DEVICE, METHOD, AND APP FOR FACILITATING SLEEP

Abstract

A device, system, and method for facilitating a sleep cycle in a subject, comprising selecting a waveform from a plurality of waveforms derived from brainwaves of at least one sleeping donor, wherein said waveform corresponds to at least one specific stage of sleep; and stimulating the subject with at least one stimulus, wherein said at least one stimulus is at least one of an auditory stimulus and a visual stimulus modulated with the selected waveform to entrain the brain of the subject with the selected waveform to facilitate sleep in the subject.

Claims

1. A method of facilitating sleep using brain stimulation, comprising: providing data defining a plurality of waveforms in a memory; retrieving a selected waveform from the memory, selectively dependent on at least one of a determined sleep phase of a human subject and a predetermined sequence; and stimulating the human subject with a stimulus modulated according to the selected waveform, to facilitate sleep in the human subject.

2. The method according to claim 1, wherein the plurality of waveforms in the memory are derived from brain activity measurements acquired during at least one sleep cycle of at least one human.

3. The method according to claim 1, wherein the plurality of waveforms in the memory are derived from brain activity measurements acquired during at least one sleep cycle of the human subject.

4. The method according to claim 1, further comprising: acquiring brain neuronal activity measurements during at least one sleep cycle of at least one human; and processing the acquired brain activity measurements to define the plurality of waveforms in the memory; entaining the brain of the human subject with the selected waveform.

5. The method according to claim 1, wherein the stimulus is modulated in a user device associated with the human subject, according to a sleep app stored within the user device, the sleep app being downloadable and upgradeable from a remote server.

6. The method according to claim 5, wherein the predetermined sequence is defined by a human user interface menu of the user device for selecting at least one respective waveform.

7. The method according to claim 1, wherein the sleep phase of the human subject is determined based on at least neuronal activity of the human subject recorded via an electroencephalogram.

8. The method according to claim 1, wherein the sleep phase of the human subject is determined based on at least bioelectric signals received from the human subject.

9. The method according to claim 1, wherein the stimulus modulated according to the selected waveform entrains the brain of the human subject with the selected waveform to facilitate sleep in the human subject.

10. The method according to claim 1, wherein the stimulus modulated according to the selected waveform is at one least of alight stimulus and a sound stimulus.

11. The method according to claim 1, wherein the selected waveform corresponds to at least one specific stage of sleep.

12. The method according to claim 1, wherein each of the plurality of waveforms is derived from recordings of brainwaves of at least one sleeping donor, processed using a statistical decision analysis.

13. The method according to claim 1, further comprising: adaptively defining a sequence of sleep stages dependent on biometric information received from a sleeping human subject; and selecting waveforms from the memory in dependence on a correspondence to a respective sleep stage of the adaptively defined sequence of sleep stages; wherein said stimulating the human subject comprises altering a sleep stage of the human subject dependent on both the determined sleep phase of a human subject and the adaptively defined sequence of sleep stages.

14. The method according to claim 1, wherein the human subject is stimulated with at least one audio transducer and wherein the stimulus comprises at least one of an isochronic tone and binaural beats.

15. The method according to claim 1, wherein the human subject is stimulated with an ambient light stimulus, selectively modulated according to the selected waveform to change at least one of brightness and color.

16. The method according to claim 15, wherein the ambient light stimulus is emitted by at least one light emitting diode disposed in a sleep mask proximate the human subjects eyes.

17. The method according to claim 1, further comprising providing at least one sensor to determine at least one of an eye movement and a facial expression of the human subject to at least one of determine a current determined sleep phase of a human subject or select the predetermined sequence.

18. The method of claim 1, wherein the predetermined sequence is a natural series of sleep stages, the method further comprising resetting the progress according to the natural series of sleep stages in dependence on an awakening of the human subject.

19. A method of generating a waveform for neuromodulation to improve sleep in a subject, the method comprising: collecting EEG recordings from at least one sleeping donor for a plurality of sleep stages; grouping a plurality of portions of the EEG recordings corresponding to the plurality of sleep stages, into a plurality of groups corresponding to the plurality of sleep stages; analyzing each group using a statistical analysis; extracting a cortical signature corresponding characteristic of each analyzed group; generating a waveform based on the characteristic cortical signature for each of the plurality of sleep stages; and modulating a stimulus for the subject according to the generated waveforms for the plurality of sleep stages.

20. A mobile device contained within a housing, comprising: a microprocessor; an electrical power source, electrically coupled with the microprocessor; a wireless communication transceiver, electrically coupled with the microprocessor; at least one microphone port, electrically coupled with the microprocessor, configured to receive an electrical signal corresponding to a sound; at least one camera port electrically coupled with the microprocessor, configured to receive an electrical signal corresponding to an image; a display, electrically coupled with the microprocessor; at least one speaker port, electrically coupled with the microprocessor, configured to generate an electrical signal corresponding to a sound; a non-volatile memory and electrically coupled with the microprocessor, configured to store at least one app downloadable through the wireless communication transceiver for controlling the microprocessor, said at least one downloadable app being configured to: (a) select a waveform from a plurality of waveforms derived from brainwaves of at least one sleeping donor, wherein said waveform corresponds to at least one a specific stage of sleep, a gender, and an age group; and (b) define a stimulus for stimulation of a subject, selected from the group consisting of at least one of an auditory stimulus generated through the at least one speaker, and a visual stimulus generated through the display, modulated with the selected waveform, and adapted to entrain the brain of the subject with the selected waveform to facilitate sleep in the subject; wherein at least one of the selection of the waveform and the definition of the stilulus is responsive to the at least one microphone or the at least one camera.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0356] The detailed description is described with reference to the accompanying figures. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. The use of the same reference number in different figures indicates similar or identical items.

[0357] FIG. 1 shows a flowchart according to one embodiment of the invention illustrating a process of replicating a sleep state from one subject to another subject.

[0358] FIG. 2 shows a flowchart according to one embodiment of the invention illustrating a process of replicating a waking stage from one subject to another subject by recording and replicating brainwaves associated with the waking stage, according to one embodiment of the invention.

[0359] FIG. 3 shows a flowchart according to one embodiment of the invention illustrating a process of replicating a sleep stage from at least one first subject to another subject by recording electroencephalogram (EEG) of said least one first subject, extracting at least one dominant frequency from the EEG and replicating the sleep stage of said at least one first subject in a second subject by stimulating the second subject with stimuli having the dominant frequency associated with the desired sleep stage, according to one embodiment of the invention.

[0360] FIG. 4 shows a flowchart according to one embodiment of the invention illustrating a method of improving sleep in a recipient by recording EEG or MEG of a healthy donor and playing it back to the recipient via transcranial stimulation.

[0361] FIG. 5 shows a flowchart according to one embodiment of the invention illustrating the creation of a database of sleep stages and their associated frequencies for later brain entrainment.

[0362] FIG. 6 shows a flowchart according to one embodiment of the invention illustrating using a neural network in the creation of a database of sleep stages and their associated frequencies for later brain entrainment.

[0363] FIG. 7 shows a flowchart according to one embodiment of the invention illustrating a method of recording a mental state of a first subject in a desirable state of the subject's circadian rhythm and transplanting this mental state into another subject to replicated the desirable state of the circadian rhythm.

[0364] FIG. 8 shows a flowchart according to a further embodiment of the invention.

[0365] FIG. 9 shows a flowchart according to one embodiment of the invention illustrating a process of replicating the desired sleep stage from one subject to another subject.

[0366] FIG. 10 shows a flowchart according to an embodiment of the invention.

[0367] FIG. 11 shows a flowchart according to an embodiment of the invention.

[0368] FIG. 12 shows a flowchart according to an embodiment of the invention.

[0369] FIG. 13 shows a flowchart according to an embodiment of the invention.

[0370] FIG. 14 shows a flowchart according to an embodiment of the invention.

[0371] FIG. 15 shows a flowchart according to an embodiment of the invention.

[0372] FIG. 16 shows a schematic representation of a smartphone for executing apps.

[0373] FIG. 17 shows a hypnogram of a healthy adult.

[0374] FIG. 18 shows a hypnogram of a healthy adult.

[0375] FIG. 19 shows a sequence of sleep stages in a healthy adult.

[0376] FIG. 20A shows an original EEG recording of a REM phase in a 34 years old female.

[0377] FIG. 20B shows an EEG recording of a REM phase in a 34 years old female reconstructed with 64 SSA groups.

[0378] FIG. 20C shows an EEG recording of a REM phase in a 34 years old female reconstructed with 16 SSA groups.

[0379] FIGS. 21A and 21B show an EEG for a 30 years old female in sleep stage R.

[0380] FIG. 22 show an EEG for a 30 years old female in sleep stage 3.

[0381] FIGS. 23A and 23B show an EEG for a 30 years old female in sleep stage 3.

[0382] FIGS. 24A and 24B show an EEG for a 25 years old female in sleep stage W.

[0383] FIGS. 25A and 25B show an EEG for a 25 years old male in sleep stage 2.

[0384] FIGS. 26A and 26B show an EEG for a 25 years old male in sleep stage 1.

[0385] FIGS. 27A and 27B show an EEG for a 25 years old male in sleep stage W.

[0386] FIG. 28 shows a schematic diagram of a mental state transfer system.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0387] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the accompanying drawings so that the present disclosure may be readily implemented by those skilled in the art. However, it is to be noted that the present disclosure is not limited to the embodiments but can be embodied in various other ways. In drawings, parts irrelevant to the description are omitted for the simplicity of explanation, and like reference numerals denote like parts through the whole document.

[0388] FIG. 1 shows a flowchart of a first embodiment according to the present invention. A first subject (donor), having a mental state, is interrogated, observed or sensed, to determine or identify his or her mental state 100. The first subject is typically human, though this is not a limit of the technology and the subject may be an animal. In this embodiment, the process seeks to identify a characteristic sleep pattern, and therefore the mental state of the first subject is monitored until a sleep state occurs 110. When the first subject (donor) is asleep, brain activity patterns reflecting or characterizing the sleep state are captured 120. This step may be done by recording EEG or MEG of the first subject (donor). And the brain activity patterns are stored in a non-volatile memory 130. These stored patterns may be optionally processed, statistically aggregated, analyzed for perturbations or anomalies, filtered, compressed, etc. Stages of sleep may be determined. It is noted that brain activity patterns change over time during sleep from stage to stage, and therefore, the stored patterns may encompass one or more stages of sleep.

[0389] The stored data from the first subject (donor) is then used to induce sleep in a second subject (a recipientalso typically a human, but may be an animal) by replicating the brain activity patterns (or sequences of brain activity patterns) of the first subject (donor) in the second subject (recipient) 150. The replication of brain activity patterns, dependent on the stored patterns, typically seeks to stimulate or induce the brain of the second subject (recipient) by modulating a stimulus (or several stimuli) in a manner synchronized with the frequency, phase and/or waveform pattern represented in the brain activity patterns of the first subject (donor) in the sleep state. Typically, when the second subject (recipient) achieves the sleep state 160 (assuming that the first subject and second subject are physiologically compatiblea donor and a recipient should both be either human or animals), the brain activity patterns of the first and second subject will be corresponding.

[0390] According to the present technology, the modulation of stimulation, which is, for example, a sensory stimulation, whose waveform is modulated to correspond to the raw or processed brainwave pattern of the first subject (donor) for the brain region associated with the stimulation electrode.

[0391] For example, the brain activity pattern of the first subject (donor) is measured by EEG electrodes. In a sleep state, it may assume various wave patterns, over the range <1 Hz to about 25 Hz, which vary in amplitude, frequency, spatial location, and relative phase. For example, the first stage of sleep is initially dominated by alpha brainwaves with a frequency of 8 Hz to 13 Hz. Typically, brain activity pattern measurement from the first subject (donor) has a higher spatial resolution, e.g., 64 or 128 electrode EEGs, than the stimulator for the second subject (recipient), and the stimulus electrodes tend to be larger than the EEG electrode. The stimulus for the second subject (recipient) is therefore processed using a dimensionality (or spatial) reduction algorithm to account for these differences, which will tend to filter the stimulus signal. By applying this stimulus modulated with the brain activity of the first subject (donor), the second subject (recipient) is made susceptible to synchronization with the brain activity pattern of the first subject (donor). For example, by temporally modulating the polarization level of the cells near the electrode, the cells will better couple to excitation stimuli in the brain of the second subject (recipient) having the characteristics of the brain activity pattern of the first subject (donor).

[0392] The donors indigenous brainwaves may be modulated on light, sound, vibrations or any number of other stimuli amenable to frequency modulation. For example, donors brainwaves may be modulated on ambient light, on binaural beats, or isochronic tones.

[0393] The verification that the recipient has achieved the desired sleep state may optionally be done by visual observation, by EEG, EKG, measuring heart and/or respiration rate, body temperature or any number of other physiological parameters that will be well understood by a person skilled in the art. These measurements should be, preferably, done automatically via biosensors.

[0394] FIG. 2 shows a flowchart of the second embodiment according to the present invention. A first subject (donor), having a mental state, is interrogated, observed or sensed, to determine or identify of his or her mental state 100. The first subject is typically human, though this is not a limit of the invention (which equally applies to any animal). In this embodiment, the interrogation seeks to identify a characteristic alert/awake pattern, and therefore the mental state of the first subject is monitored until an alert state occurs 111. When the first subject (donor) is awake, brain activity patterns reflecting or characterizing the waking state are captured 120, and stored in a non-volatile memory 130. For example, one may seek to capture the patterns that represent awakening, and therefore, the monitoring commences on a sleeping subject. These stored patterns may be optionally processed, statistically aggregated, analyzed for perturbations or anomalies, filtered, compressed, etc Stages of awakening may be determined. It is noted that the brain activity patterns change over time during awakening, and therefore, the stored patterns may encompass one or more stages of the waking process.

[0395] The stored data from the first subject (donor) is then retrieved from the non-volatile memory 140 and used to transplant the state of alertness to prevent sleep, or maintain alertness, in a second subject (a recipientalso typically, but not necessarily, a human) by replicating the awake brain activity patterns of the first subject (donor), or sequences of brain activity patterns, in the second subject (recipient) 170. The replication of brain activity patterns, dependent on the stored patterns, typically seeks to stimulate or induce the brain of the second subject (recipient) by modulating indigenous brainwaves of the donor on a stimulus in a manner synchronized with the frequency, and preferably phase and/or waveform pattern represented in the brain activity patterns of the first subject (donor) in the awake or wakening state. Typically, when the second subject is awake or wakes up, 180, the brain activity patterns of the first and second subject will be corresponding.

[0396] FIG. 3 shows a flowchart of a third embodiment, in which the technology is generalized. A first subject (donor), having a mental state, is interrogated, observed or sensed, to determine or identify his or her mental state 190. The mental state of the first subject is monitored until the desired state is achieved 200. When the first subject achieves that state, brain activity patterns reflecting or characterizing the state are captured 210 by, for example, recording EEG or MEG of the first subject, and optionally stored in non-volatile memory. The brain activity pattern is, e.g., brainwaves (e.g., EEG) 210.

[0397] The brainwaves are analyzed using statistical data mining techniques such as principal component analysis (PCA) to determine a set of linearly-uncorrelated variablesprincipal components. At least one dominant frequency in the recorded brainwaves is identified 220. Optionally, secondary and higher harmonics may be identified as well. It will be well-understood by a person skilled in the ad that any number of similar statistical data analysis techniques may be used, such as signal processing, independent component analysis, network component analysis, correspondence analysis, multiple correspondence analysis, factor analysis, canonical correlation, functional principal component analysis, independent component analysis, singular spectrum analysis, weighted PEA, sparse PCA principal geodesic analysis, eigenvector-based multivariate analyses, etc.

[0398] The stored data from the first subject is then retrieved, at least the dominant frequency is modulated on at least one stimulus and used to transplant the desired mental state of the donor in a second subject (recipient) by seeking to replicate the brain activity patterns of the first subject (donor), or sequences of brain activity patterns, in the second subject (recipient) 240. The second subject (recipient) is then monitored for induction of the desired mental state 250.

[0399] FIG. 4 shows a flowchart according to the fourth embodiment, in which an EEG or EMG of a first subject (healthy donor), while in a state of sleep, is recorded 260, optionally processed to remove noise 270, and stored 280. The data may optionally be compressed. The stored data is retrieved 290 and decompressed as necessary. The data is then played back to a second subject (recipient), to improve the quality of sleep 300.

[0400] FIG. 5 shows a flowchart according to the fifth embodiment, in which a multichannel EEG/EMG of a first subject (donor) is recorded 310, and processed to remove noise (and/or artifacts) and/or compress the data 320. It is optionally stored in non-volatile memory. PCA analysis is performed on the data to determine characteristic frequencies associated with sleep stages 330. A database is created, storing the recorded EEG/MEG, the associated characteristic frequencies, and corresponding sleep stages, so that a characteristic frequency may be retrieved for any given sleep stage 340. This database can be a relational database or any other type of searchable database as will be readily understood by anyone skilled in the art. According to the sixth embodiment, a multichannel EEG/EMG of a first subject (donor) is recorded 310, and processed to remove noise (and/or artifacts) and/or compress the data 320. It is optionally stored in non-volatile memory. An artificial neural network is trained on this data to determine characteristic frequencies associated with sleep stages 350. A deep neural network, as well as other AI machine-learning tools, may be used as will be readily understood by a person skilled in the art. A database is created, storing the recording of the EEG/MEG, the associated characteristic frequencies, and corresponding sleep stages, so that a characteristic frequency may be retrieved for any given sleep stage 340.

[0401] FIG. 6 shows a flowchart according to an embodiment of the invention. A multichannel EEG or EMG of a plurality of healthy sleeping donors is recorded 310. The multichannel EEG/EMG recordings are processed too, e.g., remove noise 320. A neural network is trained on the recorded EEG/EMG recordings to identify characteristic frequencies associated with sleep stages 350. A database of sleep stage characteristic frequencies is created.

[0402] FIG. 7 shows a flowchart according to a further embodiment of the present invention illustrating a process in which a first subject (donor) is monitored with respect to phases of his or her circadian rhythm with his or her EEG or EMG recorded 360, processed to remove noise (and/or artifacts), and, optionally, compressed 270, and then stored in a non-volatile memory 280. In this case, the stored signals are tagged with the circadian cycle phase, unless only a single phase is captured, or pattern recognition used to identify the cycle stage. The stored data is then retrieved 290, decompressed 370, and played back to a second subject (recipient) 380, using sensory stimulation, or other stimuli, to induce a desired circadian rhythm state. In this case, the technology may also be used to prolong states in the second subject or hasten the transition from one state to another. It may also be used to treat circadian rhythm disorders, by reinforcing healthy or normal circadian rhythm patterns in a second subject with an otherwise abnormal cycle.

[0403] FIG. 8 shows a flowchart according to a further embodiment of the present invention illustrating a process of replicating the desired sleep stage from one subject (donor) to another subject (recipient). In general, the sleep stage of the source subject is determined in a traditional manner, which may include brain signal analysis, other biometrics, and/or observation. The data may be acquired 400 over one or more sleep cycles, and during or after different types of environmental conditions or stimulation. For example, various types of music may be played, seeking to entrain a conscious or subconscious rhythm. Lights can flash, and various other sensory stimulation may occur. The brain signal readings are synchronized and tagged with the stimulation parameters 410 so that the stimulation is associated with its respective effect. Similarly, before sleep, the subject may be presented with certain experiences, such that during sleep, the memory processing within the brain is dependent on these experiences.

[0404] After the various data is acquired from the subject 400, along with information about the pre-sleep experience and or context 410, and sensory Stimulation during sleep, a memory, database, statistical model, the rule-based model is generated, and/or neural network is trained, reflecting the subject (donor). Data may be aggregated from a plurality of subjects (donors), but typically, these are processed for the particular subject before aggregation. Based on single or multiple subject data, a normalization process may occur 420. The normalization may be spatial and/or temporal. For example, the EEG electrodes between sessions or for the different subject may be in different locations, leading to a distortion of the multichannel spatial arrangement. Further, the head size and shape of different individuals are different, and this needs to be normalized and/or encoded as well. The size and shape of the head/skull and/or brain may also lead to temporal differences in the signals, such as characteristic time delays, resonant or characteristic frequencies, etc.

[0405] One way to account for these effects is through the use of a time-space transform, such as a wavelet-type transform. It is noted that, in a corresponding way that statistical processes are subject to frequency decomposition analysis through Fourier transforms, they are also subject to time-frequency decomposition through wavelet transforms. Typically, the wavelet transform is a discrete wavelet transform (DINT), though more complex and less regular transforms may be employed. As discussed above, principal component analysis (PCA) and spatial PCA may be used to analyze signals, presuming linearity (linear superposition) and statistical independence of components. However, these presumptions technically do not apply to brainwave data, and practically, one would normally expect interaction between brain wave components (non-independence) and lack of linearity (since neural networks by their nature are non-linear), defeating the use of PCA or spatial PCA unmodified. However, a field of nonlinear dimensionality reduction provides various techniques to permit corresponding analyses under the presumptions of non-linearity and non-independence. See, en.wikipeda.org/wiki/Nonlinear_dimensionality_reduction, www.image.ucar.edu/pub/toyN/monahan_5_16.pdf (An Introduction to Nonlinear Principal Component Analysis, Adam Monahan), Nonlinear PCA toolbox for MATLAB (www.nlpca.org), Nonlinear PCA (www.comp.nus.edu.sg/cs5240/lecture/nonlinear-pca.pdf), Nonlinear Principal Components Analysis: Introduction and Application (openaccess.leidenuniv.nl/bitstream/handle/1887/12386/Chapter2.pdf?sequence=10, 2007), Nonlinear Principal Component Analysis: Neural Network Models and Applications (pdfs.semanticscholar.org/9d31/23542031a227d2f4c4602066cf8ebceaeb7a.pdf), Karl Friston, Nonlinear PCA: characterizing interactions between modes of brain activity (www.fil.ion.uctac.uk/karl/NonlinearPCA.pdf,2000), Howard et al., Distinct Variation Pattern Discovery Using Alternating Nonlinear Principal Component Analysis, IEEE Trans Neural Network Learn Syst 2018 January; 29(1):156-166. doi: 10.1109/TNNLS.2016.2616145. Epub 2016 Oct. 26 (www.ncbi.nlm.nih.gov/pubmed/27810837); Jolliffe, I. T., Principal Component Analysis, Second Edition, Springer 2002, cda.psych.uiuc.edu/statistical_learning_course/Jolliffe I. Principal Component Analysis (2ed., Springer, 2002)(518s)_MVsa_.pdf, Stone, James V. Blind source separation using temporal predictability. Neural computation 13, no. 7 (2001):1559-1574; Barros, Allan Kardec, and Andrzej Cichocki. Extraction of specific signals with temporal structure. Neural computation 13, no. 9 (2001):1995-2003; Lee, Soo-Young. Blind source separation and independent component analysis: A review. Neural Information Processing-Letters and Reviews 6, no. 1 (2005): 1-57; Hyvrinen, Aapo, and Patrik Hoyer. Emergence of phase- and shift-invariant features by decomposition of natural images into independent feature subspaces. Neural computation 12, no. 7 (2000):1705-1720; Wahlund, Bjrn, Wlodzimierz Klonowski, Pawel Stepien, Robert Stepien, Tatjana von Rosen, and Dietrich von Rosen. EEG data, fractal dimension and multivariate statistics. Journal of Computer Science and Engineering 3, no. 1 (2010): 10-14; Yu, Xianchuan, Dan Hu, and Jindong Xu. Blind source separation: theory and applications. John Wiley & Sons, 2013; Panda, Shantipriya, Satchidananda Dehuri, and Sung-Bae Cho. Machine Learning Approaches for Cognitive State Classification and Brain Activity Prediction: A Survey. Current Bioinformatics 10, no. 4 (2015): 344-359; Friston, Karl J., Andrew P. Holmes, Keith J. Worsley, J-P. Poline, Chris D. Frith, and Richard S J Frackowiak. Statistical parametric maps in functional imaging: a general linear approach. Human brain mapping 2, no. 4 (1994):189-210; Wang, Ya n, Matthew T. Sutherland, Lori L Sanfratello, and Akaysha C. Tang. Single-trial classification of ERPS using second-order blind identification (SOBI). In Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on, vol. 7, pp. 4246-4251. IEEE, 2004; Jutten, Christian, and Massoud Babaie-Zadeh. Source separation: Principles, current advances and applications. IAR Annu Meet Nancy Fr 110 (2006); Saproo, Sameer, Victor Shih, David C Jangraw, and Paul Sajda. Neural mechanisms underlying catastrophic failure in human-machine interaction during aerial navigation. Journal of neural engineering 13, no. 6 (2016): 066005; Valente, Giancarlo. Separazione cieca di sorgenti in ambienti reali: nuovi algoritmi, applicazionie implementazioni. (2006); SAPIENZA L A. Blind Source Separation in real-world environments: new algorithms, applications and implementations Separazione cieca di sorgenti in ambienti reali: nuovi algoritmi, applicazionie.; Ewald, Arne. Novel multivariate data analysis techniques to determine functionally connected networks within the brain from EEG or MEG data (2014); Friston, Karl J. Basic concepts and overview. SPMcourse, Short course; Crainiceanu, Ciprian M., Ana-Maria Staicu, Shubankar Ray, and Naresh Punjabi. Statistical inference on the difference in the means of two correlated functional processes: an application to sleep EEG power spectra. Johns Hopkins University, Dept. of Biostatistics Working Papers (2011): 225; Konar, Amit, and Aruna Chakraborty. Emotion recognition: A pattern analysis approach. John Wiley & Sons, 2014; Kohl, Florian. Blind separation of dependent source signals for MEG sensory stimulation experiments. (2013); Onken, Arno, Jian K Liu, P P Chamanthi R. Karunasekara, loannis Delis, Tim Gollisch, and Stefano Panzeri. Using matrix and tensor factorizations for the single-trial analysis of population spike trains. PLoS computational biology 12, no. 11 (2016): e1005189; Tressold, Patrizio, Luciano Pederzoli, Marco Bilucaglia, Patrizio Caini, Pasquale Fedele, Alessandro Ferrini, Simone Melloni, Diana Richeld, Florentina Richeld, and Agostino Accardo. Brain-to-Brain (Mind-to-Mind) Interaction at Distance: A Confirmatory Study. (2014). f1000researchdata. s3.amazonaws.com/manuscripts/5914/5adbf847-787a-4fc1-ac04-2e1cd61ca972_4336_-_patrizio tressoldi_v3.pdf?doi=10.12688/f1000research.4336.3; Tsia pa ras, Nikolaos N. Wavelet analysis in coherence estimation of electroencephalographic signals in children for the detection of dyslexia-related abnormalities. PhD diss., 2006.

[0406] FIG. 9 shows a flowchart of an embodiment of the invention. Asleep stage of a first subject is identified, and then it is determined whether the sleep stage is the desired sleep stage. If not, the first subject is further monitored. If the sleep stage is the one desired, the brain activity of the first subject is captured, reflecting the sleep stage, and the brain activity patterns of the first subject while in the desired sleep stage stored in non-volatile memory 500. The stored brain activity patterns are subsequently retrieved and used to induce the sleep stage in a second subject by replicating the brain activity patterns of the first subject in the second subject by appropriate stimulation of the second subject. The second subject may be monitored to verify that the second subject is in the desired sleep stage.

[0407] As shown in FIG. 10, a human brain state or mental state in a subject is modified or altered. In some implementations, a current brainwave pattern of the subject, a phase of a characteristic wave of the current brainwave pattern of the subject, a characteristic timing of a stimulus-response dependent on the mental state, or temporal relationships in monitored neurological or motor patterns of the subject is determined. The desired change in the current brain wave pattern of the subject is determined or defined. A stimulus is applied, e.g., electrical, magnetic, acoustic or ultrasound, sensory, etc., which can be for determining the current state, changing the state, or both. For example, a characteristic timing of a stimulus-response dependent on the mental state may be extracted, or temporal relationships in monitored neurological or motor patterns of the subject determined. The stimulus may be asynchronous, or time-synchronized with respect to the phase state, or dependent on at least the determined temporal relationships. In a closed-loop excitation, the brain wave pattern of the subject after at least one stimulus is monitored or the response parameters, e.g., characteristic timing measured or assessed. The stimulus may be controlled dependent on the observed or monitored changes, indicative of an effective alteration or modification of the brain state or mental state in the subject. FIG. 10 thus shows a flowchart of an embodiment of the invention. A desired mental state is identified 540. The mental state of a subject identified 550, and a phase of a dominant brainwave, characteristic of the mental state of the subject identified 560. A stimulus is applied to the subject to change the mental state of the subject to the desired mental state, while synchronizing the phase of the stimulus with the phase of the dominant brainwave of the subject 570. The subject is monitored to determine if the desired mental state is achieved. If the desired mental state is sleep, the sleep state of the subject may be verified 580.

[0408] FIG. 11 shows a flowchart of a further embodiment of the invention. An app is opened on a smartphone, tablet or another mobile or wearable device 1110. Note that in some applications, the device need not be mobile, and for example may be part of a headboard, nightstand, dock radio, etc A soundtrack conducive to sleep, e.g., sounds of rainfall, waterfall, ocean waves, a melody, white noise, pink noise, etc., is chosen 1120. An organic waveform is chosen, derived from brainwaves of a sleeping donor, corresponding to a specific stage of a sleep cycle or a complete sleep cycle 1130. The sound delivery may be chosen to be through a mobile device speaker, earphones, wireless earbuds. If separate sound delivery to each ear, the sound may be isochronic tones or binaural beats 1140, while if not isolated, isochronic tones may be played 1160.

[0409] FIG. 12 shows a flowchart of a still further embodiment of the invention. An app may be opened on a smartphone, tablet or wearable device 110. Light settings, such as color and intensity, are chosen 1220. An organic waveform derived from brainwaves of a sleeping donor, corresponding to a specific stage or stages of sleep, or a complete sleep cycle is chosen 1230. Light delivery may be chosen through an ambient light source or e.g., LEDs positioned on a wearable eye mask 1240, which is wirelessly connected to the device. Sleep stimulation is turned on by projecting the light modulated with the chosen organic waveform through ambient light or LEDs positioned near the eyes 1250.

[0410] FIG. 13 shows a flowchart of an embodiment of the invention. The subject opens an app on a device 1310, and chooses light and sound settings, e.g., color, intensity, sound, volume, etc. 1320. An organic waveform derived from the brainwaves of a sleeping donor is chosen, e.g., automatically by the app, corresponding to a specificstage(s) of sleep or a complete sleep cycle 1330. The stimulus is chosen as light or sound delivery through the device or wirelessly 1340. Sleep stimulation is turned using synchronized light and sound modulated with the chosen organic waveform 1350.

[0411] FIG. 14 shows a flowchart of an embodiment of the invention. The subject opens an app on e.g., a mobile or wearable device 1410, and chooses light and/or sound settings, e.g., color, intensity, sound, volume, etc. 1420. An organic waveform derived from the brainwaves of a sleeping donor is chosen, e.g., automatically by the app, corresponding to a specificstage(s) of sleep or a complete sleep cycle 1430. The stimulus is chosen as light and/or sound delivery through the device or wirelessly 1440. Sleep stimulation is turned using synchronized light and/or sound modulated with the chosen organic waveform 1450. EEG and/or other biometric data is recorded from the subject and transmitted in real time to the device or a cloud computing resource for analysis 1460. The stimulation of the subject is adjusted based on the data received from the subject 1470.

[0412] FIG. 15 shows a flowchart of an embodiment of the invention. The subject opens an app on, e.g., a mobile or wearable device and logs in to a personal account 1510. A new waveform, modified from the last waveform used based on biometric sleep data received from the subject during a previous stimulation session 1520. Light and/or sound delivery through the device or through a wireless peripheral is chosen 1540. Sleep stimulation is turned using synchronized light and/or sound modulated with the chosen organic waveform 1550. EEG and/or other biometric data is recorded from the subject and transmitted to a remote for analysis 1560. The received biometric data from the subject is analyzed, to measure the effectiveness of the stimulation and to adjust the waveform accordingly, to improve the effect of the stimulation 1570.

[0413] Therefore, statistical approaches are available for separating EEG signals from other signals, and for analyzing components of EEG signals themselves. According to the present invention, various components that might be considered noise in other contexts, e.g., according to prior technologies, such as a modulation pattern of a brainwave, are preserved. Likewise, interactions and characteristic delays between significant brainwave events are preserved. This information may be stored either integrated with the brainwave pattern in which it occurs or as a separated modulation pattern that can then be recombined with an unmodulated brainwave pattern to approximate the original subject.

[0414] According to the present technology, lossy perceptual encoding (i.e., functionally optimized with respect to a subjective response) of the brainwaves may be employed to process, store, and communicate the brainwave information. In a testing scenario, the perceptual features may be tested, so that important information is preserved over information that does not strongly correspond to the effective signal. Thus, while one might not know a priori which components represent useful information, a genetic algorithm may empirically determine which features or data reduction algorithms or parameter sets optimize retention of useful information vs. information efficiency. It is noted that subjects may differ in their response to signal components, and therefore the perceptual encoding may be subjective with respect to the recipient. On the other hand, different donors may have different information patterns, and therefore, each donor may also require individual processing. As a result, pairs of donor and recipient may require optimization, to ensure accurate and efficient communication of the relevant information. According to the present invention, sleep/wake mental states and their corresponding patterns are sought to be transferred. In the recipient, these patterns have characteristic brainwave patterns. Thus, the donor may be used, under a variety of alternate processing schemes, to stimulate the recipient, and the sleep/wake response of the recipient determined based on objective criteria, such as resulting brainwave patterns or expert observer reports, or subjective criteria, such as recipient self-reporting, survey or feedback. Thus, after a training period, optimized processing of the donor, which may include filtering, dominant frequency resynthesis, feature extraction, etc., may be employed, which is optimized for both donor and recipient. In other cases, the donor characteristics may be sufficiently normalized, that only recipient characteristics need be compensated. In a trivial case, there is only one exemplar donor, and the signal is oversampled and losslessly recorded, leaving only recipient variation as a significant factor.

[0415] Because dominant frequencies tend to have low information content (as compared to the modulation of these frequencies and interrelation of various sources within the brain), one efficient way to encode the main frequencies is by location, frequency, phase, and amplitude. The modulation of a wave may also be represented as a set of parameters. By decomposing the brainwaves according to functional attributes, it becomes possible, during stimulation, to modify the sequence of events from the donor, so that the recipient need not experience the same events, in the same order, and in the same duration, as the donor. Rather, a high-level control may select states, dwell times, and transitions between states, based on classified patterns of the donor brainwaves. The extraction and analysis of the brainwaves of the donors, and response of the recipient, may be performed using statistical processes, such as principal components analysis (PCA), independent component analysis (ICA), and related techniques; clustering, classification, dimensionality reduction and related techniques; neural networks and other known technologies. These algorithms may be implemented on general purpose CPUs, array processors such as GPUs, and other technologies.

[0416] In practice, a brainwave pattern of the first subject may be analyzed by a PCA technique that respects the non-linearity and non-independence of the brainwave signals, to extract the major cyclic components, their respective modulation patterns, and their respective interrelation. The major cyclic components may be resynthesized by a waveform synthesizer, and thus may be efficiently coded. Further, a waveform synthesizer may modify frequencies or relationships of components from the donor based on normalization and recipient characteristic parameters. For example, the brain of the second subject (recipient) may have characteristic classified brainwave frequencies 3% lower than the donor (or each type of wave may be separately parameterized), and therefore the resynthesis may take this difference into account. The modulation patterns and interrelations may then be reimposed onto the resynthesized patterns. The normalization of the modulation patterns and interrelations may be distinct from the underlying major cyclic components, and this correction may also be made, and the normalized modulation patterns and interrelations included in the resynthesis. If the temporal modifications are not equal, the modulation patterns and interrelations may be decimated or interpolated to provide a correct continuous time sequence of the stimulator. The stimulator may include one or more stimulation channels, which may be implemented as electrical, magnetic, auditory, visual, tactile, or another stimulus, and/or combinations.

[0417] The stimulator is preferably feedback controlled. The feedback may relate to the brainwave pattern of the recipient, and/or context or ancillary biometric basis. For example, if the second subject (recipient) begins to awaken from sleep, which differs from the first subject (donor) sleep pattern, then the stimulator may resynchronize based on this finding. That is, the stimulator control will enter a mode corresponding to the actual state of the recipient, and seek to guide the recipient to the desired state from a current state, using the available range and set of stimulation parameters. The feedback may also be used to tune the stimulator, to minimize error from a predicted or desired state of the recipient subject based on the prior and current stimulation.

[0418] The control for the stimulator is preferably adaptive and may employ a genetic algorithm to improve performance overtime. For example, if there are multiple first subjects (donors), the second subject (recipient) may be matched with those donors from whose brainwave signals (or algorithmically modified versions thereof) the predicted response in the recipient is best, and distinguished from those donors from whose brainwave signals the predicted response in the recipient subject poorly corresponds. Similarly, if the donors have brainwave patterns determined over a range of time and context and stored in a database, the selection of alternates from the database may be optimized to ensure best correspondence of the recipient subject to the desired response.

[0419] It is noted that a resynthesizer-based stimulator is not required, if a signal pattern from a donor is available that properly corresponds to the recipient and permits a sufficiently low error between the desired response and the actual response. For example, if a donor and a recipient are the same subject at different times, a large database may be unnecessary, and the stimulation signal may be a minimally processed recording of the same subject at an earlier time. Likewise, in some cases, a deviation is tolerable, and an exemplar signal may be emitted, with relatively slow periodic correction. For example, a sleep signal may be derived from a single subject and replayed with a periodicity of 90 minutes or 180 minutes, such as a light or sound signal, which may be useful in a dormitory setting, where individual feedback is unavailable or unhelpful.

[0420] In some cases, it is useful to provide a stimulator and feedback-based controller on the donor. This will better match the conditions of the donor and recipient, and further allow determination of not only the brainwave pattern of the donor but also responsivity of the donor to the feedback. One difference between the donors and the recipients is that in the donor, the natural sleep pattern is sought to be maintained and not interrupted. Thus, the adaptive mufti-subject database may include data records from all subject, whether selected ab initio as a useful exemplar or not. Therefore, the issue is whether a predictable and useful response can be induced in the recipient from the database record and, if so, that record may be employed. If the record would produce an unpredictable result or a non-useful result, the use of that record should be avoided. The predictability and usefulness of the responses may be determined by a genetic algorithm or other parameter-space searching technology.

[0421] Extending the sleep signal illumination example, an illuminator (e.g., red LED lightbulb) may have an intensity modulated based on a donors' brainwave pattern. The illuminator may have a flash memory module with tens or hundreds of different brainwave patterns available. The illuminator may further include a sensor, such as a camera or non-imaging optical or infrared sensor, and speech control, similar to Amazon Alexa. The illuminator may also include an associated speaker, to play synchronized sounds or music. When a sleep cycle is commenced, the illuminator begins displaying (and playing and associated audio) the brainwave pattern as a program, seeking to induce a predetermined sleep pattern. The sensors may be used to determine whether the recipient is in the predicted sleep state based on the program. If the recipient has a sleep state that deviates from the program, then the program may be reset to a portion that corresponds to the actual state of the recipient or reset to a guiding state that seeks to guide the sleep state of the recipient back to the desired program. If the target subject cannot be efficiently synchronized or guided, then the illuminator may adopt a different source subject brainwave pattern. In this case, no electrical stimulation or electrical feedback is employed, and the entire operation may be non-contact.

[0422] FIG. 16 shows a representation of a mobile device 11. The mobile device is shown in a familiar smadphone form factor. Data can be transferred to and from the mobile device 11 via wireless data communications. In general, the mobile device 11 can include a touch-sensitive display screen 18, a speaker 30, a microphone 31, and one or more control buttons 32 for controlling some operations of device 11. The device 11 depicted in FIG. 1(a) can be a device, such as, for example, a smartphone capable of communicating with a wireless local area network, and so forth. In this respect, the mobile device 11 can be implemented with touch screen capabilities associated with the display screen 18. Display screen 18 can be configured to display data including video and text and icons 33 operable as soft buttons providing options and action by the mobile device 11 when selected by a user. The mobile device 11 can be capable of carrying out a variety of functionalities. For example, microprocessor shown as CPU 10 of the mobile device 11 can function as the main controller operating under the control of operating docks supplied from a dock oscillator. CPU 10 can be configured as, for example, a microprocessor. Such a microprocessor can be configured to facilitate the operations of and communicate by the electronic wireless hand-held multimedia device 11. External pins of CPU 10 can be coupled to an internal bus 26 so that it can be interconnected to respective components. The mobile device 11 can also be configured to include memories such as, for example, SRAM 24 which can be provided as a writeable memory that does not require a refresh operation and can be generally utilized as a working area of CPU 10, SRAM (Static RAM) is generally a form of semiconductor memory (RAM) based on a logic circuit known as a flip-flop, which retains information as long as there is enough power to run the device. Font ROM 22 can be configured as a read only memory for storing character images (e.g., icons and font) displayable on a display 18, which can be implemented as, for example, a touch-sensitive display screen. Example types of displays that can be utilized in accordance with display 18 include, for example, a TFT active matrix display, an illuminated LCD (Liquid Crystal Display), or other small-scaled displays being developed or available in the art in compact form. CPU 10 can be utilized to drive display 18 utilizing, among other media, font images from Font ROM 22 and images transmitted as data through wireless unit 17 and processed by image-processing unit 35. EPROM 20 can be configured as a read-only memory that is generally erasable under certain conditions and can be utilized for permanently storing control codes for operating respective hardware components and security data, such as a serial number. A camera capable of capturing video and pictures can be provided and can also work in conjunction with the image processing unit 35.

[0423] IR controller 14, when provided, can be generally configured as a dedicated controller for processing infrared codes transmitted/received by an IR transceiver module 16 and for capturing the same as computer data. Wireless unit 17 can be generally configured as a dedicated controller and transceiver module for processing all wireless data transmitted from and to a wireless communications network. It can be appreciated that other variations for wireless transceiver module 17 can also be provided, such as standardized Bluetooth, NFC, Zigbee, etc., and proprietary RF protocols that may be developed for specialized applications.

[0424] Port 12 can be connected to CPU 10 and can be temporarily attached, for example, to a docking station to transmit information to and from the mobile device 11 to other devices, such as personal computers. In light of the present invention, port 12 can also be connected to external probes and external sensors for monitoring or providing data. Port 12 can also be configured, for example to link with a battery charger, data communication device, and can permit network devices, a personal computer, or other computing devices to communicate with mobile device 11.

[0425] User controls 32 can permit a user to enter data to mobile device 11 and/or initiate particular processing operations via CPU 10. A user interface 33 can be linked to user controls 32 to permit a user to access and manipulate electronic wireless hand held multimedia device 11 for a particular purpose, such as, for example, viewing video images on display 18. User interface 33 can be implemented as a touch screen manipulated user interface, as indicated by the dashed lines linking display 18 with user interface 33. User interface 33 can be configured to accept user input into the mobile device 11. In addition, CPU 10 can cause a sound generator 28 to generate sounds of predetermined frequencies from a speaker 30. Speaker 30 can be utilized to produce music and other audio information associated with video data transmitted to mobile device 11 from an outside source.

[0426] A GPS (Global Positioning System) module 13 can be included in the mobile device and can be connected to bus 26. GPS module 13 can be configured to provide location information for the mobile device 11 and can operate with mapping software and resources to provide navigable directions on the display screen 18 to the user, which can be referred to as GPS mapping. The CPU 10 can execute apps, which are downloadable programs that provide a user interface, and access to various application programming interface (API) calls made available through the operating system, but are generally limited to executing in a low privilege mode and without direct hardware or driver level access. The apps may be downloaded from the Internet, or an on-line service (e.g., iTunes store, Google Play) or through a wireless transceiver.

[0427] FIG. 17 shows a hypnogram of a healthy adult. As shown, the sleep cycle progresses non-monotonically through a series of stages.

[0428] FIG. 18 shows a hypnogram of a healthy adult. As shown, one sleep cycle lasting approximately 90 min is comprised of several sleep stages, including REM sleep (R), first non-REM stage (N1), second non-REM stage (N2), and third non-REM stage (N3), also known as slow-wave sleep, having different duration and periodicity. The waking stage is indicated on the hypnogram as W.

[0429] FIG. 19 shows a flowchart indicating the sequence of sleep stages.

[0430] FIGS. 20A-20C show a sample of the REM stage of sleep in a 34 year-old female under different filtering. This sample is obtained from the database of Sleep EEG recordings described in B Kemp, A H Zwinderman, B Tuk, H A C Kamphuisen, J J L Obery. Analysis of a sleep-dependent neuronal feedback loop: the slow-wave microcontinuity of the EEG. IEEE-BME 47(9):1185-1194 (2000) has been used. For each sleep/wake state of each subjects, 1260-seconds samples have been obtained (totaling up to 72 samples per subject, totaling 4898 samples). Only one channel (Fpz-Cz) has been considered. The samples have been cleaned from noise/non-stationary component using singular spectrum analysis, see Singular Spectrum Analysis with R. Springer. 2018 Authors: Golyandina, Nina, Korobeynikov, Anton, Zhigljaysky, Anatoly, generally following approach of Neurosci Methods. 2016 Nov. 1; 273:96-106. doi:10.1016/j.jneumeth.2016.08.008. Epub 2016 Aug. 12; Improving time-frequency domain sleep EEG classification via singular spectrum analysis. Mahvash Mohammad, Kouchaki, Ghavami, Sanei. Data analysis showed that the use of just 16 55A components is sufficient to preserve waveform spectrum of the EEG recordings, whereas 64 55A components is enough to very precisely match the shape as well. Restoration with the use of 64 components has been used to generate filtered EEG samples.

[0431] FIG. 20A shows the original, FIG. 20B shows sample reconstructed with 64 SSA groups, and FIG. 20C shows the sample reconstructed with 16 SSA groups, for a sample of REM phase of 34-year female.

[0432] FIGS. 21A and 21B show an EEG for a 30 years old female in sleep stage R.

[0433] FIG. 22 show an EEG for a 30 years old female in sleep stage 3.

[0434] FIGS. 23A and 23B show an EEG for a 30 years old female in sleep stage 3.

[0435] FIGS. 24A and 24B show an EEG for a 25 years old female in sleep stage W.

[0436] FIGS. 25A and 25B show an EEG for a 25 years old male in sleep stage 2.

[0437] FIGS. 26A and 26B show an EEG for a 25 years old male in sleep stage 1.

[0438] FIGS. 27A and 27B show an EEG for a 25 years old male in sleep stage W.

[0439] See Reference List Table 19

[0440] Through the whole document, the term connected to or coupled to that is used to designate a connection or coupling of one element to another element includes both a case that an element is directly connected or coupled to another element and a case that an element is electronically connected or coupled to another element via still another element. Further, it is to be understood that the term comprises or includes and/or comprising or including used in the document means that one or more other components, steps, operation and/or existence or addition of elements are not excluded in addition to the described components, steps, operation and/or elements unless context dictates otherwise.

[0441] Through the whole document, the term unit or module includes a unit implemented by hardware or software and a unit implemented by both of them. One unit may be implemented by two or more pieces of hardware, and two or more units may be implemented by one piece of hardware.

[0442] Other devices, apparatus, systems, methods, features, and advantages of the invention will be or will become apparent to one with skill in the art upon examination of the following figures and detailed description. It is intended that all such additional systems, methods, features, and advantages be included within this description, be within the scope of the invention, and be protected by the accompanying claims.

[0443] In this description, several preferred embodiments were discussed. Persons skilled in the ad will, undoubtedly, have other ideas as to how the systems and methods described herein may be used. It is understood that this broad invention is not limited to the embodiments discussed herein. Rather, the invention is limited only by the following claims.

[0444] The aspects of the invention are intended to be separable and may be implemented in combination, sub-combination, and with various permutations of embodiments. Therefore, the various disclosure herein, including that which is represented by acknowledged prior art, may be combined, sub-combined and permuted in accordance with the teachings hereof, without departing from the spirit and scope of the invention. All references and information sources cited herein are expressly incorporated herein by reference in their entirety. [0445] Each reference is expressly incorporated herein by reference in its entirety