sss.cpp

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//=============================================================================================================
 
//=============================================================================================================
// INCLUDES
//=============================================================================================================
 
#include "sss.h"
 
//=============================================================================================================
// FIFF INCLUDES
//=============================================================================================================
 
#include <fiff/fiff_constants.h>
#include <fiff/fiff_ch_info.h>
 
//=============================================================================================================
// EIGEN INCLUDES
//=============================================================================================================
 
#include <Eigen/Dense>
#include <Eigen/SVD>
 
//=============================================================================================================
// QT INCLUDES
//=============================================================================================================
 
#include <QDebug>
 
//=============================================================================================================
// C++ INCLUDES
//=============================================================================================================
 
#include <cmath>
 
//=============================================================================================================
// USED NAMESPACES
//=============================================================================================================
 
using namespace UTILSLIB;
using namespace FIFFLIB;
using namespace Eigen;
 
//=============================================================================================================
// CONSTANTS
//=============================================================================================================
 
static constexpr double SSS_PI = M_PI;
 
//=============================================================================================================
// PRIVATE HELPERS
//=============================================================================================================
 
namespace
{
 
//=============================================================================================================
MatrixXd regPinv(const MatrixXd& A, double reg = 1e-5)
{
    JacobiSVD<MatrixXd> svd(A, ComputeThinU | ComputeThinV);
    const VectorXd& sv = svd.singularValues();
    double threshold = reg * sv(0);
    VectorXd invSv = sv;
    for (int i = 0; i < invSv.size(); ++i) {
        invSv(i) = (sv(i) > threshold) ? 1.0 / sv(i) : 0.0;
    }
    return svd.matrixV() * invSv.asDiagonal() * svd.matrixU().transpose();
}
 
} // anonymous namespace
 
//=============================================================================================================
// SSS MEMBER DEFINITIONS
//=============================================================================================================
 
void SSS::computeNormALP(int lmax, double cosTheta, double sinTheta,
                         MatrixXd& P, MatrixXd& dP)
{
    // Guard against pole singularity
    if (sinTheta < 1e-12) {
        sinTheta = 1e-12;
    }
 
    const int sz = lmax + 2;
    P.resize(sz, sz);
    dP.resize(sz, sz);
    P.setZero();
    dP.setZero();
 
    // ---- Build un-normalised ALPs using standard recurrence ----
    // P_0^0 = 1
    // P_1^0 = cos θ
    // P_1^1 = -sin θ
    // P_l^l = -(2l-1) * sin θ * P_{l-1}^{l-1}
    // P_l^{l-1} = (2l-1) * cos θ * P_{l-1}^{l-1}
    // P_l^m = [(2l-1)*cos θ * P_{l-1}^m - (l-1+m)*P_{l-2}^m] / (l-m)
 
    // Temporary table (un-normalised)
    MatrixXd Praw(sz, sz);
    Praw.setZero();
    Praw(0, 0) = 1.0;
    if (lmax >= 1) {
        Praw(1, 0) = cosTheta;
        Praw(1, 1) = -sinTheta;
    }
    for (int l = 2; l <= lmax + 1; ++l) {
        Praw(l, l) = -(2 * l - 1) * sinTheta * Praw(l - 1, l - 1);
        Praw(l, l - 1) = (2 * l - 1) * cosTheta * Praw(l - 1, l - 1);
        for (int m = 0; m <= l - 2; ++m) {
            Praw(l, m) = ((2 * l - 1) * cosTheta * Praw(l - 1, m) - (l - 1 + m) * Praw(l - 2, m)) / static_cast<double>(l - m);
        }
    }
 
    // ---- Apply 4π-normalisation and compute dP/dθ ----
    for (int l = 1; l <= lmax; ++l) {
        for (int m = 0; m <= l; ++m) {
            // Normalisation factor N_l^m
            // (l-m)! / (l+m)!
            double fac = 1.0;
            for (int k = l - m + 1; k <= l + m; ++k) {
                fac /= static_cast<double>(k);
            }
            double norm = (m == 0)
                ? std::sqrt((2.0 * l + 1.0) / (4.0 * SSS_PI) * fac)
                : std::sqrt(2.0 * (2.0 * l + 1.0) / (4.0 * SSS_PI) * fac);
 
            P(l, m) = norm * Praw(l, m);
 
            // dP_l^m/dθ using:
            //   sin θ * dP_l^m/dθ = l * cos θ * P_l^m - (l+m) * P_{l-1}^m
            // (applied to un-normalised P, then multiplied by norm / sinθ)
            double sinThetaDeriv = static_cast<double>(l) * cosTheta * Praw(l, m);
            if (l - 1 >= m) {
                sinThetaDeriv -= static_cast<double>(l + m) * Praw(l - 1, m);
            }
            dP(l, m) = norm * sinThetaDeriv / sinTheta;
        }
    }
}
 
//=============================================================================================================
 
Vector3d SSS::basisGradCart(int l, int m, bool bInternal,
                            const Vector3d& rPos,
                            const MatrixXd& P, const MatrixXd& dP,
                            double cosTheta, double sinTheta,
                            double cosPhi, double sinPhi)
{
    // Precompute |r| and its powers
    double r = rPos.norm();
    if (r < 1e-12) {
        return Vector3d::Zero();
    }
 
    const int absM = std::abs(m);
 
    // ---- Y_l^m and its derivatives ----
    //
    // Y_l^m (real):
    //   m  = 0 : N_l^0 * P_l^0(cos θ)
    //   m  > 0 : N_l^m * P_l^m(cos θ) * cos(m φ)
    //   m  < 0 : N_l^|m| * P_l^|m|(cos θ) * sin(|m| φ)
    //
    // Derivatives:
    //   dY/dθ:  N * dP/dθ * {1, cos(mφ), sin(|m|φ)}
    //   dY/dφ:  N * P * {0, -m sin(mφ), |m| cos(|m|φ)}
 
    double Plm = P(l, absM);   // normalised, at (θ,φ)
    double dPlm = dP(l, absM); // normalised dP/dθ
 
    double angFactor, dAngFactor_phi;
    if (m == 0) {
        angFactor = 1.0;
        dAngFactor_phi = 0.0;
    } else if (m > 0) {
        double cosmPhi = std::cos(static_cast<double>(m) * std::atan2(sinPhi, cosPhi));
        double sinmPhi = std::sin(static_cast<double>(m) * std::atan2(sinPhi, cosPhi));
        angFactor = cosmPhi;
        dAngFactor_phi = -static_cast<double>(m) * sinmPhi;
    } else {
        // m < 0
        double cosmPhi = std::cos(static_cast<double>(absM) * std::atan2(sinPhi, cosPhi));
        double sinmPhi = std::sin(static_cast<double>(absM) * std::atan2(sinPhi, cosPhi));
        angFactor = sinmPhi;
        dAngFactor_phi = static_cast<double>(absM) * cosmPhi;
    }
 
    // Y_l^m at this point
    double Ylm = Plm * angFactor;
 
    // dY_l^m/dθ = dP/dθ * angFactor   (for m=0 angFactor=1, for m≠0 it's cos/sin but θ-independent)
    double dYdTheta = dPlm * angFactor;
 
    // dY_l^m/dφ  =  P_l^|m| * dAngFactor/dφ
    double dYdPhi = Plm * dAngFactor_phi;
 
    // ---- Gradient in spherical coordinates ----
    // Internal (sources inside):  grad(r^{-(l+1)} Y_l^m)
    //   G_r = -(l+1) r^{-(l+2)} Y,  G_θ = r^{-(l+2)} dY/dθ,  G_φ = r^{-(l+2)} / sinθ dY/dφ
    // External (sources outside): grad(r^l Y_l^m)
    //   G_r = l r^{l-1} Y,          G_θ = r^{l-1} dY/dθ,      G_φ = r^{l-1} / sinθ dY/dφ
 
    double radPow, Gr_coeff, Gtu_coeff;
    if (bInternal) {
        radPow = std::pow(r, -(l + 2));
        Gr_coeff = -static_cast<double>(l + 1) * radPow;
        Gtu_coeff = radPow; // G_θ and sinθ*G_φ share this
    } else {
        radPow = std::pow(r, l - 1);
        Gr_coeff = static_cast<double>(l) * radPow;
        Gtu_coeff = radPow;
    }
 
    double Gr = Gr_coeff * Ylm;
    double Gtheta = Gtu_coeff * dYdTheta;
    double GphiTimesSin = Gtu_coeff * dYdPhi; // = G_φ * sinθ (avoids 1/sinθ singularity)
 
    // ---- Convert (Gr, Gθ, sinθ * Gφ) to Cartesian ----
    // r̂  = (sinθ cosφ,  sinθ sinφ,  cosθ)
    // θ̂  = (cosθ cosφ,  cosθ sinφ, -sinθ)
    // φ̂  = (-sinφ,      cosφ,       0   )
    //
    // grad_cart = Gr * r̂ + Gθ * θ̂ + Gφ * φ̂
    //           = Gr * r̂ + Gθ * θ̂ + (sinθ * Gφ) / sinθ * φ̂
    // Numerically we use GphiTimesSin / sinθ for the φ̂ component.
 
    double Gphi = (sinTheta > 1e-12) ? GphiTimesSin / sinTheta : 0.0;
 
    double gx = Gr * sinTheta * cosPhi + Gtheta * cosTheta * cosPhi - Gphi * sinPhi;
    double gy = Gr * sinTheta * sinPhi + Gtheta * cosTheta * sinPhi + Gphi * cosPhi;
    double gz = Gr * cosTheta - Gtheta * sinTheta;
 
    return Vector3d(gx, gy, gz);
}
 
//=============================================================================================================
 
SSS::Basis SSS::computeBasis(const FiffInfo& fiffInfo, const Params& params)
{
    Basis basis;
    basis.iOrderIn = params.iOrderIn;
    basis.iOrderOut = params.iOrderOut;
    basis.iNin = params.iOrderIn * (params.iOrderIn + 2);
    basis.iNout = params.iOrderOut * (params.iOrderOut + 2);
 
    // ---- Collect MEG channel indices and geometry ----
    for (int i = 0; i < fiffInfo.nchan; ++i) {
        int kind = fiffInfo.chs[i].kind;
        if (kind == FIFFV_MEG_CH || kind == FIFFV_REF_MEG_CH) {
            basis.megChannelIdx.append(i);
        }
    }
 
    const int nMeg = basis.megChannelIdx.size();
    if (nMeg == 0) {
        qWarning() << "SSS::computeBasis: no MEG channels found in FiffInfo.";
        return basis;
    }
 
    basis.matSin.resize(nMeg, basis.iNin);
    basis.matSout.resize(nMeg, basis.iNout);
    basis.matSin.setZero();
    basis.matSout.setZero();
 
    const int lmax = std::max(params.iOrderIn, params.iOrderOut);
 
    // ---- Fill basis matrices row-by-row (one sensor per row) ----
    for (int si = 0; si < nMeg; ++si) {
        const FiffChInfo& ch = fiffInfo.chs[basis.megChannelIdx[si]];
 
        // Sensor position relative to SSS origin (metres)
        Vector3d rPos(static_cast<double>(ch.chpos.r0(0)) - params.origin(0),
                      static_cast<double>(ch.chpos.r0(1)) - params.origin(1),
                      static_cast<double>(ch.chpos.r0(2)) - params.origin(2));
 
        // Sensor normal (ez of coil coordinate system)
        Vector3d normal(static_cast<double>(ch.chpos.ez(0)),
                        static_cast<double>(ch.chpos.ez(1)),
                        static_cast<double>(ch.chpos.ez(2)));
 
        // Normalise the normal vector
        double nNorm = normal.norm();
        if (nNorm < 1e-12) {
            continue;
        }
        normal /= nNorm;
 
        // Convert to spherical coordinates
        double r = rPos.norm();
        if (r < 1e-12) {
            continue;
        }
        double cosTheta = rPos(2) / r;
        double sinTheta = std::sqrt(rPos(0) * rPos(0) + rPos(1) * rPos(1)) / r;
        if (sinTheta < 1e-12)
            sinTheta = 1e-12;
        double phi = std::atan2(rPos(1), rPos(0));
        double cosPhi = std::cos(phi);
        double sinPhi = std::sin(phi);
 
        // Normalised ALP tables for this sensor
        MatrixXd P, dP;
        computeNormALP(lmax, cosTheta, sinTheta, P, dP);
 
        // Internal basis columns: iterate (l=1..L_in, m=-l..l)
        int colIn = 0;
        for (int l = 1; l <= params.iOrderIn; ++l) {
            for (int m = -l; m <= l; ++m) {
                Vector3d grad = basisGradCart(l, m, /*internal=*/true,
                                              rPos, P, dP,
                                              cosTheta, sinTheta, cosPhi, sinPhi);
                basis.matSin(si, colIn) = normal.dot(grad);
                ++colIn;
            }
        }
 
        // External basis columns: iterate (l=1..L_out, m=-l..l)
        int colOut = 0;
        for (int l = 1; l <= params.iOrderOut; ++l) {
            for (int m = -l; m <= l; ++m) {
                Vector3d grad = basisGradCart(l, m, /*internal=*/false,
                                              rPos, P, dP,
                                              cosTheta, sinTheta, cosPhi, sinPhi);
                basis.matSout(si, colOut) = normal.dot(grad);
                ++colOut;
            }
        }
    }
 
    // ---- Compute combined pseudoinverse and internal projector ----
    //
    // S = [S_in | S_out]  (n_meg × (N_in + N_out))
    // pinv(S) = V * diag(1/σ_i) * U^T  with Tikhonov regularisation
    // P_in = S_in * pinv(S)[:N_in, :]
    MatrixXd S(nMeg, basis.iNin + basis.iNout);
    S.leftCols(basis.iNin) = basis.matSin;
    S.rightCols(basis.iNout) = basis.matSout;
 
    // Column norms span ~10 orders of magnitude (r^-(l+2) vs r^(l-1)); normalise them before the pseudoinverse, as MNE does
    const VectorXd colNorms = S.colwise().norm().cwiseMax(1e-300).transpose();
    basis.matPinvAll = colNorms.cwiseInverse().asDiagonal() * regPinv(S * colNorms.cwiseInverse().asDiagonal(), params.dRegIn); // (N_in+N_out) × n_meg
    basis.matProjIn = basis.matSin * basis.matPinvAll.topRows(basis.iNin);
 
    return basis;
}
 
//=============================================================================================================
 
MatrixXd SSS::apply(const MatrixXd& matData, const Basis& basis)
{
    if (basis.megChannelIdx.isEmpty()) {
        return matData;
    }
 
    const int nMeg = basis.megChannelIdx.size();
    MatrixXd matOut = matData;
 
    // Extract MEG rows
    MatrixXd megData(nMeg, matData.cols());
    for (int i = 0; i < nMeg; ++i) {
        megData.row(i) = matData.row(basis.megChannelIdx[i]);
    }
 
    // Apply internal projector: data_sss = P_in * data_meg
    MatrixXd megSss = basis.matProjIn * megData;
 
    // Write back
    for (int i = 0; i < nMeg; ++i) {
        matOut.row(basis.megChannelIdx[i]) = megSss.row(i);
    }
 
    return matOut;
}
 
//=============================================================================================================
 
MatrixXd SSS::applyTemporal(const MatrixXd& matData,
                            const Basis& basis,
                            int iBufferLength,
                            double dCorrLimit)
{
    if (basis.megChannelIdx.isEmpty()) {
        return matData;
    }
 
    const int nMeg = basis.megChannelIdx.size();
    const int nSamp = static_cast<int>(matData.cols());
    const int bufLen = std::min(iBufferLength, nSamp);
 
    MatrixXd matOut = matData;
 
    // Extract MEG rows
    MatrixXd megData(nMeg, nSamp);
    for (int i = 0; i < nMeg; ++i) {
        megData.row(i) = matData.row(basis.megChannelIdx[i]);
    }
 
    // Decompose time series into expansion coefficients
    // c_in  (N_in  × nSamp) = pinv_all[:N_in , :] * megData
    // c_out (N_out × nSamp) = pinv_all[N_in: , :] * megData
    MatrixXd cIn = basis.matPinvAll.topRows(basis.iNin) * megData;      // N_in  × nSamp
    MatrixXd cOut = basis.matPinvAll.bottomRows(basis.iNout) * megData; // N_out × nSamp
 
    // Process in sliding windows
    int offset = 0;
    while (offset < nSamp) {
        int winLen = std::min(bufLen, nSamp - offset);
 
        // Window slices
        MatrixXd cInWin = cIn.middleCols(offset, winLen);   // N_in  × winLen
        MatrixXd cOutWin = cOut.middleCols(offset, winLen); // N_out × winLen
 
        // ---- Temporal tSSS projection ----
        // SVD of external coefficient matrix (column = time point):
        // cOutWin = U * S * V^T   (N_out × winLen)
        // Right singular vectors V (winLen × min) form the temporal subspace of external signals.
        JacobiSVD<MatrixXd> svd(cOutWin, ComputeThinU | ComputeThinV);
        const VectorXd& sv = svd.singularValues();
        const MatrixXd& V = svd.matrixV(); // winLen × rank_out
 
        // Determine how many external temporal components to suppress
        double svMax = (sv.size() > 0) ? sv(0) : 0.0;
        if (svMax < 1e-30) {
            offset += winLen;
            continue;
        }
 
        // Build temporal projector to remove the correlated subspace
        // P_remove: (winLen × winLen) = V_r * V_r^T
        // Applied: cInWin_clean = cInWin * (I - P_remove)
        MatrixXd Vr; // winLen × n_remove
        int nRemove = 0;
        for (int k = 0; k < sv.size(); ++k) {
            if (sv(k) / svMax > dCorrLimit) {
                ++nRemove;
            } else {
                break;
            }
        }
 
        if (nRemove > 0) {
            Vr = V.leftCols(nRemove); // winLen × nRemove
            // cInWin_clean = cInWin * (I - Vr * Vr^T)
            MatrixXd cInWinClean = cInWin - cInWin * (Vr * Vr.transpose());
            cIn.middleCols(offset, winLen) = cInWinClean;
        }
 
        offset += winLen;
    }
 
    // Reconstruct cleaned MEG data from internal expansion
    MatrixXd megTsss = basis.matSin * cIn; // n_meg × nSamp
 
    // Write back
    for (int i = 0; i < nMeg; ++i) {
        matOut.row(basis.megChannelIdx[i]) = megTsss.row(i);
    }
 
    return matOut;
}