This paper presents the Neurotechnology Integration Model (NIM), a closed-loop neurocognitive architecture designed to facilitate the stabilization of non-representational awareness states while preserving user autonomy and preventing device dependency.<\/p>\n\n\n\n
The model integrates:<\/p>\n\n\n\n
The central principle is that neurotechnology must function as a transitional scaffold<\/strong>, not as a substitute for internal stabilization capacity.<\/p>\n\n\n\n\n\n\n\n2. Theoretical Foundations<\/h1>\n\n\n\n2.1 Core Hypothesis<\/h2>\n\n\n\nLet:<\/p>\n\n\n\n\nA<\/mi>(<\/mo>t<\/mi>)<\/mo><\/mrow>A(t)<\/annotation><\/semantics><\/math>A(t) = Stabilized non-representational awareness<\/li>\n\n\n\nC<\/mi>(<\/mo>t<\/mi>)<\/mo><\/mrow>C(t)<\/annotation><\/semantics><\/math>C(t) = Cross-network neural coherence<\/li>\n\n\n\nD<\/mi>(<\/mo>t<\/mi>)<\/mo><\/mrow>D(t)<\/annotation><\/semantics><\/math>D(t) = Narrative self-dominance (DMN proxy)<\/li>\n\n\n\nR<\/mi>(<\/mo>t<\/mi>)<\/mo><\/mrow>R(t)<\/annotation><\/semantics><\/math>R(t) = Representational mediation level<\/li>\n\n\n\nU<\/mi>(<\/mo>t<\/mi>)<\/mo><\/mrow>U(t)<\/annotation><\/semantics><\/math>U(t) = User autonomy<\/li>\n<\/ul>\n\n\n\nInternal realization occurs when:C<\/mi>(<\/mo>t<\/mi>)<\/mo>\u226b<\/mo>D<\/mi>(<\/mo>t<\/mi>)<\/mo>,<\/mo>R<\/mi>(<\/mo>t<\/mi>)<\/mo><\/mrow>C(t) \\gg D(t), R(t)<\/annotation><\/semantics><\/math>C(t)\u226bD(t),R(t)<\/p>\n\n\n\nwith:d<\/mi>U<\/mi><\/mrow>d<\/mi>t<\/mi><\/mrow><\/mfrac>><\/mo>0<\/mn><\/mrow>\\frac{dU}{dt} > 0<\/annotation><\/semantics><\/math>dtdU\u200b>0<\/p>\n\n\n\nThe system must increase coherence while decreasing narrative dominance, without reducing autonomy.<\/p>\n\n\n\n\n\n\n\n3. System Architecture<\/h1>\n\n\n\n3.1 High-Level Architecture<\/h2>\n\n\n\n[ User ] \u2193[ Sensing Layer ] \u2193[ State Inference Engine ] \u2193[ Intervention Controller ] \u2193[ Feedback + Stimulation ] \u2193[ User Adaptation ] \u21ba (closed loop)<\/pre>\n\n\n\n\n\n\n\n4. Layer-by-Layer Technical Specification<\/h1>\n\n\n\n\n\n\n\n4.1 Sensing Layer<\/h2>\n\n\n\nInputs<\/h3>\n\n\n\n\nEEG (8\u201332 channels recommended)\n\nSpectral power (\u03b4, \u03b8, \u03b1, \u03b2, \u03b3)<\/li>\n\n\n\nPhase coherence<\/li>\n\n\n\nEntropy metrics<\/li>\n<\/ul>\n<\/li>\n\n\n\nHRV (RMSSD, LF\/HF ratio)<\/li>\n\n\n\nRespiration rate + variability<\/li>\n\n\n\nOptional:\n\nEDA<\/li>\n\n\n\nPupillometry<\/li>\n\n\n\nAccelerometer (movement suppression proxy)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\nOutput Vector<\/h3>\n\n\n\nX<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>E<\/mi>E<\/mi>G<\/mi>s<\/mi><\/msub>,<\/mo>E<\/mi>E<\/mi>G<\/mi>c<\/mi><\/msub>,<\/mo>H<\/mi>R<\/mi>V<\/mi>,<\/mo>R<\/mi>e<\/mi>s<\/mi>p<\/mi>,<\/mo>E<\/mi>D<\/mi>A<\/mi>}<\/mo><\/mrow>X(t) = \\{EEG_s, EEG_c, HRV, Resp, EDA\\}<\/annotation><\/semantics><\/math>X(t)={EEGs\u200b,EEGc\u200b,HRV,Resp,EDA}<\/p>\n\n\n\n\n\n\n\n4.2 State Inference Engine<\/h2>\n\n\n\nDefines latent state vector:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>C<\/mi>^<\/mo><\/mover>,<\/mo>D<\/mi>^<\/mo><\/mover>,<\/mo>T<\/mi>^<\/mo><\/mover>,<\/mo>A<\/mi>^<\/mo><\/mover>,<\/mo>U<\/mi>^<\/mo><\/mover>}<\/mo><\/mrow>S(t) = \\{\\hat{C}, \\hat{D}, \\hat{T}, \\hat{A}, \\hat{U}\\}<\/annotation><\/semantics><\/math>S(t)={C^,D^,T^,A^,U^}<\/p>\n\n\n\nWhere:<\/p>\n\n\n\n\nC<\/mi>^<\/mo><\/mover><\/mrow>\\hat{C}<\/annotation><\/semantics><\/math>C^ = coherence index<\/li>\n\n\n\nD<\/mi>^<\/mo><\/mover><\/mrow>\\hat{D}<\/annotation><\/semantics><\/math>D^ = DMN dominance proxy<\/li>\n\n\n\nT<\/mi>^<\/mo><\/mover><\/mrow>\\hat{T}<\/annotation><\/semantics><\/math>T^ = physiological instability (\u201ctemperature\u201d)<\/li>\n\n\n\nA<\/mi>^<\/mo><\/mover><\/mrow>\\hat{A}<\/annotation><\/semantics><\/math>A^ = awareness stabilization score<\/li>\n\n\n\nU<\/mi>^<\/mo><\/mover><\/mrow>\\hat{U}<\/annotation><\/semantics><\/math>U^ = autonomy index<\/li>\n<\/ul>\n\n\n\nInference model:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>F<\/mi>\u03b8<\/mi><\/msub>(<\/mo>X<\/mi>(<\/mo>t<\/mi>)<\/mo>,<\/mo>H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub>)<\/mo><\/mrow>S(t) = \\mathcal{F}_{\\theta}(X(t), H_{user})<\/annotation><\/semantics><\/math>S(t)=F\u03b8\u200b(X(t),Huser\u200b)<\/p>\n\n\n\nWhere H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub><\/mrow>H_{user}<\/annotation><\/semantics><\/math>Huser\u200b = personalized calibration history.<\/p>\n\n\n\nModel types:<\/p>\n\n\n\n\nBayesian state estimation<\/li>\n\n\n\nLSTM recurrent model<\/li>\n\n\n\nKalman filter for stability tracking<\/li>\n<\/ul>\n\n\n\n\n\n\n\n4.3 Intervention Controller<\/h2>\n\n\n\nControl law:u<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>\u03c0<\/mi>
Let:<\/p>\n\n\n\n
Internal realization occurs when:C<\/mi>(<\/mo>t<\/mi>)<\/mo>\u226b<\/mo>D<\/mi>(<\/mo>t<\/mi>)<\/mo>,<\/mo>R<\/mi>(<\/mo>t<\/mi>)<\/mo><\/mrow>C(t) \\gg D(t), R(t)<\/annotation><\/semantics><\/math>C(t)\u226bD(t),R(t)<\/p>\n\n\n\nwith:d<\/mi>U<\/mi><\/mrow>d<\/mi>t<\/mi><\/mrow><\/mfrac>><\/mo>0<\/mn><\/mrow>\\frac{dU}{dt} > 0<\/annotation><\/semantics><\/math>dtdU\u200b>0<\/p>\n\n\n\nThe system must increase coherence while decreasing narrative dominance, without reducing autonomy.<\/p>\n\n\n\n\n\n\n\n3. System Architecture<\/h1>\n\n\n\n3.1 High-Level Architecture<\/h2>\n\n\n\n[ User ] \u2193[ Sensing Layer ] \u2193[ State Inference Engine ] \u2193[ Intervention Controller ] \u2193[ Feedback + Stimulation ] \u2193[ User Adaptation ] \u21ba (closed loop)<\/pre>\n\n\n\n\n\n\n\n4. Layer-by-Layer Technical Specification<\/h1>\n\n\n\n\n\n\n\n4.1 Sensing Layer<\/h2>\n\n\n\nInputs<\/h3>\n\n\n\n\nEEG (8\u201332 channels recommended)\n\nSpectral power (\u03b4, \u03b8, \u03b1, \u03b2, \u03b3)<\/li>\n\n\n\nPhase coherence<\/li>\n\n\n\nEntropy metrics<\/li>\n<\/ul>\n<\/li>\n\n\n\nHRV (RMSSD, LF\/HF ratio)<\/li>\n\n\n\nRespiration rate + variability<\/li>\n\n\n\nOptional:\n\nEDA<\/li>\n\n\n\nPupillometry<\/li>\n\n\n\nAccelerometer (movement suppression proxy)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\nOutput Vector<\/h3>\n\n\n\nX<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>E<\/mi>E<\/mi>G<\/mi>s<\/mi><\/msub>,<\/mo>E<\/mi>E<\/mi>G<\/mi>c<\/mi><\/msub>,<\/mo>H<\/mi>R<\/mi>V<\/mi>,<\/mo>R<\/mi>e<\/mi>s<\/mi>p<\/mi>,<\/mo>E<\/mi>D<\/mi>A<\/mi>}<\/mo><\/mrow>X(t) = \\{EEG_s, EEG_c, HRV, Resp, EDA\\}<\/annotation><\/semantics><\/math>X(t)={EEGs\u200b,EEGc\u200b,HRV,Resp,EDA}<\/p>\n\n\n\n\n\n\n\n4.2 State Inference Engine<\/h2>\n\n\n\nDefines latent state vector:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>C<\/mi>^<\/mo><\/mover>,<\/mo>D<\/mi>^<\/mo><\/mover>,<\/mo>T<\/mi>^<\/mo><\/mover>,<\/mo>A<\/mi>^<\/mo><\/mover>,<\/mo>U<\/mi>^<\/mo><\/mover>}<\/mo><\/mrow>S(t) = \\{\\hat{C}, \\hat{D}, \\hat{T}, \\hat{A}, \\hat{U}\\}<\/annotation><\/semantics><\/math>S(t)={C^,D^,T^,A^,U^}<\/p>\n\n\n\nWhere:<\/p>\n\n\n\n\nC<\/mi>^<\/mo><\/mover><\/mrow>\\hat{C}<\/annotation><\/semantics><\/math>C^ = coherence index<\/li>\n\n\n\nD<\/mi>^<\/mo><\/mover><\/mrow>\\hat{D}<\/annotation><\/semantics><\/math>D^ = DMN dominance proxy<\/li>\n\n\n\nT<\/mi>^<\/mo><\/mover><\/mrow>\\hat{T}<\/annotation><\/semantics><\/math>T^ = physiological instability (\u201ctemperature\u201d)<\/li>\n\n\n\nA<\/mi>^<\/mo><\/mover><\/mrow>\\hat{A}<\/annotation><\/semantics><\/math>A^ = awareness stabilization score<\/li>\n\n\n\nU<\/mi>^<\/mo><\/mover><\/mrow>\\hat{U}<\/annotation><\/semantics><\/math>U^ = autonomy index<\/li>\n<\/ul>\n\n\n\nInference model:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>F<\/mi>\u03b8<\/mi><\/msub>(<\/mo>X<\/mi>(<\/mo>t<\/mi>)<\/mo>,<\/mo>H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub>)<\/mo><\/mrow>S(t) = \\mathcal{F}_{\\theta}(X(t), H_{user})<\/annotation><\/semantics><\/math>S(t)=F\u03b8\u200b(X(t),Huser\u200b)<\/p>\n\n\n\nWhere H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub><\/mrow>H_{user}<\/annotation><\/semantics><\/math>Huser\u200b = personalized calibration history.<\/p>\n\n\n\nModel types:<\/p>\n\n\n\n\nBayesian state estimation<\/li>\n\n\n\nLSTM recurrent model<\/li>\n\n\n\nKalman filter for stability tracking<\/li>\n<\/ul>\n\n\n\n\n\n\n\n4.3 Intervention Controller<\/h2>\n\n\n\nControl law:u<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>\u03c0<\/mi>
with:d<\/mi>U<\/mi><\/mrow>d<\/mi>t<\/mi><\/mrow><\/mfrac>><\/mo>0<\/mn><\/mrow>\\frac{dU}{dt} > 0<\/annotation><\/semantics><\/math>dtdU\u200b>0<\/p>\n\n\n\nThe system must increase coherence while decreasing narrative dominance, without reducing autonomy.<\/p>\n\n\n\n\n\n\n\n3. System Architecture<\/h1>\n\n\n\n3.1 High-Level Architecture<\/h2>\n\n\n\n[ User ] \u2193[ Sensing Layer ] \u2193[ State Inference Engine ] \u2193[ Intervention Controller ] \u2193[ Feedback + Stimulation ] \u2193[ User Adaptation ] \u21ba (closed loop)<\/pre>\n\n\n\n\n\n\n\n4. Layer-by-Layer Technical Specification<\/h1>\n\n\n\n\n\n\n\n4.1 Sensing Layer<\/h2>\n\n\n\nInputs<\/h3>\n\n\n\n\nEEG (8\u201332 channels recommended)\n\nSpectral power (\u03b4, \u03b8, \u03b1, \u03b2, \u03b3)<\/li>\n\n\n\nPhase coherence<\/li>\n\n\n\nEntropy metrics<\/li>\n<\/ul>\n<\/li>\n\n\n\nHRV (RMSSD, LF\/HF ratio)<\/li>\n\n\n\nRespiration rate + variability<\/li>\n\n\n\nOptional:\n\nEDA<\/li>\n\n\n\nPupillometry<\/li>\n\n\n\nAccelerometer (movement suppression proxy)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\nOutput Vector<\/h3>\n\n\n\nX<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>E<\/mi>E<\/mi>G<\/mi>s<\/mi><\/msub>,<\/mo>E<\/mi>E<\/mi>G<\/mi>c<\/mi><\/msub>,<\/mo>H<\/mi>R<\/mi>V<\/mi>,<\/mo>R<\/mi>e<\/mi>s<\/mi>p<\/mi>,<\/mo>E<\/mi>D<\/mi>A<\/mi>}<\/mo><\/mrow>X(t) = \\{EEG_s, EEG_c, HRV, Resp, EDA\\}<\/annotation><\/semantics><\/math>X(t)={EEGs\u200b,EEGc\u200b,HRV,Resp,EDA}<\/p>\n\n\n\n\n\n\n\n4.2 State Inference Engine<\/h2>\n\n\n\nDefines latent state vector:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>C<\/mi>^<\/mo><\/mover>,<\/mo>D<\/mi>^<\/mo><\/mover>,<\/mo>T<\/mi>^<\/mo><\/mover>,<\/mo>A<\/mi>^<\/mo><\/mover>,<\/mo>U<\/mi>^<\/mo><\/mover>}<\/mo><\/mrow>S(t) = \\{\\hat{C}, \\hat{D}, \\hat{T}, \\hat{A}, \\hat{U}\\}<\/annotation><\/semantics><\/math>S(t)={C^,D^,T^,A^,U^}<\/p>\n\n\n\nWhere:<\/p>\n\n\n\n\nC<\/mi>^<\/mo><\/mover><\/mrow>\\hat{C}<\/annotation><\/semantics><\/math>C^ = coherence index<\/li>\n\n\n\nD<\/mi>^<\/mo><\/mover><\/mrow>\\hat{D}<\/annotation><\/semantics><\/math>D^ = DMN dominance proxy<\/li>\n\n\n\nT<\/mi>^<\/mo><\/mover><\/mrow>\\hat{T}<\/annotation><\/semantics><\/math>T^ = physiological instability (\u201ctemperature\u201d)<\/li>\n\n\n\nA<\/mi>^<\/mo><\/mover><\/mrow>\\hat{A}<\/annotation><\/semantics><\/math>A^ = awareness stabilization score<\/li>\n\n\n\nU<\/mi>^<\/mo><\/mover><\/mrow>\\hat{U}<\/annotation><\/semantics><\/math>U^ = autonomy index<\/li>\n<\/ul>\n\n\n\nInference model:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>F<\/mi>\u03b8<\/mi><\/msub>(<\/mo>X<\/mi>(<\/mo>t<\/mi>)<\/mo>,<\/mo>H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub>)<\/mo><\/mrow>S(t) = \\mathcal{F}_{\\theta}(X(t), H_{user})<\/annotation><\/semantics><\/math>S(t)=F\u03b8\u200b(X(t),Huser\u200b)<\/p>\n\n\n\nWhere H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub><\/mrow>H_{user}<\/annotation><\/semantics><\/math>Huser\u200b = personalized calibration history.<\/p>\n\n\n\nModel types:<\/p>\n\n\n\n\nBayesian state estimation<\/li>\n\n\n\nLSTM recurrent model<\/li>\n\n\n\nKalman filter for stability tracking<\/li>\n<\/ul>\n\n\n\n\n\n\n\n4.3 Intervention Controller<\/h2>\n\n\n\nControl law:u<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>\u03c0<\/mi>
The system must increase coherence while decreasing narrative dominance, without reducing autonomy.<\/p>\n\n\n\n
[ User ] \u2193[ Sensing Layer ] \u2193[ State Inference Engine ] \u2193[ Intervention Controller ] \u2193[ Feedback + Stimulation ] \u2193[ User Adaptation ] \u21ba (closed loop)<\/pre>\n\n\n\n\n\n\n\n4. Layer-by-Layer Technical Specification<\/h1>\n\n\n\n\n\n\n\n4.1 Sensing Layer<\/h2>\n\n\n\nInputs<\/h3>\n\n\n\n\nEEG (8\u201332 channels recommended)\n\nSpectral power (\u03b4, \u03b8, \u03b1, \u03b2, \u03b3)<\/li>\n\n\n\nPhase coherence<\/li>\n\n\n\nEntropy metrics<\/li>\n<\/ul>\n<\/li>\n\n\n\nHRV (RMSSD, LF\/HF ratio)<\/li>\n\n\n\nRespiration rate + variability<\/li>\n\n\n\nOptional:\n\nEDA<\/li>\n\n\n\nPupillometry<\/li>\n\n\n\nAccelerometer (movement suppression proxy)<\/li>\n<\/ul>\n<\/li>\n<\/ul>\n\n\n\nOutput Vector<\/h3>\n\n\n\nX<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>E<\/mi>E<\/mi>G<\/mi>s<\/mi><\/msub>,<\/mo>E<\/mi>E<\/mi>G<\/mi>c<\/mi><\/msub>,<\/mo>H<\/mi>R<\/mi>V<\/mi>,<\/mo>R<\/mi>e<\/mi>s<\/mi>p<\/mi>,<\/mo>E<\/mi>D<\/mi>A<\/mi>}<\/mo><\/mrow>X(t) = \\{EEG_s, EEG_c, HRV, Resp, EDA\\}<\/annotation><\/semantics><\/math>X(t)={EEGs\u200b,EEGc\u200b,HRV,Resp,EDA}<\/p>\n\n\n\n\n\n\n\n4.2 State Inference Engine<\/h2>\n\n\n\nDefines latent state vector:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>C<\/mi>^<\/mo><\/mover>,<\/mo>D<\/mi>^<\/mo><\/mover>,<\/mo>T<\/mi>^<\/mo><\/mover>,<\/mo>A<\/mi>^<\/mo><\/mover>,<\/mo>U<\/mi>^<\/mo><\/mover>}<\/mo><\/mrow>S(t) = \\{\\hat{C}, \\hat{D}, \\hat{T}, \\hat{A}, \\hat{U}\\}<\/annotation><\/semantics><\/math>S(t)={C^,D^,T^,A^,U^}<\/p>\n\n\n\nWhere:<\/p>\n\n\n\n\nC<\/mi>^<\/mo><\/mover><\/mrow>\\hat{C}<\/annotation><\/semantics><\/math>C^ = coherence index<\/li>\n\n\n\nD<\/mi>^<\/mo><\/mover><\/mrow>\\hat{D}<\/annotation><\/semantics><\/math>D^ = DMN dominance proxy<\/li>\n\n\n\nT<\/mi>^<\/mo><\/mover><\/mrow>\\hat{T}<\/annotation><\/semantics><\/math>T^ = physiological instability (\u201ctemperature\u201d)<\/li>\n\n\n\nA<\/mi>^<\/mo><\/mover><\/mrow>\\hat{A}<\/annotation><\/semantics><\/math>A^ = awareness stabilization score<\/li>\n\n\n\nU<\/mi>^<\/mo><\/mover><\/mrow>\\hat{U}<\/annotation><\/semantics><\/math>U^ = autonomy index<\/li>\n<\/ul>\n\n\n\nInference model:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>F<\/mi>\u03b8<\/mi><\/msub>(<\/mo>X<\/mi>(<\/mo>t<\/mi>)<\/mo>,<\/mo>H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub>)<\/mo><\/mrow>S(t) = \\mathcal{F}_{\\theta}(X(t), H_{user})<\/annotation><\/semantics><\/math>S(t)=F\u03b8\u200b(X(t),Huser\u200b)<\/p>\n\n\n\nWhere H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub><\/mrow>H_{user}<\/annotation><\/semantics><\/math>Huser\u200b = personalized calibration history.<\/p>\n\n\n\nModel types:<\/p>\n\n\n\n\nBayesian state estimation<\/li>\n\n\n\nLSTM recurrent model<\/li>\n\n\n\nKalman filter for stability tracking<\/li>\n<\/ul>\n\n\n\n\n\n\n\n4.3 Intervention Controller<\/h2>\n\n\n\nControl law:u<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>\u03c0<\/mi>
X<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>E<\/mi>E<\/mi>G<\/mi>s<\/mi><\/msub>,<\/mo>E<\/mi>E<\/mi>G<\/mi>c<\/mi><\/msub>,<\/mo>H<\/mi>R<\/mi>V<\/mi>,<\/mo>R<\/mi>e<\/mi>s<\/mi>p<\/mi>,<\/mo>E<\/mi>D<\/mi>A<\/mi>}<\/mo><\/mrow>X(t) = \\{EEG_s, EEG_c, HRV, Resp, EDA\\}<\/annotation><\/semantics><\/math>X(t)={EEGs\u200b,EEGc\u200b,HRV,Resp,EDA}<\/p>\n\n\n\n\n\n\n\n4.2 State Inference Engine<\/h2>\n\n\n\nDefines latent state vector:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>C<\/mi>^<\/mo><\/mover>,<\/mo>D<\/mi>^<\/mo><\/mover>,<\/mo>T<\/mi>^<\/mo><\/mover>,<\/mo>A<\/mi>^<\/mo><\/mover>,<\/mo>U<\/mi>^<\/mo><\/mover>}<\/mo><\/mrow>S(t) = \\{\\hat{C}, \\hat{D}, \\hat{T}, \\hat{A}, \\hat{U}\\}<\/annotation><\/semantics><\/math>S(t)={C^,D^,T^,A^,U^}<\/p>\n\n\n\nWhere:<\/p>\n\n\n\n\nC<\/mi>^<\/mo><\/mover><\/mrow>\\hat{C}<\/annotation><\/semantics><\/math>C^ = coherence index<\/li>\n\n\n\nD<\/mi>^<\/mo><\/mover><\/mrow>\\hat{D}<\/annotation><\/semantics><\/math>D^ = DMN dominance proxy<\/li>\n\n\n\nT<\/mi>^<\/mo><\/mover><\/mrow>\\hat{T}<\/annotation><\/semantics><\/math>T^ = physiological instability (\u201ctemperature\u201d)<\/li>\n\n\n\nA<\/mi>^<\/mo><\/mover><\/mrow>\\hat{A}<\/annotation><\/semantics><\/math>A^ = awareness stabilization score<\/li>\n\n\n\nU<\/mi>^<\/mo><\/mover><\/mrow>\\hat{U}<\/annotation><\/semantics><\/math>U^ = autonomy index<\/li>\n<\/ul>\n\n\n\nInference model:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>F<\/mi>\u03b8<\/mi><\/msub>(<\/mo>X<\/mi>(<\/mo>t<\/mi>)<\/mo>,<\/mo>H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub>)<\/mo><\/mrow>S(t) = \\mathcal{F}_{\\theta}(X(t), H_{user})<\/annotation><\/semantics><\/math>S(t)=F\u03b8\u200b(X(t),Huser\u200b)<\/p>\n\n\n\nWhere H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub><\/mrow>H_{user}<\/annotation><\/semantics><\/math>Huser\u200b = personalized calibration history.<\/p>\n\n\n\nModel types:<\/p>\n\n\n\n\nBayesian state estimation<\/li>\n\n\n\nLSTM recurrent model<\/li>\n\n\n\nKalman filter for stability tracking<\/li>\n<\/ul>\n\n\n\n\n\n\n\n4.3 Intervention Controller<\/h2>\n\n\n\nControl law:u<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>\u03c0<\/mi>
Defines latent state vector:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>{<\/mo>C<\/mi>^<\/mo><\/mover>,<\/mo>D<\/mi>^<\/mo><\/mover>,<\/mo>T<\/mi>^<\/mo><\/mover>,<\/mo>A<\/mi>^<\/mo><\/mover>,<\/mo>U<\/mi>^<\/mo><\/mover>}<\/mo><\/mrow>S(t) = \\{\\hat{C}, \\hat{D}, \\hat{T}, \\hat{A}, \\hat{U}\\}<\/annotation><\/semantics><\/math>S(t)={C^,D^,T^,A^,U^}<\/p>\n\n\n\nWhere:<\/p>\n\n\n\n\nC<\/mi>^<\/mo><\/mover><\/mrow>\\hat{C}<\/annotation><\/semantics><\/math>C^ = coherence index<\/li>\n\n\n\nD<\/mi>^<\/mo><\/mover><\/mrow>\\hat{D}<\/annotation><\/semantics><\/math>D^ = DMN dominance proxy<\/li>\n\n\n\nT<\/mi>^<\/mo><\/mover><\/mrow>\\hat{T}<\/annotation><\/semantics><\/math>T^ = physiological instability (\u201ctemperature\u201d)<\/li>\n\n\n\nA<\/mi>^<\/mo><\/mover><\/mrow>\\hat{A}<\/annotation><\/semantics><\/math>A^ = awareness stabilization score<\/li>\n\n\n\nU<\/mi>^<\/mo><\/mover><\/mrow>\\hat{U}<\/annotation><\/semantics><\/math>U^ = autonomy index<\/li>\n<\/ul>\n\n\n\nInference model:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>F<\/mi>\u03b8<\/mi><\/msub>(<\/mo>X<\/mi>(<\/mo>t<\/mi>)<\/mo>,<\/mo>H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub>)<\/mo><\/mrow>S(t) = \\mathcal{F}_{\\theta}(X(t), H_{user})<\/annotation><\/semantics><\/math>S(t)=F\u03b8\u200b(X(t),Huser\u200b)<\/p>\n\n\n\nWhere H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub><\/mrow>H_{user}<\/annotation><\/semantics><\/math>Huser\u200b = personalized calibration history.<\/p>\n\n\n\nModel types:<\/p>\n\n\n\n\nBayesian state estimation<\/li>\n\n\n\nLSTM recurrent model<\/li>\n\n\n\nKalman filter for stability tracking<\/li>\n<\/ul>\n\n\n\n\n\n\n\n4.3 Intervention Controller<\/h2>\n\n\n\nControl law:u<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>\u03c0<\/mi>
Where:<\/p>\n\n\n\n
Inference model:S<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>F<\/mi>\u03b8<\/mi><\/msub>(<\/mo>X<\/mi>(<\/mo>t<\/mi>)<\/mo>,<\/mo>H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub>)<\/mo><\/mrow>S(t) = \\mathcal{F}_{\\theta}(X(t), H_{user})<\/annotation><\/semantics><\/math>S(t)=F\u03b8\u200b(X(t),Huser\u200b)<\/p>\n\n\n\nWhere H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub><\/mrow>H_{user}<\/annotation><\/semantics><\/math>Huser\u200b = personalized calibration history.<\/p>\n\n\n\nModel types:<\/p>\n\n\n\n\nBayesian state estimation<\/li>\n\n\n\nLSTM recurrent model<\/li>\n\n\n\nKalman filter for stability tracking<\/li>\n<\/ul>\n\n\n\n\n\n\n\n4.3 Intervention Controller<\/h2>\n\n\n\nControl law:u<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>\u03c0<\/mi>
Where H<\/mi>u<\/mi>s<\/mi>e<\/mi>r<\/mi><\/mrow><\/msub><\/mrow>H_{user}<\/annotation><\/semantics><\/math>Huser\u200b = personalized calibration history.<\/p>\n\n\n\nModel types:<\/p>\n\n\n\n\nBayesian state estimation<\/li>\n\n\n\nLSTM recurrent model<\/li>\n\n\n\nKalman filter for stability tracking<\/li>\n<\/ul>\n\n\n\n\n\n\n\n4.3 Intervention Controller<\/h2>\n\n\n\nControl law:u<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>\u03c0<\/mi>
Model types:<\/p>\n\n\n\n
Control law:u<\/mi>(<\/mo>t<\/mi>)<\/mo>=<\/mo>\u03c0<\/mi>