semg-decomposition

GUI on jupyter notebook

Progress tracking

The following algorithm is described on Negro et al. (J. Neural Engineering, 2015) - page 3:

• Extend the observations x by a R factor
• Subtract the mean from the observations x
• Whiten x
• Initialize the matrix B to empty matrix
• For i = 1, 2, ..., M repeat:
  • 1. Initialize the vector w_i(0) and w_i(-1)
  • 2. While |w_i(n)^{T}w_i(n - 1) - 1| < Tolx
        a. Fixed point algorithm
            w_i(n) = E{zg[w_i(n - 1)^{T}z]} - Aw_i(n - 1)
            with A = E{g'[w_i(n - 1)^{T}z]}
        b. Orthogonalization
            w_i(n) = w_i(n) - BB^{T}w_i(n)
        c. Normalization
            w_i(n) = w_i(n)/||w_i(n)||
        d. Set n = n + 1
    
  • 3. End while
  • 4. Initialize CoV_{n - 1} and CoV_n
  • 5. While CoV_n < CoV_{n - 1}
        a. Estimate the i-th source
        b. Estimate the pulse train PT_n with peak detection and K-means class
        c. Set CoV_{n - 1} = CoV_n and calculate CoV_n of PT_n
        d. w_i(n + 1) = (1/J) \sum_{J = 1}^{J} z(t_j)
        e. Set n = n + 1
    End while CoV(n) < CoV(n - 1)
  • 6. If SIL > 0.9
        a. Accept the source estimate
        b. Add w_i to the matrix B
    End for loop