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Table 1 Mathematical definitions of the features used in this study

From: On the identification of sensory information from mixed nerves by using single-channel cuff electrodes

Feature Name Definition Description and references
Mean absolute value (MAV) [25]
Variance (VAR) [25]
Wave length (WL) [25]
Wavelet denoise (WDEN) θ = σ (0.3936 + 0.1829log2(N)), σ-standard deviation of noise Symmlet 7 mother wavelet, and hard threshold (θ).
[28, 29]. Finally the feature is MAV of denoised signal.
Energy based on discrete Fourier transformation (DFT) ,
where X [k] is DFT of x [n]
Autoregressive coefficients (AR) The forward-backward approach. The sum of a least squares criterion for a forward model and the analogous criterion for a time-reversed model is minimized [27].
Cepstral coefficients (CEPS) c1 = -a1
The cepstrum coefficients (ci), are calculated from AR coefficients (AR model with order P), as proposed in Kang's work [26].
Autocorrelation-based, second order processing (HOS2) ,
H0-noise only (null hypotesis), H1-presence of signal
Toepliz matrix creation, based on estimate of autocorrelation; singular value decomposition; difference among maximum and minimum eigenvalue (σ) [21].
Cumulant-based third order processing (HOS3) ,
H0-noise only, H1-presence of signal
Toepliz matrix creation, based on estimate of third order cumulant of a data frame; singular value decomposition; the largest eigenvalue (λ) [21].
Autocorrelation minimum value (ACORR) ,
L-length of ROW
First negative peak value of r(τ) [23, 24].
  1. References are also provided for further explanation. N is the number of samples in ROW and xi is the single sample.