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Spectral and other time/frequency analyses

Methods for spectral and other time/frequency analyses, including power spectral density estimation

Command Description
PSD Welch's method for power spectral density estimation
MTM Multi-taper method for power spectral density estimation
FFT Basic discrete Fourier transform of a signal
IRASA Irregular-resampling auto-spectral analysis
HILBERT Hilbert transform
CWT Continuous wavelet transform
CWT-DESIGN Complex Morlet wavelet properties
EMD Empirical mode decomposition
MSE Multi-scale entropy statistics
LZW LZW compression (information content) index
1FNORM Remove the 1/f trend from a signal
TV Total variation denoiser
ACF Autocorrelation function


Estimates a signal's power spectral density (PSD)

This command uses Welch's method to estimate power spectra and band power for one or more signals. As well as estimates for the entire signal (possibly following masking, etc), this command optionally provides epoch-level estimates.

Internally, this command operates on an epoch-by-epoch basis: e.g. taking 30 seconds of signal, and using Welch's method of overlapping windows (by default, 4-second windows with 2-second overlap) to estimate the power spectra via FFT. By default, intervals are windowed using a 50% tapered Tukey window, although Hann and Hamming windows can also be specified. If epoch-level output is requested, e.g. with the epoch option, then these spectra are also written to the output database. The overall estimate of the PSD is the average of the epoch-level estimates.


Parameter Example Description
spectrum spectrum Estimate power spectra as well as band power
max max=30 Upper frequency range to report for spectra (default is 20 Hz)
epoch epoch Output epoch-level band power estimates
epoch-spectrum epoch-spectrum Output epoch-level power spectra
dB dB Give power in dB units
peaks peaks Reports statistics on extreme peaks (spikes) ( see section below for more options/details )
slope 30,45 Estimate spectral slope ( see section below for more options/details )
th 3 Remove epochs that are +/- 3 SD units outliers before calculating mean/median/SD over epochs
median Compute median (not mean) over epochs
sd Report standard deviation for epoch-to-epoch variability in power

In addition to the primary parameters above, there are a number of other, more detailed parameters (that can probably be ignored by most users), as described in this table:

Parameter Example Description
segment-sec segment-sec=8 Set window size for Welch's method (default is 4 seconds)
segment-overlap segment-overlap=4 Set window overlap for Welch's method (default is 2 seconds)
center center First mean-center each epoch (or centre)
no-average no-average Do not average adjacent points in the power spectra
tukey50 tukey50 Apply Tukey 50% window (default)
hann hann Apply a Hann window function
hamming hamming Apply a Hamming window function
no-window no-window Do not apply any window function
segment-median Use median (not mean) over Welch segments (to get epoch estimates)
segment-sd Report inter-segment standard deviation as well as mean (or median) per epoch
Simple ultradian/power dynamics statistics
Parameter Example Description
dynamics Report inter-segment standard deviation as well as mean (or median) per epoch


If the EPOCH size is set to a small value (i.e. under 4 seconds) you will need to adjust the parameters of Welch's method accordingly.

Cache options
Parameter Example Description
cache-metrics cache-metrics=c1 Cache PSD by F and CH (e.g. for PSC)

Band definitions

Luna uses the following band definitions:

  • SLOW (0.5 to 1 Hz)
  • DELTA (1-4 Hz)
  • THETA (4-8 Hz)
  • ALPHA (8-12 Hz)
  • SIGMA (12-15 Hz)
  • BETA (15-30 Hz)
  • GAMMA (30+ Hz).


These can be modified by setting special variables either via the command-line or in a parameter file.

In addition, SLOW_SIGMA and FAST_SIGMA are defined as 12-13.5 Hz and 13.5-15 Hz respectively.


Channel-level information (strata: CH)

Variable Description
NE Number of epochs included

Spectral band power (strata: B x CH)

Variable Description
PSD Absolute spectral power
RELPSD Relative spectral power

Spectral power by frequency bin (option: spectrum, strata: F x CH)

Variable Description
PSD Absolute spectral power
PSD_SD Standard deviation (within epoch) of absolute spectral power

Epoch-level spectral band power (option: epoch, strata: E x B x CH)

Variable Description
PSD Absolute spectral power
RELPSD Relative spectral power

Epoch-level spectral power by frequency bin (option: epoch-spectrum, strata: E x F x CH)

Variable Description
PSD Absolute spectral power


Here we calculate band power and the PSD for tutorial individual nsrr01, for all N2 and all N3 sleep separately. Note, here we run Luna twice but put all output in the same out.db database, by using -a to append for the second command, rather than -o. We also add a TAG command to disambiguate the output:

luna s.lst 2 sig=EEG -o out.db -s 'EPOCH & MASK ifnot=NREM2 & RE & TAG SS/N2 & PSD spectrum'
luna s.lst 2 sig=EEG -a out.db -s 'EPOCH & MASK ifnot=NREM3 & RE & TAG SS/N3 & PSD spectrum'
Here we see that all output for PSD has an additional SS (sleep stage) stratifier:
destrat out.db
distinct strata group(s):
  commands      : factors           : levels        : variables 
  [EPOCH]       : .                 : 1 level(s)    : DUR INC NE
                :                   :               : 
  [RE]          : .                 : 1 level(s)    : DUR1 DUR2 NR1 NR2
                :                   :               : 
  [MASK]        : EPOCH_MASK        : 2 level(s)    : N_MASK_SET N_MASK_UNSET N_MATCHES
                :                   :               : N_RETAINED N_TOTAL N_UNCHANGED
                :                   :               :
                :                   :               : 
  [PSD]         : CH SS             : 2 level(s)    : NE
                :                   :               : 
  [PSD]         : F CH SS           : 82 level(s)   : PSD
                :                   :               : 
  [PSD]         : B CH SS           : 20 level(s)   : PSD RELPSD
                :                   :               : 

The number of epochs of N2 and N3 sleep respectively:

destrat out.db +PSD -r CH SS
ID        CH     SS    NE
nsrr02    EEG    N2    399
nsrr02    EEG    N3    185

Here we tabulate relative power for N2 and N3 sleep:

destrat out.db +PSD -r CH B -c SS -v RELPSD -p 2 
ID       B           CH     RELPSD.SS.N2  RELPSD.SS.N3
nsrr02   SLOW        EEG    0.18          0.21
nsrr02   DELTA       EEG    0.50          0.61
nsrr02   THETA       EEG    0.15          0.10
nsrr02   ALPHA       EEG    0.07          0.03
nsrr02   SIGMA       EEG    0.04          0.02
nsrr02   SLOW_SIGMA  EEG    0.02          0.01
nsrr02   FAST_SIGMA  EEG    0.01          0.01
nsrr02   BETA        EEG    0.02          0.01
nsrr02   GAMMA       EEG    0.00          0.00
nsrr02   TOTAL       EEG    1.00          1.00

As expected, the relative power of delta sleep is higher in N3 (61%) compared to N2 (50%) for this individual.

To look at per-epoch estimates of band power for all N2 and N3 sleep:

luna s.lst 2 sig=EEG -o out2.db -s 'MASK if=wake & RE & PSD epoch'

For a change, here we'll use lunaR to directly load out2.db into the R statistical package. If you have R and lunaR installed, then at the R prompt:

k <- ldb("out2.db")
To summarize the contents:
RE : BL 
We can directly extract a data frame of epoch by band by channel information:
d <- k$PSD$B_CH_E
From this, we can further select on delta power:
delta <- d[ d$B == "DELTA" , ] 
Plotting these data, we see a moderate decrease in relative delta power across the night:
plot( delta$E ,delta$RELPSD , pch=20 , col="blue", ylab="Relative delta power" , xlab="Epoch" )

The correlation coefficient between epoch number and relative delta power is r = -0.36 and highly significant:

cor.test( delta$E ,delta$RELPSD  ) 
    Pearson's product-moment correlation

data:  delta$E and delta$RELPSD
t = -10.304, df = 713, p-value < 2.2e-16
alternative hypothesis: true correlation is not equal to 0
95 percent confidence interval:
 -0.4221768 -0.2944507
sample estimates:


Here we review two options that perform post-processing of power spectra derived from PSD: peaks and slope.

The peaks option gives diagnostics that indicate likely sharp peaks in the power spectra, e.g. as caused by line noise rather than genuine physiological oscillatory activity, which tends to produce smoother "bumps", even though those are often called peaks in the literature (e.g. alpha or sigma).

Option Example Description
peaks Perform peaks analysis
epoch-peaks Perform peaks analysis epoch-by-epoch
peaks-window 7 Size of median filter used by peaks (default: 11 sample points)
peaks-frq 30,45 Set lower and upper bounds for the peaks analysis (default: whole spectrum)
peaks-verbose Give verbose output from peaks (show smoothed spectra, etc)

Here we take some real N2 EEG signals, and estimate the power spectra via the Welch method:

luna s.lst -o out.db
           -s ' MASK ifnot=N2 & RE & uV sig=C3
                PSD  sig=C3 max=50 spectrum dB '

We'll also look at the same signal, but with some artifact spiked in, using the SIMUL command to introduce artificial spectral peaks (spikes) at these frequencies (i.e. as might reflect contamination from non-physiological sources), at 5 Hz intervals from 15 Hz up to 45 Hz:

luna s.lst -o out2.db
           -s ' MASK ifnot=N2 & RE & uV sig=C3
                SIMUL frq=15,20,25,30,35,40,45 psd=500,500,500,500,500,500,500 add sig=C3
                PSD  sig=C3 max=50 spectrum dB'
Note that the add option for SIMUL adds the simulated signal onto the existing (real) C3 signal.

Plotting the resulting power spectra from both runs, we can clearly see the super-imposed artifact resulting in a more spiky or peaked spectrum:


We can use the peaks option to provide one simple way of quantifying the extent of peakedness, by adding peaks to the PSD command. By default, this would use the full spectrum to derive peak statistics: for this particular metric, it can be a good idea to avoid the lower frequenies that often contain true bumps/peaks, e.g. resulting from oscillatory activity at those frequencies, and so we'll use the peaks-frq option instead to explicitly set the frequency range used for the assessment of peaks: in this case 20 to 50 Hz. We'll also add the peaks-verbose option to get additional output to make the plots below. The PSD command now reads as follows:

  PSD sig=C3 max=50 spectrum dB peaks-frq=20,50 peaks-verbose '
Extracting the relevant channel-specific metrics, the peaks algorithm results in two metrics: KURT and SPK: for the original data:

destrat out.db +PSD -r CH | behead
     CH   C3                  
   KURT   0.317643047723098   
    SPK   0.747317003553891   

And for the data with spikes introduced, we see these metics are much higher:

     CH   C3                  
   KURT   24.053679807259     
    SPK   5.39341010843361    

Both measures are based on the folllowing heuristic:

  • take the log-scaled power spectrum (between the frequencies specified by peaks-frq) and scale it between 0.0 and 1.0

  • detrend this spectrum (DT: detrended spectum) and then apply a smoothing median filter (with peaks-window sample points) to give a smoothed spectrum (SM)

  • calculate the differece (DF) between DT and SM

  • estimate the kurtosis of DF (which is the KURT metric) and the SPK (spikiness) as the sum of absolute values of the derivative of DF (sum( abs(diff(DF)))

The kurtosis estimate is normalized to have an expected value of 0 for normal distributions (i.e. subtract 3.0); larger positive values indicate greater spikiness in the spectrum. Likewise, greater values of SPK reflect higher spikiness. The two metrics difference a little, in that the latter is more sensitive to having many but smaller peaks (i.e. summing over all differences), whereas the KURT metric is more sensitive to a single, strong outlier. These metrics do not have directly interpreted scales (e.g. they may depend on sample rate, etc) but are designed to provide rankings across multiple studies, to identifier outliers, with respect to the extent of spectral spikes.

Adding the peaks-verbose option gives additional output (stratified by both CH and F) that directly give the DT, SM and DF spectra as described above: plotting these for the first instance: (black = DT, blue = SM):


Whereas, for the second instance (with the spikes introduced), we see greater values for DF (note the different x-axis for the rightmost plot, versus above):


If this approach is including true bumps as outliers/spikes here, you can try reducing peaks-window from the default of 11 (the number of bins in the spectrum over which to do median smoothing), which basically requires that sharper peaks by doing less smoothing.

Spectral slopes

Given a power spectrum (or multiple epoch-wise power spectra) for an individual/channel, we can estimate the spectral slope as the log-log linear regression of power on frequency. For an example of using Luna to estimate the spectral slope, see Kozhemiako et al (2021).

This is achieved by adding slope to the PSD command, and giving the frequency interval over which the slope should be estimated. See the references in the abovementioned pre-print to see other applications of the spectral slope to sleep data, and justification for looking at particular frequency ranges (i.e. in the above work, 30-45 Hz).

The other options are as follows:

Option Example Description
slope 30,45 Estimate spectral slope in this frequency range
slope-th 2 Threshold to remove points when estimating slope (default: 3)
slope-th2 2 Threshold to remove epochs when summarizing slopes over all epochs (default: 3)
epoch-slope Display epoch-by-epoch slope estimates

Luna fits the linear model of log power regressed on log frequency. After fitting an initial model, it identifies any points (frequency bins) that have residual values greater or less than the slope-th threshold. This helps to avoid spikes in the power spectrum having undue leverage on the overall slope estimate. You can also use the peaks metrics above to flag studies that might have issues with respect to spikes. After removing any outlier points, Luna refits the model: the estimated slope is SPEC_SLOPE, and the number of data points used to estimate it is in SPEC_SLOPE_N. If looking at a 30-45 Hz (inclusive) slope with a spectral resolution of 0.25 Hz, this gives an expected 61 points; the number may be lower is points were removed for being outliers.

Avoiding periodic acticvity that will bias spectral slope estimates

This simple implementation for estimating the spectral slope is not suitable for frequency ranges where one expects strong oscillatory activity, e.g. the sigma band during sleep, if it is to be interpreted as an index of the aperiodic component of the power spectrum. The frequency range 30-45 Hz tends to be freely of such activity and also avoids frequencies with common line noise (50/60 Hz) artifacts. As such, it is not recommended that this be used as a general method (unless other procedures have first been applied to remove oscillatory components from the signal).

Assuming multiple epochs are present in the data, Luna will also estimate the slope epoch-by-epoch. The mean, median and the SD of these epoch-by-epoch slopes are given in SPEC_SLOPE_MN, SPEC_SLOPE_MD and SPEC_SLOPE_SD respectively. When combining these epoch-wise slopes, Luna will first remove epochs that have slope estimates that are outliers as defined by the slope-th2 (default = 3 SD).

Spectral slope outputs: (strata: CH)

Value Description
SPEC_SLOPE Spectral slope of the average power spectrum
SPEC_SLOPE_N Number of data points in the final model
SPEC_SLOPE_MN Mean slope (over all epochs)
SPEC_SLOPE_MD Median slope (over all epochs)
SPEC_SLOPE_SD Slope SD (over all epochs)


Applies the multitaper method for spectral density estimation

This provides an alternative to PSD for spectral density estimation, that can be more efficient in some scenarios (albeit slower): the multitaper method as described here.

The time half bandwidth product parameter (nw) provides a way to balance the variance and resolution of the PSD: higher values reduce both the variance and the frequency resolution, meaning smoother but potentially blunted and biased power spectra. The optimal choice of nw will depend on the properties of the data and the research question at hand. This manuscript provides a nice review of the use of multitaper spectral analysis in the sleep domain, along with considerations for specifying the time half bandwidth product (nw) and the number of tapers (t). (By default, MTM will always use 2nw-1 tapers.)

As currently specified, the MTM command does not use the standard epoch mechanism for output. Rather, it is based on the concept of segments, which define the window of spectral analysis. These may be much smaller than a typical epoch (e.g. 1 second) and one may wish to have highly overlapping segments in a sliding-window style of analysis. Because of this, it is more efficient (internally) to use a different mechanism. By default, segments are defined to be 30 seconds, and to step in increments of 30 seconds, so for all intents and purposes, this will be identical to (default) epoch specification.


Parameter Example Description
sig C3,C4 Which signals to analyse
epoch Report epoch-level results (nb. actually segments, see above)
nw 4 Time half bandwidth product (default 3, typically: 2, 5/2, 3, 7/2, or 4)
t 7 Number of tapers (default 2*nw-1, i.e. 5)
segment-sec 30 Segment size (default 30 seconds)
segment-inc 30 Segment increment/step (default 30 seconds)
min 0.5 Maximum frequency for power spectra (default is 20Hz)
max 25 Maximum frequency for power spectra (default is 20Hz)
dB Report power in dB units
dump-tapers Report the taper coefficients in the output
mean-center Mean center segments prior to analysis


Whole-signal power spectra (strata: CH x F)

Variable Description
MTM Absolute spectral power via the multitaper method

Epoch-level (segment) power spectra (option: epoch, strata: SEG x CH x F)

Variable Description
MTM Spectral power via the multitaper method


To compare results for the N2 power spectra up to 20 Hz, from PSD and MTM for the three tutorial individuals:

luna s.lst -o out.db -s ' MASK ifnot=NREM2
                        & RE
            & PSD sig=EEG dB spectrum max=20
            & MTM sig=EEG dB tw=15 max=20'

This gives some output describing the properties of the MT analysis in the console:

 CMD #4: MTM
   options: dB=T max=20 sig=EEG tw=15
  assuming all channels have the same sample rate of 125Hz:
    time half-bandwidth (nw) = 15
    number of tapers         = 29
    spectral resolution      = 1Hz
    segment duration         = 30s
    segment step             = 30s
    FFT size                 = 4096
    number of segments       = 375
    adjustment               = none
  processed channel(s): EEG

k <- ldb( "out.db" )
mtm <- lx( k , "MTM" , "CH" , "F" )
psd <- lx( k , "PSD" , "CH" , "F" )
yr <- range( c( mtm$MTM , psd$PSD ) )
for (i in unique( mtm$ID ) ) { 
plot( mtm$F[mtm$ID==i] ,  mtm$MTM[mtm$ID==i] , type="l" , col="purple" , lwd=2 , xlab="Frequency (Hz)" , ylab="Power (dB)" , ylim=yr ) 
lines( psd$F[psd$ID==i] , psd$PSD[psd$ID==i] , type="l" , col="orange" , lwd=2 ) 


As expected, in this particular scenario and with long signals, both methods produce similar results.

As a second example, here is a whole-night MT spectrogram, performed within lunaR:

lattach( lsl( "s.lst" ) , 1 ) 
k <- leval( "MTM sig=EEG tw=15 max=30 epoch dB" ) 

Examing the output:


And plotting a heatmap:

d <- k$MTM$CH_F_SEG
lheatmap( d$SEG , d$F , d$MTM ) 


As a third example: here is an application of MTM on a smaller segment of data (a single epoch), which shows sleep spindles in the MTM spectrogram (plotting the results in the range of 8 to 20 Hz), generated by the commands:

MTM segment-sec=2.5 segment-inc=0.02 epoch nw=5 max=30 dB

Note the use of a small (2.5 seconds) segment size, which is shifted only 0.02 seconds at a time, and so gives a considerable smoothing of estimates in the time domain (which may or may not be desirable, depending on the goal of the analysis.)



Applies the basic discrete Fourier transform to a signal

In contrast to Welch (PSD) or multi-taper (MTM) approaches, the FFT performs that same function (for a single, real, 1-dimensional signal) as the fft() function in R or Matlab, i.e. the DFT with no windowing or tapering, and which will have as many points as there are samples. As such, this is intended for use with simple/short signals, where one wants this exact quantity, e.g. if validating a computation, as we did here. For real data (especially long, whole night recordings), PSD and MTM will provide better estimates of the power spectrum.


Practically, for very long signals, FFT will return a very large/dense spectrum, which might make the destrat output mechanism struggle; if you really want this, run with the -t command-line option to produce text table outputs.


Parameter Example Description
sig C3,C4 Which signal(s) to analyse
verbose Output additional variables (see below)


Whole-signal power spectra (strata: CH x F)

Variable Description
PSD Raw power spectral density
DB 10log10(PSD)

Optional output (option: verbose, strata: CH x F)

Variable Description
RE Real part of the DFT
IM Imaginary part of the DFT
UNNORM_AMP Unnormalized amplitude
NORM_AMP Normalized amplitude


This command is a wrapper around the same FFT/DFT analysis performed by the fft command line function:

luna -d fft 100 < data.txt

See this vignette for a description of the outputs, and an example of usage (i.e. here, the only difference is that FFT command operates on EDF channels, whereas the example above is based on reading a text file.)


Implements the Irregular-Resampling Auto-Spectral Analysis method

Implements the IRASA method as described here, which seeks to partition power spectra into periodic (oscillatory) and aperiodic (fractal) components.


Parameter Example Description
sig C3,C4 Which signal(s) to analyse
h-min 1.05 Minimum resampling factor (default 1.05)
h-max 1.95 Maximum resampling factor (default 1.95)
h-steps 19 Number of steps between h-min and h-max (default 19, i.e. 0.05 increments)
dB Report log-scaled power
epoch Report epoch-level statistics
min 1 Minimum frequency to include in the output (default 1 Hz)
max 30 Maximum frequency to include in the output (default 30 Hz)

Secondary parameters

Parameter Example Description
segment-mean Use the mean (rather than median) across segments, within epoch
epoch-mean Use the mean (rather than median) across epochs
fast Use a faster (but less accurate) linear resampling method
segment-sec 5 Set the Welch segment size (default 4 seconds)
segment-overlap 2.5 Set the Welch segment overlap/increment (default 2 seconds)
no-window No windowing for Welch method
hann Use a Hann window for Welch method
hamming Use a Hamming window for Welch method
tukey50 Use a Tukey window for Welch method


Whole-signal power spectra (strata: CH x F)

Variable Description
LOGF Log-scaled frequency F (if dB specified)
APER Aperiodic component of the power spectrum
PER Periodic component of the power spectrum

Whole-signal slope statistics (strata: CH)

Variable Description
SPEC_SLOPE Spectral slope (based on aperiodic component)
SPEC_INTERCEPT Spectral intercept (based on aperiodic component)
SPEC_RSQ R-sq for above fit
SPEC_SLOPE_N Number of non-outlier data points in slope estimate

Optional epoch-level output (option: epoch, strata: E x CH x F)

Variable Description
LOGF Log-scaled frequency F ( if dB specified)
APER Aperiodic component of the power spectrum
PER Periodic component of the power spectrum

Epoch-level slope statistics (option: epoch, strata: E x CH)

Variable Description
SPEC_SLOPE Epoch spectral slope (based on aperiodic component)
SPEC_INTERCEPT Epoch spectral intercept (based on aperiodic component)
SPEC_RSQ Epoch R-sq for above fit
SPEC_SLOPE_N Epoch number of non-outlier data points in slope estimate


We use Luna's SIMUL command to generate random time series data, specifying a 1/f^alpha slope with alpha set to 2, as well as a periodic component centered at 15 Hz. We generate 3000 seconds of data, with a sample rate of 256 Hz:

luna . -o out.db --nr=3000 --rs=1 \
       -s ' SIMUL alpha=2 intercept=1 frq=15 psd=10 w=0.1 sig=S1 sr=256
            PSD sig=S1 spectrum max=30 slope=1,30 dB
            IRASA sig=S1 dB h-max=4'
As shown above, we then analyse the simulated channel S1 using first PSD (Welch method) and then IRASA. For PSD, we have to explicitly request that the spectral slope be estimated (slope=1,30) which indicates a log-log regression of power on frequency (after removing outlier points). We expect this estimate of slope to be biased by the oscillatory peak at 15 Hz. In contrast, IRASA will generate two spectra, the aperiodic and periodic components, and estimate the slope (using the same approach as PSD) only on the aperiodic component.

To compare like-with-like, we use a range of 1 to 30 Hz in both cases (this is the default for IRASA). We set the resampling factor h to have a maximum value of 4, which implies an evaluated range of 1/4 = 0.25 Hz to 30 * 4 = 120 Hz.

  specified frequency range is 1 - 30 Hz
  full evaluated frequency range given h_max = 4 is 0.25 - 120 Hz

Extracting the estimated slopes from both methods: Welch estimates are a little biased (-1.9 instead of -2, i.e. minus alpha) and has a modest model fit (R-sq ~50%):

destrat out.db +PSD -r CH  | behead
                 SPEC_RSQ   0.50951
               SPEC_SLOPE   -1.91503
In contrast, the IRASA estimate is closer to -2 and basically has a near perfect model fit:
destrat out.db +IRASA -r CH  | behead
                 SPEC_RSQ   0.99874   
               SPEC_SLOPE   -2.03858

We can visualize the three resulting power spectra as follows, in R:

k <- ldb( "out.db" )
d <- k$PSD$CH_F
plot( d$F, d$PSD, type="l", lwd=3, col="blue", xlab="Frequency (Hz)", ylab="log(Power)", main="Welch" ) 
i <- k$IRASA$CH_F
plot( i$F, i$APER, type="l", lwd=3, col="orange", xlab="Frequency (Hz)", ylab="log(Power)", main = "IRASA, aperiodic" )
plot( i$F, i$PER, type="l", lwd=3, col="purple", xlab="Frequency (Hz)", ylab="log(Power)", main="IRASA, periodic" )


As nicely illustrated by Gerster et al. (2021), IRASA is not infallible - for instance, if there are very broad oscillatory peaks and the maximum resampling factor is not sufficiently high, it can fail to properly separate out aperiodic and periodic components (see their Figure 6). We can recapitulate this property by increasing the peak width (w in SIMUL) and reduced the resampling factor (h-max in IRASA):

luna . -o out.db --nr=3000 --rs=1 \
       -s ' SIMUL alpha=2 intercept=1 frq=15 psd=10 w=1 sig=S1 sr=256
            PSD sig=S1 spectrum max=30 slope=1,30 dB
            IRASA sig=S1 dB h-max=2'


This is of course an extreme example (i.e. with a very large amplitude, broad oscillatory peak), but nonetheless shows one failure mode of IRASA. In practice, examining the shape of the aperiodic spectrum (i.e. which should be approximately striaght on a log-log or semi-log plot) will indicate if resulting slope estimates are likely biased by oscillatory activity.


Applies filter-Hilbert transform to a signal, to estimate envelope and instantaneous phase

This function can be used to generate the envelope of a (band-pass filtered) signal.


Parameter Example Description
sig sig=EEG Which signal(s) to apply the filter-Hilbert to
f f=0.5,4 Lower and upper transition frequencies
ripple ripple=0.02 Ripple (0-1)
tw tw=0.5 Transition width (in Hz)
tag tag=v1 Additional tag to be added to the new signal
phase phase Generate a second new signal with instantaneous phase


No formal output, other than one or two new signals in the in-memory representation of the EDF, with _hilbert_mag and (optionally) _hilbert_phase suffixes.


Using lunaR, with nsrr02 attached, we will use the filter-Hilbert method to get the envelope of a sigma-filtered EEG signal. After attaching the sample, we then drop all signals except the one of interest:

leval( "SIGNALS keep=EEG" ) 

We then apply the filter-Hilbert method, which will generate two new channels, EEG_hilbert_11_15_mag and EEG_hilbert_11_15_phase:

leval( "HILBERT sig=EEG f=11,15 ripple=0.02 tw=0.5 phase" )

For illustration, we'll also generate a copy of the original signal:

leval( "COPY sig=EEG tag=SIGMA" )

and then apply a bandpass filter to it, in the same sigma range as above:

leval( "FILTER sig=EEG_SIGMA bandpass=11,15 ripple=0.02 tw=0.5" ) 


Unlike HILBERT, FILTER modifies the source channel, which is why we COPY-ed the original channel first.

We now have four signals in the in-memory representation of the EDF:

[1] "EEG"                     "EEG_hilbert_11_15_mag"  
[3] "EEG_hilbert_11_15_phase" "EEG_SIGMA"              

To view some of the results, we can use ldata() to extract signals for a particular epoch. For better visualization, here we'll select smaller (15 second) epochs:


We can then pull all four signals for given (set of) epoch(s), say number 480:

d <- ldata( 480 , chs=lchs() ) 
Using R's plotting functions:
plot( d$SEC , d$EEG , ylab = "Raw" , type="l" ,axes=F)
plot( d$SEC , d$EEG_SIGMA , ylab = "Filtered" , type="l" , axes=F)
lines( d$SEC , d$EEG_hilbert_11_15_mag , col="red" , lwd=2 ) 
plot( d$SEC , d$EEG_hilbert_11_15_phase , ylab = "Phase" , type="l" , axes=F)



Applies a continuous wavelet transform by convolution with a complex Morlet wavelet

The CWT is the basis of the SPINDLE command. This command allows you to generate new signals in the EDF that correspond to the underlying CWT, e.g. for plotting, or getting insight into the performance of SPINDLES under different circumstances.


Parameter Example Description
sig sig=EEG Which signal(s) to apply the CWT to
fc fc=15 Wavelet center frequency
cycles cycles=12 Bandwidth of the wavelet, specified in terms of the number of cycles
tag tag=v1 Additional tag to be added to the new signal
phase phase Generate a second new signal with wavelet's phase


No formal output, other than one (or two) new signals appended to the in-memory representation of the EDF.


Display the properties of a complex Morlet wavelet transform

This command does not operate on EDFs per se; rather, it produces analytic output on the properties of a continuous wavelet transform (CWT) given the design parameters.

Wavelet bandwidth can be specifed in one of two ways: by giving the number of cycles (cycles option) OR by specifying the time-domain full width at half maximum (FWHM) value (in seconds). See this manuscript for a discussion of the advantages of this latter specificiation.

In both cases, the CWT-DESIGN will estimate the implied FWHM in the frequency domain, i.e. the tightness of the wavelet around the specified central frequency (fc).


Parameter Example Description
fs fs=200 Sample rate
fc fc=15 Center frequency
fwhm fwhm=1 Time-domain FWHM (use instead of cycles)
cycles cycles=7 Number of cycles in wavelet (use instead of fwhm)


Time/frequency domain FWHM (strata: PARAM)

Variable Description
FWHM Specified time-domain full width at half max (if fwhm option given) (secs)
FWHM_F Estimated frequency-domain FWHM (Hz)
FWHM_LWR Estimated lower half-max frequency bound (Hz)
FWHM_UPR Estimated upper half-max frequency bound (Hz)

Frequency response for wavelet (strata: PARAM x F)

Variable Description
MAG Magnitude of response (arbitrary units)

Wavelet coefficients (strata: PARAM x SEC)

Variable Description
REAL Real part of wavelet
IMAG Imaginary part of wavelet


To display the properties of a wavelet with center frequency of 15 hz and 12 cycles, applied to a signal with sample rate of 12 Hz.

luna s.lst 1 -o out.db -s 'CWT-DESIGN fc=15 cycles=12 fs=200'


The default value of cycles for the SPINDLES command is 7 cycles.

Equivalently, without an EDF/sample list, you can use the --cwt parameter and pipe the parameters (fc, cycles and fs). Here we use it for both 11 Hz and 15 Hz wavelets. Also, note the use of -a instead of -o for the second command, so that the output of the second command appends (rather than overwrites) the existing out.db:

echo "fc=11 cycles=12 fs=200" | luna --cwt -o out.db 
echo "fc=15 cycles=12 fs=200" | luna --cwt -a out.db 

Using lunaR to view the output:

k <- ldb("out.db")
We see two strata:
Extracting the strata defined by PARAM (a description of the input parameters) and F (frequency):
d <- lx( k , "CWT_DESIGN" , "PARAM" , "F" ) 
We can then plot the amplitude response (arbitrary units, scaled to 1.0) for the two wavelets:
plot( d$F[ d$PARAM == "11_12_200" ]  , d$MAG[ d$PARAM == "11_12_200" ] , 
 xlim=c(0,20) , type="l" , lwd=2 , col="blue" , 
 xlab="Frequency (Hz)" , ylab="Amplitude" , ylim=c(0,1) ) 

lines( d$F[ d$PARAM == "15_12_200" ]  , d$MAG[ d$PARAM == "15_12_200" ] , 
 lwd=2 , col="red" )

legend( 2 , 0.9 , c("11 Hz","15 Hz") , fill = c("blue","red") )


Looking at the estimated frequency domain FWHM values, we see these correspond to the y=0.5 (i.e. 50%) values for each wavelet, at lower and upper valeus respectively.

lx( k , "CWT_DESIGN" , "PARAM"  )
.   11_12_200  2.197802   9.89011  12.08791
.   15_12_200  3.003003  13.51351  16.51652


Empirical mode decomposition

Empirical mode decomposition, or the Hilbert-Huang transform (described here). Currently, EMD is applied to the entire duration of the recording (i.e. it does not work epoch-wise).


Option Example Description
sig C3 Specify the channels to which EMD will be applied
tag EMD Change the default _IMF_N tag, e.g. C3_IMF_1 to C3_EMD_1
sift 20 Maximum number of sifting iterations (default: 20)
imf 10 Number of intrinsic mode functions to extract (default: 10)


There is no formal output from the EMD command, other than generating new channels that are added to the in-memory EDF. That is, the intrinsic mode functions (up to imf of them) are written to the EDF as new channels (with the same sample rate as the original signal) and given suffixes _IMF_1, _IMF_2, etc. The residual component is given the suffix _IMF_0.


Here we use the SIMUL command to generate 5 minutes of a simple, stationary signal, that comprises three independent sine-waves, at 2, 7 and 15 Hz, with power of 1, 1 and 2 units, respectively.

luna . --nr=300 --rs=1 -o out.db \
       -s ' SIMUL frq=2,7,15 psd=1,1,2 sig=S1 sr=100
            EMD sig=S1 imf=3
            MATRIX file=s1.txt'

The EMD command by default takes requires the channel(s) (sig) to be specified. In this example, because we know there are only three components, we set imf to 3 (the default is to return 10 components). Note that the first three components will be identical whether or not imf is specified, as this option only impacts what is output (and the residual component).

The FFT command performs a DFT, for the original signal S1 but also the new signals attached by EMD: namely, S1_IMF_1, S1_IMF_2 and S1_IMF_3. Finally, the MATRIX command dumps the raw signals to a file s1.txt (for plotting).

After running the above, we can first look at the raw signals (in s1.txt), for the original simulated signal S1 (here showing just two seconds of the recording):


and likewise for the three components extracted by EMD:


It does indeed look as though EMD has extracted three sine waves, with the first being the fastest (as expected given the sifting/EMD algorithm) and also of greater amplitude (as expected given the simulation).

We can look at the spectral properties of the original signal and the EMD-derived components. As expected, the original signal has power at exactly 2, 7 and 15 Hz (in a ratio of 1:1:2):


Performing the FFT separately on each component, we see that EMD has in this instance successfully isolated the three simulated components:



In practice, although EMD can be more appropriate for working with nonlinear and nonstationary signals compared to other time/frequency decomposition methods, there can still be issues, e.g. arising from boundary effects or highly variably signals, as discussed here. Simply put, like most things, EMD is not guaranteed to magically work as expected every time...


Calculates per-epoch multi-scale entropy statistics

This function estimates multi-scale entropy (MSE) as described in the approach of Costa et al, which is based on the concept of sample entropy.

In short, there are two steps: first, the time series is coarse-grained, dependent on scale parameter s (typically varied between 1 and 20); second, sample entropy is calculated for each coarse-grained time series, dependent on parameters m and r. Parameters m and r define the pattern length and the similarity criterion respectively, with default values of 2 and 0.15 respectively. Smaller values of (multi-scale) entropy indicate more self-similarity and less noise in a signal.


Parameter Example Description
m m=3 Embedding dimension (default 2)
r r=0.2 Matching tolerance in standard deviation units (default 0.15)
s s=1,15,2 Consider scales 1 to 15, in steps of 2 (default 1 to 10 in steps of 1)
verbose verbose Emit epoch-level MSE statistics


MSE per channel and scale (strata: CH x SCALE)

Variable Description
MSE Multi-scale entropy

Epoch-level MSE per channel and scale (option: verbose, strata: E x CH x SCALE)

Variable Description
MSE Multi-scale entropy


Calculate per-epoch LZW compression index

Lempel–Ziv–Welch (LZW) is a commonly used data compression algorithm, which can be applied to coarse-grained sleep signals to provide a quantitative metric (the ratio of the size of the compressed signal versus the original signal) of the amount of non-redundant information in a signal.


Parameter Example Description
nsmooth nsmooth=2 Coarse-graining parameter (similar to scale s in MSE)
nbins nbins=5 Matching tolerance in standard deviation units (default 10)
epoch epoch Emit epoch-level LZW statistics


LZW per channel (strata: CH)

Variable Description
LZW Compression index

Epoch-level LZW per channel and scale (option: epoch, strata: E x CH)

Variable Description
LZW Compression index


Applies a differentiator filter to remove 1/f trends in signals

Many biological signals such as the EEG have an approximately 1/f frequency distribution, meaning that slower frequencies tend to have exponentially greater power than faster frequencies. It may sometimes be useful to normalize signals in such a way that removes this trend (e.g. in visualization, or detecting peaks against a background of a roughly flat baseline). The 1FNORM command is an implementation of this method to normalize power spectra, by passing the signal through a differentiator prior to spectral analysis.


Parameter Example Description
sig sig=C3,C4 Optional parameter to specify which channels to normalize (otherwise, all channels are normalized)


No output per se, other than modifying the in-memory representation of the specified channels.


Using the tutorial dataset and lunaC to run the analysis:

luna s.lst sig=EEG -o out.db 
  -s 'MASK ifnot=NREM2 
      TAG NORM/no 
      PSD spectrum 
      TAG NORM/yes 
      PSD spectrum '

Using lunaR to visualize the normalized and raw power spectra (in R):

k <- ldb( "out.db" )

Looking at the contents of out.db, we are interested in the results of PSD stratified by F (for power spectra), CH and NORM (the TAG that tracks in the output whether we have applied the normalization or not):


Extracting these variables and values:

d <- lx( k , "PSD" , "CH" , "F" , "NORM" )

      ID  CH F NORM       PSD
1 nsrr01 EEG 0   no  5.143138
2 nsrr02 EEG 0   no 15.236110
3 nsrr03 EEG 0   no 36.675012
4 nsrr01 EEG 0  yes 17.649644
5 nsrr02 EEG 0  yes 30.562531
6 nsrr03 EEG 0  yes 52.218946
Pulling out the pre-normalized spectra:
pre <- d[ d$NORM == "no" , ] 
and then post normalization:
post <- d[ d$NORM == "yes" , ] 

Tracking the IDs of the three tutorial individuals, to plot them separately:

ids <- unique( d$ID ) 

We can then use R basic plotting commands to generate spectra for the three individuals (columns) corresponding to the raw, unnormalized spectra showing a 1/f trend (top row), the log-scaled spectra, which show more of a linear trend (middle row), and the normalized spectra (bottom row). Whereas we would not expect these spectra to be completely flat (e.g. certainly, if bandpass filters have already been applied to the data), which the range of ~5 to 20 Hz the baselines are relatively flat, and arguably the "peaks" (for nsrr02 around 13 Hz) are visibly clearer.

par( mfrow=c(3,3) , yaxt='n' , mar=c(4,4,1,1) ) 

for (i in ids) {
plot( pre$F[ pre$ID == i ] , pre$PSD[ pre$ID == i ] , 
 type="l" , lwd=2 , col="cornflowerblue" , ylab="Raw" , xlab=i )  }

for (i in ids) {
plot( pre$F[ pre$ID == i ] , 10*log10( pre$PSD[ pre$ID == i ] ), 
 type="l" , lwd=2 , col="goldenrod" , ylab="Log" , xlab=i)  } 

for (i in ids) {
plot( post$F[ post$ID == i ] , post$PSD[ post$ID == i ] , 
 type="l" , lwd=2 , col="olivedrab" , ylab="1/f-norm" , xlab=i)  }



Applies of fast algorithm for 1D total variation denoising

The TV is a wrapper around the algorithm described here. In lunaC it operates on EDF channels, modifying the in-memory representation of the signal.


Given that this is not something one typically wants to perform on raw physiological signals, a more common use-case may be via lunaR however, where the ldenoise() function provides a simple interface for any time series.
It is mentioned here only for completeness.


Parameter Example Description
sig sig=EEG Optional specification of signals (otherwise applied to all signals)
lambda lambda=10 Smoothing parameter (0 to infinity)

See the description of ldenoise() for using this function with lunaR. Higher values of lambda put more weight on minimizing variation in the new signal, i.e. producing a more flattened representation. The exact choice of lambda will depend on the numerical scale of the data as well as its variability and the goal of the analysis.


No output other than modifying the in-memory representation of the signal.


Using lunaR to plot delta power across sleep epochs and fit a de-noised line using ldenoise() (which invokes TV), to the nsrr02 individual from the tutorial dataset:

sl <- lsl("s.lst")
Get delta power (in dB units) for each sleep epoch:
k <- leval( "MASK if=wake & RE & PSD sig=EEG epoch dB" )
d <- k$PSD$B_CH_E
d <- d[ d$B == "DELTA" , ] 

Also get sleep stages via the lstages() function:

ss <- lstages()

Using ldenoise(), we can fit a de-noised line, with lambda of 10 in this particular case:

d1 <- ldenoise( d$PSD , lambda = 10 ) 

Plotting the original and de-noised versions, also using the convenience lstgcols() function:

plot( d$PSD, col=lstgcols(ss), pch=20, xlab="Sleep Epochs", ylab="Delta power (dB)" ) 
lines( d1 , lwd=5 , col="orange" )



Compute the autocorrelation function for a signal


Parameter Example Description
sig sig=EEG Optional specification of signals (otherwise applied to all signals)
lag lag=200 Maxmimum lag (in sample units)


ACF per channel (strata: CH x LAG)

Variable Description
SEC Lag in seconds
ACF Autocorrelation


To estimate the ACF for an example EEG, ECG and EMG channel, for up to 3 seconds lag (here assuming all channels are sampled at 100 Hz, and so a lag of 300):

luna s.lst 1 -o out.db -s 'ACF sig=EEG,ECG,EMG lag=300'
We can extract the output from the ACF function, conditional on CH and LAG strata (here putting different channels in different columns (-c CH) and different lags in different rows (-r LAG):

destrat out.db +ACF -c CH -r LAG > o.txt
Plotting these ACF (e.g. from o.txt, SEC.CH_EEG on the x-axis, and ACF.CH_EEG on the y-axis), we see strong, regular autocorrelations, with peaks at periodically recuring intervals (top row of plots below). These would be indicative of artifact in EEG, ECG or EMG channels: indeed, in this particular case (which is the first tutorial EDF), there is considerable artifact at the end of the recording (i.e. with spectral peaks at 25 Hz and 12.5 Hz, reflecting harmonics of electrical noise artifacts).


If we repeat the analysis just looking at sleep (N2) epochs (i.e. just a quick way to chop off the particularly noisy part of the recording), we see quite different ACF signatures, which are more characteristic of typical EEG, ECG and EMG respectively.

luna s.lst 1 -o out.db -s 'MASK ifnot=NREM2 & RE & ACF sig=EEG,ECG,EMG lag=300'
The output from this second run are plotted in the lower panel of the above figure.

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