thermal, and / or electrical transcutaneous stimulation, such as a stimulation unit in a wearable device, smartwatch, or mobile device. In some embodiments, output device 314 may include a stimulation unit for generating an electromagnetic field or current to deliver transcranial stimulation, such as an array of electromagnets or electrodes.

[0062] In general, the BRS can be configured to (1) read a patient's profile from a profile manager, (2) select a pattern based on the profile, (3) retrieve one or more selected oscillatory signals and / or states from the ES / OSM, (4) generate a pattern, (5) adjust the pattern based on the profile, and (6) display or output the rhythmic stimulus on an output device. In some embodiments, the pattern refers to a light pattern and the output device refers to a visual output device.

[0063] System 300 may include a brain oscillation monitor (BOM) 316. BOM 316 can provide neurofeedback, which can be used to optimize the frequency, amplitude and phase of the oscillation of the visual presentation, so as to optimize the frequency, phase and amplitude of the oscillation in the brain. In some embodiments, BOM 316 can provide feedback to system 300 (e.g., to ES 308) so that ES 308 can adjust parameters to optimize the phase of the output oscillation signal. BOM 316 can include electrodes, magnetometers or other components, signal amplifiers, filtering components and feedback interface components arranged to sense brain activity, and be connected to these component interfaces or otherwise communicate. In some embodiments, BOM 316 can provide feedback to ES 308 in the form of EEG signals. BOM 316 can be configured to identify the frequency, phase and amplitude of the brain oscillation caused by stimulation. BOM 316 can be configured to sense the electric field or magnetic field in the brain, amplify brain signals, filter the signals to identify specific neural frequencies, and provide input to ES 308 as described above. BOM 316 can be configured to sense electric or magnetic fields in the brain and can include electrodes connected to an electroencephalogram (EEG), intracranial EEG (iEEG) (also known as electrocorticoencephalogram (ECoG), magnetoencephalogram (MEG)), and other systems for sensing electric or magnetic fields.

[0064] AAS 302, profile manager 306, ES 308, OSM 310, BRS 312, and BOM 316 may each be or include any hardware, including a processor, circuitry, or any other processing component, including those referenced below. Figure 10 any hardware or components described.

[0065] In general, the system 300 can be configured to (1) receive auditory input, (2) simulate neural intra- and extra-corporeal coupling of a pre-processed auditory signal using one or more extra- and extra-corporeal coupling simulators 308, which can include a network of multi-frequency artificial neural oscillators, (3) couple oscillations within the network using phase-amplitude or phase-phase coupling, (4) adjust coupling parameters and / or intrinsic parameters using an adaptive learning algorithm, and / or (5) select the most significant oscillations in one or more frequency bands to display as visual stimuli via a BRS 312, as described below.

[0066] In various embodiments, the rhythmic visual stimulation (described below) selected for output to the user can include delta, theta and / or gamma frequencies, and theta-gamma and / or delta-gamma phase-amplitude coupling to enhance the naturally occurring oscillatory response to the musical rhythm. The sensory cortex in the brain (e.g., primary visual cortex and primary auditory cortex) is functionally connected to areas important for learning and memory, such as the hippocampus and the medial and lateral prefrontal cortexes. Therefore, coupling complex rhythmic visual stimulation (including delta, theta and gamma frequency visual stimulation) with musical rhythm can drive theta, gamma and theta-gamma coupling in the brain, activating the neural circuits involved in learning, memory and cognition. This, in turn, can drive the learning and memory circuits involved in music.

[0067] Now refer to Figure 5 and Figure 6 , depicts a diagram illustrating example stimuli using different songs and visual stimuli according to an example embodiment of the present disclosure. Specifically, Figure 5 and Figure 6 Shown is a comparison of auditory and visual stimulation provided by the systems and methods described herein compared to a 40 Hz pulse train. Figure 5 and Figure 6 Different frequencies of both the audio and visual stimulation and the 40 Hz pulse trains provided by the systems and methods of the present disclosure are shown. Figure 5 and Figure 6 Each shows stimulation provided by a different song.As can be seen, the 40 Hz pulse train provides both audio and visual stimulation at a single frequency, which can be easily compared to the wide frequency range at which the systems and methods described herein provide audio and visual stimulation.

[0068] Now refer to Figure 7, depicts an example of an output device 314 for providing visual stimulation. Output device 314 is provided via a visual stimulation ring 700, which includes an LED light 702 operably connected to system 300 including BRS 312. In some embodiments, visual stimulation ring 700 is positioned in front of a participant, who is instructed to focus at the center indicated by reference symbol 701. In some embodiments, visual stimulation ring 700 is positioned at an appropriate distance to stimulate the retina at a specific viewing angle. For example, ring 700 can be positioned at an appropriate distance to stimulate the retina at a viewing angle between 0 and 15 degrees, or between 10 and 60 degrees, or between 15 and 50 degrees, or between 15 and 25 degrees, or between 18 and 22 degrees, or between 19 and 21 degrees. In some embodiments, visual stimulation ring 700 can be positioned at an appropriate distance to stimulate the retina at a viewing angle of 20 degrees, at which the maximum density of rods in the retina is found.

[0069] Although shown as a stimulation ring 700, various other output devices 314 may be used as part of the system 300, in conjunction with or in addition to the stimulation ring 700. For example, in some embodiments, the output device 314 may include a head-mounted device. The head-mounted device may include a display and / or one or more speakers of a speaker system. The head-mounted device may include augmented reality glasses, virtual reality goggles, etc. The display of the head-mounted device may present visual patterns to the user. For example, if the head-mounted device includes augmented reality glasses, the augmented reality glasses may utilize visual patterns to enhance the user's environment visible through the glasses. As another example, if the head-mounted device includes virtual reality goggles (or other non-AR goggles), the goggles may display visual patterns on a display adjacent to the patient's eyes. In some embodiments, the display of the head-mounted device may display separate visual patterns on each of the patient's eyes and at different angles to provide visual stimulation to the patient. The one or more speakers may include in-ear speakers or earbuds for each ear of the patient, headphones, a speaker system (e.g., locally on the head-mounted device), etc. The one or more speakers may be configured to present the audio signal 304 to provide audio stimulation to the patient.

[0070] In some embodiments, the output device 314 may include multiple output devices 314. For example, the output device 314 may include an audio output device 314 and a visual output device 314. The audio output device 314 may be configured to receive a control signal from the BRS 312 for presenting the audio signal 304 as audio stimulation to the patient. Similarly, the visual output device 314 may be configured to receive a control signal from the BRS 312 for presenting a visual pattern as visual stimulation to the patient. The audio output device 314 may be or include headphones, earbuds, a speaker system, etc. The visual output device 314 may include a stimulation ring 700, a display device (e.g., a television, tablet computer, smartphone, or other display), a head-mounted device including a display, etc.

[0071] Therefore, in a method according to one embodiment of the present solution, the system may perform the following process:

[0072] (A) receiving auditory input,

[0073] (B) Filtering the acoustic signal,

[0074] (C) detecting the onset of an acoustic event,

[0075] (D) using one or more multi-frequency neural oscillator networks to simulate neural intra- and extra-corporeal coupling to pre-processed auditory signals,

[0076] (E) using phase-amplitude or phase-phase coupling to couple oscillations within the network,

[0077] (F) using an adaptive learning algorithm to adjust coupling parameters and / or intrinsic parameters,

[0078] (G) selecting the most significant oscillations in the delta, theta and / or gamma frequency bands for display,

[0079] (H) generating a light pattern, and

[0080] (I) Displaying rhythmic light on a visual output device.

[0081] In some embodiments, prior to receiving audio input, the system may perform a process of prompting the user to select an audio input source and / or select from a library of songs or musical works stored on the system.

[0082] In brain regions including the hippocampus, auditory cortex, and frontal lobe areas important for long-term memory, self-selected music, i.e., music chosen by the individual patient and familiar to him / her, can be more effective in engaging larger brain activity networks than music chosen by others or music unfamiliar to the patient. Therefore, listening to familiar music may be more effective in driving brain activity in older adults, and it activates more brain regions. Importantly, familiar music can drive greater activation of the hippocampus, a key area for memory.

[0083] Compared to music selected by researchers, music selected by listeners may be more pleasing and familiar to the listener and may more effectively activate brain activity. Specifically, in addition to activating the auditory system, self-selected music can increase activity in the dopamine reward system, the default mode network, and predictive brain processes. Prolonged music listening may also increase functional connectivity from the sensory cortex to the dopamine reward system, which is responsible for various motivated behaviors.

[0084] Thus, in some embodiments, auditory stimulation can include music selected by the patient themselves, which has the practical effect of maximizing overall brain function. The systems and methods described herein can facilitate receiving music recordings from a patient while the patient simultaneously views an engaging audiovisual display including delta, theta, and gamma frequency stimulation, further improving patient compliance with the disclosed treatment regimen.

[0085] In some embodiments, prior to the process of generating and displaying the light pattern, the system 300 may prompt the user to select a profile from an input device and / or user interface integrated in or coupled to the system 300. The system 300 may perform one or more of the following processes: (G2) reading the patient's profile from the profile manager 306, (G3) selecting a light pattern based on the profile, (G4) retrieving one or more oscillation signals from the ES 308, (H) generating the light pattern, and (H2) adjusting the light pattern based on the profile.

[0086] In some embodiments, the system 300 may also optimize the frequency, phase, and / or amplitude of the output oscillating signal based on data received from the BOM 316. Thus, the system 300 may intermittently or continuously perform one or more of the following additional processes: (J) receiving input from the BOM 316, (K) providing input to the ES 308, (L) coupling the input via phase-to-phase coupling, and (M) using an adaptive learning algorithm to adjust coupling parameters and / or intrinsic parameters to optimize the frequency, phase, and amplitude of the output oscillating signal.

[0087] Thus, the systems and methods of the present solution may provide neural stimulation to a user via at least the presentation of rhythmic visual stimulation simultaneously, synchronously, and in coordination with musical stimulation.

[0088] For example, in some embodiments, the system 300 can generate and display light patterns to be displayed simultaneously with the musical stimulus based on system self-selection or based on profile data stored for an individual user. In some embodiments, the system 300 can perform one or more of the following additional processes:

[0089] (A) selecting one or more oscillations in the delta, theta and / or gamma frequency bands,

[0090] (B) generating a light pattern using the selected one or more oscillations, and

[0091] (C) Displaying the light pattern on a visual output device 314 .

[0092] The system 300 may also consult the user's profile and select a light pattern based on the profile. The system 300 may first prompt the user to select a profile from an input device and / or user interface integrated into or coupled to the system 300 and read the patient's profile from the profile manager 306 to determine the appropriate light pattern to display.

[0093] As described herein, in some embodiments, the AAS 302 may receive auditory input via a microphone or auxiliary audio input, filter the acoustic signal, detect the onset of an acoustic event (eg, a musical note or drum beat), and adjust the gain of the resulting signal.

[0094] As described herein, in some embodiments, the ES 308 can receive auditory input from the AAS 302, use the input to simulate neural intra- and extra-neural coupling of pre-processed auditory signals using one or more multi-frequency neural oscillator networks, couple oscillations within the network using phase-amplitude or phase-phase coupling, adjust coupling parameters and / or intrinsic parameters using an adaptive learning algorithm, and select oscillations within a predetermined frequency range for display based on the retrieved profile. The ES 308 can also receive input from the BOM 316, provide input to the one or more multi-frequency neural networks, couple the neural inputs via phase-phase coupling, and adjust the coupling parameters using an adaptive learning algorithm to optimize the amplitude and phase of the output oscillatory signal.

[0095] As described herein, in some embodiments, the BRS 312 can read the patient's profile from the profile manager 306, select a light pattern based on the profile, read one or more oscillating signals from the ES 308, select at least one of a delta frequency, a theta frequency, a gamma frequency, and / or a combination of these frequencies, whose frequency, amplitude, and phase are determined by the ES 308, generate a rhythmic light pattern based on the selected frequencies, adjust the light pattern based on the profile, and display the rhythmic visual stimulation directed toward the eye on an LED, computer monitor, TV monitor, or other suitable light output device.

[0096] The results of the systems and methods described herein can be that the system senses electric or magnetic fields in the brain, amplifies brain signals, and filters the signals to identify specific neural frequencies. In some embodiments, the system then collects output from the user's brain based on the brain's reception of visual and auditory stimuli and returns this feedback to the ES 308 to further optimize the visual and auditory stimuli.

[0097] The system and method can induce and drive oscillatory neural activity involved in learning, memory and cognition. By providing music as the only auditory stimulus, coupled with visual stimulation in the delta, theta and / or gamma frequency bands, the present system and method can be used as a method for treating, preventing, prophylactic or otherwise affecting Alzheimer's disease and dementia.

[0098] refer to Figure 8 , shows a block diagram of an example system using supervised learning. Supervised learning is a method for training a machine learning model given input-output pairs. An input-output pair is an input and an associated known output (e.g., an expected output).

[0099] The machine learning model 804 can be trained on known input-output pairs so that the machine learning model 804 can learn how to predict known outputs given known inputs. Once the machine learning model 804 has learned how to predict known input-output pairs, the machine learning model 804 can operate on unknown inputs to predict outputs.

[0100] The machine learning model 804 can be trained based on general data and / or granular data (e.g., based on data for a specific user 832), such that the machine learning model 804 can be trained for a specific user 832, neurological condition, cognitive function range, age, etc.

[0101] Training inputs 802 and actual outputs 810 may be provided to a machine learning model 804. The training inputs 802 may include effectiveness scores or weights for the audio content, times or frequencies at which the user 832 listened to previous instances of the audio content, therapeutic effects achieved for members of the user's 832 peer group in response to one or more instances of the audio content, total listening time or number of audio content items listened to, and the like.

[0102] Input 802 and actual output 810 can be received from a user interface (e.g., from a user input or via an API from a related service) or a data repository. The data repository may include one or more local or distributed databases and may include a database management system. The data repository may include an interface to one or more services to access various information. For example, the data repository may access music or social services via an API to access audio content, audio content history, or its attributes. The data repository may include a computer data storage device or memory and may store one or more data elements of audio content, audio content history, peer group data, or cognitive data.

[0103] The audio content may include music or other audio files, links, or signals. The data repository may export, access, or store data associated with the audio content, such as frequency content associated with gamma or theta brainwave activity. The associated data may include correlations with treatment responses (such as brain activity), for example, for a general population, a population specific to a neurological disorder (e.g., Alzheimer's disease, Parkinson's disease, etc.), or associated with a level of cognitive function. The audio content history may include the listening history of the user 832, such as media files saved to a device or accessed through a media service. Peer group data may include social peers of the user 832 based on connected electronic tags, or cognitive peers based on neurological conditions or cognitive functions. Cognitive data may include cognitive functions of the user 832, the degree of progression of a neurological condition, or cognitive abilities. Thus, the machine learning model 804 may be trained based on the training inputs 802 and actual outputs 810 used to train the machine learning model 804 to predict the therapeutic effects of user preferences, listening time, or listening sessions (e.g., the sum or other combination of therapeutic effects based on individual content items).

[0104] The system 300 may include one or more machine learning models 804. In an embodiment, a first machine learning model 804 may be trained to predict data associated with a user's preferences for audio content or a cognitive peer group of a user 832. For example, by applying the current state of the first machine learning model 804 to a training input 802, the first machine learning model 804 may use the training input 802 of cognitive abilities, listening history, user interactions, medical information, or a peer social group to predict an output 806 of a music parameter preferred by the user 832 or a cognitive peer group of the user 832. A comparator 808 may compare the predicted output 806 with an actual output 810, such as listening time or expressed affinity (e.g., received by a user interface), or progress in cognitive abilities relative to a peer group, to determine an amount of error or difference. For example, a predicted music parameter (e.g., predicted output 806) may be compared with an actual expressed affinity (e.g., actual output 810).

[0105] In other embodiments, the second machine learning model 804 can be trained to make one or more recommendations to the user 832 based on the predicted output from the first machine learning model 804. For example, the second machine learning model 804 can use the training input 802 of music parameters preferred by the user 832 or a cognitive peer group of the user 832 to predict an output 806 of the total treatment effect achieved by the patient or the total listening time of the patient by applying the current state of the second machine learning model 804 to the training input 802. The comparator 808 can compare the predicted output 806 with the actual output 810 (such as the actual listening time / amount of content items, the similarity or cognitive progress of the user 832 relative to the predicted cognitive peer group, or the response to a stimulus relative to the cognitive peer group) to determine the amount of error or difference.

[0106] Actual output 810 can be determined based on the historical data of the recommendation made to user 832. In an illustrative, non-limiting example, the audio content recommended to user 832 can be listened to in full (or more than once), partially listened to (such as 50%) or rejected. User 832 can also express affinity via a user interface (e.g., via a weighted rating, such as a 1-5 scale or a like / dislike option). Listening time or expressed affinity (alone or in combination) can be used to predict the listening time or expressed affinity of future suggestions. The listening time of a conversation comprising various audio content items and the predicted (or measured) therapeutic effect of each audio content item can be combined to generate a score for a total therapeutic effect. Machine learning model 804 can be trained to maximize total therapeutic effect (e.g., as an objective function).

[0107] In some embodiments, a single machine learning model 804 can be trained to make one or more recommendations to a user 832 based on current user 832 data received from a user interface. That is, a single machine learning model can be trained using the training input of the first machine learning model to predict the output 806 of the second machine learning model by applying the current state of the machine learning model 804 to the training input 802. A comparator 808 can compare the predicted output 806 to the actual output 810 to determine the amount of error or difference. The actual output 810 can be determined based on historical data associated with recommendations to the user 832.

[0108] During training, the error determined by the comparator 808 (represented by the error signal 812) can be used to adjust the weights in the machine learning model 804 so that the machine learning model 804 changes (or learns) over time. For example, the machine learning model 804 can be trained using a backpropagation algorithm. The backpropagation algorithm operates by propagating the error signal 812. The error signal 812 can be calculated at each iteration (e.g., each pair of training inputs 802 and associated actual outputs 810), batch, and / or round, and propagated through the algorithm weights in the machine learning model 804 so that the algorithm weights are adapted based on the amount of error. The error is minimized using a loss function. Non-limiting examples of loss functions can include a squared error function, a root mean square error function, and / or a cross entropy error function.

[0109] The weighting coefficients of the machine learning model 804 can be adjusted to reduce the amount of error, thereby minimizing the difference between the predicted output 806 and the actual output 810 (or otherwise converging). The machine learning model 804 can be trained until the error determined at the comparator 808 is within a certain threshold (or a threshold number of batches, rounds, or iterations has been reached). The trained machine learning model 804 and associated weighting coefficients can then be stored in a memory 816 or other data repository (e.g., a database) so that the machine learning model 804 can be used for unknown data (e.g., not the training input 802). Once trained and validated, the machine learning model 804 can be employed during testing (or inference phase). During testing, the machine learning model 804 can ingest unknown data to predict future data (e.g., total listening time, total treatment effect, expressed affinity, peer cognitive group, etc.).

[0110] refer to Figure 9 , shows a block diagram of a simplified neural network model 900. The neural network model 900 may include a stack of different layers (oriented vertically) that transform a variable number of inputs 902 taken by an input layer 904 into an output 906 at an output layer 908.

[0111] The neural network model 900 may include multiple hidden layers 910 between the input layer 904 and the output layer 908. Each hidden layer has a corresponding number of nodes (212, 914, and 916). In the neural network model 900, the first hidden layer 910-1 has nodes 912, and the second hidden layer 910-2 has nodes 914. Nodes 912 and 914 perform specific calculations and are interconnected to nodes in adjacent layers (e.g., node 912 in the first hidden layer 910-1 is connected to node 914 in the second hidden layer 910-2, and node 914 in the second hidden layer 910-2 is connected to node 916 in the output layer 908). Each node (212, 914, and 916) sums the values ​​from adjacent nodes and applies an activation function, thereby allowing the neural network model 900 to detect nonlinear patterns in the input 902. Each node (212, 914, and 916) is interconnected by weights 920-1, 920-2, 920-3, 920-4, 920-5, 920-6 (collectively, weights 920). Weights 920 are adjusted during training to adjust the strength of the nodes. Adjusting the strength of the nodes contributes to the ability of the neural network to predict accurate outputs 906.

[0112] In some embodiments, output 906 can be one or more numbers. For example, output 906 can be a real number vector that is subsequently classified by any classifier. In one example, a real number can be input into a softmax classifier. The softmax classifier uses a softmax function or a normalized exponential function to convert a real number input into a normalized probability distribution on a predicted output category. For example, a softmax classifier can indicate the probability that the output is in category A, B, C, etc. Therefore, due to the ability of the classifier to classify various categories, this softmax classifier can be adopted. Other classifiers can be used to perform other classifications. For example, a sigmoid function makes a binary determination about the classification of a category (that is, label A can be used to classify the output, or label A can not be used to classify the output).

[0113] Figure 10An example block diagram of an example computer system 1000 is depicted. Computer system or computing device 1000 may include or be used to implement a data processing system or components thereof. Computing system 1000 includes at least one bus 1005 or other communication component for communicating information, and at least one processor 1010 or processing circuitry coupled to bus 1005 for processing information. Computing system 1000 may also include one or more processors 1010 or processing circuitry coupled to bus 1005 for processing information. Computing system 1000 also includes at least one main memory 1015, such as a random access memory (RAM) or other dynamic storage device, coupled to bus 1005 for storing information and instructions to be executed by processor 1010. Main memory 1015 may be used to store information during execution of instructions by processor 1010. Computing system 1000 may also include at least one read-only memory (ROM) 1020 or other static storage device coupled to bus 1005 for storing static information and instructions for processor 1010. A storage device 1025 , such as a solid-state device, magnetic disk, or optical disk, may be coupled to bus 1005 for persistently storing information and instructions.

[0114] The computing system 1000 may be coupled to a display 1035, such as a liquid crystal display or an active matrix display, via the bus 1005 for displaying information to a user. An input device 1030, such as a keyboard or a voice interface, may be coupled to the bus 1005 for communicating information and commands to the processor 1010. The input device 1030 may include a touch screen display 1035. The input device 1030 may also include a cursor control, such as a mouse, a trackball, or cursor direction keys, for communicating direction information and command selections to the processor 1010 and for controlling cursor movement on the display 1035.

[0115] The processes, systems, and methods described herein can be implemented by the computing system 1000 in response to the processor 1010 executing an arrangement of instructions contained in the main memory 1015. Such instructions can be read into the main memory 1015 from another computer-readable medium, such as the storage device 1025. Execution of the arrangement of instructions contained in the main memory 1015 causes the computing system 1000 to perform the illustrative processes described herein. One or more processors in a multi-processing arrangement can also be used to execute the instructions contained in the main memory 1015. Hard-wired circuitry can be used with the systems and methods described herein in place of or in combination with software instructions. The systems and methods described herein are not limited to any specific combination of hardware circuitry and software.

[0116] Although already Figure 10An example computing system is described in the specification, but the subject matter, including the operations described in this specification, may be implemented in other types of digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in a combination of one or more of them.

[0117] Now that some illustrative implementations have been described, it will be apparent that the foregoing implementations are illustrative rather than restrictive and have been presented by way of example. Specifically, although many of the examples presented herein involve specific combinations of method actions or system elements, those actions and those elements can be combined in other ways to achieve the same objectives. Actions, elements, and features discussed in conjunction with one implementation are not intended to be excluded from similar roles in other implementations or implementations.

[0118] The hardware and data processing components for implementing the various processes, operations, illustrative logic, logic blocks, modules and circuits described in conjunction with the embodiments disclosed herein can be implemented or executed using a general-purpose single-chip or multi-chip processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components or any combination thereof designed to perform the functions described herein. A general-purpose processor can be a microprocessor, or any conventional processor, controller, microcontroller or state machine. A processor can also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors combined with a DSP core, or any other such configuration. In some embodiments, specific processes and methods can be performed by circuits specific to a given function. Memory (e.g., memory, memory unit, storage device, etc.) can include one or more devices (e.g., RAM, ROM, flash memory, hard disk storage device, etc.) for storing data and / or computer code to complete or facilitate the various processes, layers and modules described in this disclosure. The memory may be or may include volatile memory or non-volatile memory and may include database components, object code components, script components, or any other type of information structure for supporting the various activities and information structures described in this disclosure. According to an exemplary embodiment, the memory is communicatively connected to the processor via the processing circuitry and includes computer code for executing (e.g., by the processing circuitry and / or the processor) one or more processes described herein.

[0119] The present disclosure contemplates methods, systems, and program products on any machine-readable medium for completing each operation. Embodiments of the present disclosure can be implemented using existing computer processors, or by a dedicated computer processor of an appropriate system introduced for this purpose or another purpose, or by a hard-wired system. Embodiments within the scope of the present disclosure include program products that include a machine-readable medium for carrying or having machine-executable instructions or data structures stored thereon. Such machine-readable media can be any available medium that can be accessed by a general-purpose or special-purpose computer or other machine with a processor. For example, such machine-readable media can include RAM, ROM, EPROM, EEPROM, or other optical disk storage devices, magnetic disk storage devices, or other magnetic storage devices, or any other medium that can be used to carry or store desired program code in the form of machine-executable instructions or data structures and can be accessed by a general-purpose or special-purpose computer or other machine with a processor. The above combinations are also included within the scope of machine-readable media. Machine-executable instructions include, for example, instructions and data that cause a general-purpose computer, a special-purpose computer, or a dedicated processing machine to perform a certain function or a group of functions.

[0120] The phraseology and terminology used herein are for descriptive purposes and should not be construed as limiting. The use of "including," "comprising," "having," "includes," "involving," "characterized by," "characterized by," and variations thereof herein is meant to encompass the items listed thereafter, their equivalents and additional items, and alternative implementations consisting solely of the items listed thereafter. In one implementation, the systems and methods described herein consist of one, each combination of more than one, or all of the described elements, acts, or components.

[0121] Any reference herein to an implementation or element or action of a system or method mentioned in the singular may also include implementations that include a plurality of such elements, and any reference herein to any implementation or element or action in the plural may also include implementations that include only a single element. Reference in the singular or plural is not intended to limit the presently disclosed systems or methods, their components, actions, or elements to a single configuration or plural configurations. Reference to any action or element based on any information, action, or element may include implementations in which the action or element is based, at least in part, on any information, action, or element.

[0122] Any implementation disclosed herein may be combined with any other implementation or embodiment, and references to "implementations," "some implementations," "an implementation," etc. are not necessarily mutually exclusive and are intended to indicate that a particular feature, structure, or characteristic described in connection with the implementation may be included in at least one implementation or embodiment. These terms as used herein are not necessarily all referring to the same implementation. Any implementation may be combined with any other implementation, inclusively or exclusively, in any manner consistent with the aspects and implementations disclosed herein.

[0123] Where a technical feature in the drawings, detailed description, or any claims is followed by a reference sign, the reference sign is included to enhance the intelligibility of the drawings, detailed description, and claims. Therefore, neither the reference sign nor its absence shall have any limiting effect on the scope of any claim element.

[0124] The systems and methods described herein may be embodied in other specific forms without departing from their characteristics. Unless expressly stated otherwise, references to any degree terms include variations of + / - 10% from the given measurement, unit, or range. Coupled elements may be coupled electrically, mechanically, or physically to each other directly, or with the use of intermediate elements. The scope of the systems and methods described herein is therefore indicated by the appended claims, rather than the foregoing description, and changes that come within the meaning and range of equivalents of the claims are embraced therein.

[0125] The term "coupled" and variations thereof include two members being directly or indirectly joined to one another. Such joining may be static (e.g., permanent or fixed) or removable (e.g., removable or releasable). Such joining may be achieved by direct coupling of the two members or coupling to one another, by coupling the two members to one another using a separate intermediate member and any additional intermediate members, or by coupling the two members to one another using an intermediate member integrally formed as a single unitary body with one of the two members. If "coupled" or variations thereof are modified by additional terms (e.g., directly coupled), the general definition of "coupled" provided above is modified by the plain language meaning of the additional terms (e.g., "directly coupled" means the joining of two members without any separate intermediate member), resulting in a narrower definition than the general definition of "coupled" provided above. Such coupling may be mechanical, electrical, or fluidic.

[0126] References to "or" are to be construed as inclusive, such that any term described using "or" may refer to any of a single, more than one, and all of the terms. References to "at least one of 'A' and 'B'" may include only "A," only "B," and both "A" and "B." Such references used in conjunction with "including" or other open-ended terms may include additional items.

[0127] Without materially departing from the teachings and advantages of the subject matter disclosed herein, modifications may be made to the described elements and actions, such as changes in the dimensions, size, structure, shape and proportions of the various elements, parameter values, mounting arrangements, material usage, color, orientation. For example, an element shown as integrally formed may be composed of multiple parts or elements, the positions of elements may be reversed or otherwise changed, and the nature, number, or position of discrete elements may be changed or varied. Other substitutions, modifications, changes, and omissions may also be made in the design, operating conditions, and arrangement of the disclosed elements and actions without departing from the scope of the present disclosure.

Claims

1. A method comprising: receiving one or more attributes of the patient; receiving a plurality of audio content from a data source associated with the patient; generating predictions of amplitudes and affinity scores of brain response signals at target frequencies for at least some of the plurality of audio contents; selecting the first audio content from the plurality of audio content based on the prediction of the amplitude and affinity score for the first audio content; and A control signal is transmitted to an output device to cause the output device to present the first audio content, thereby providing audio stimulation to the patient.

2. The method according to claim 1, wherein The affinity score of the first audio content is based on: Music parameters of the first audio content; The user's social peers; and The user's age. 3 . The method of claim 1 , further comprising generating a prediction of an amplitude and an affinity score of a brain response signal for each of the plurality of audio contents from the data source.

4. The method according to claim 1, wherein The data source includes a library associated with a patient, and wherein at least some of the plurality of audio content from the library are merged into the library by a peer of the patient.

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