The goal of `ravetools`

is to provide memory-efficient signal & image
processing toolbox for `intracranial Electroencephalography`

.
Highlighted features include:

`Notch filter`

(remove electrical line frequencies)`Welch Periodogram`

(averaged power over frequencies)`Wavelet`

(frequency-time decomposition)- 2D, 3D image convolution via
`FFT`

`CT/MRI`

to`MRI`

image alignment

The package is available on `CRAN`

. To install the compiled version,
simply run:

```
install.packages("ravetools")
```

Installing the package from source requires installation of proper
compilers and some `C`

libraries; see this
document
for details.

`iEEG`

preprocess pipelineThis is a basic example which shows you how to preprocess an `iEEG`

signal. The goal here is to:

- Plot diagnostic graphs to inspect channels
- Apply Notch filters to remove electrical line noise
- Frequency-time decomposition and show the power densities

* Channel referencing is not included

```
library(ravetools)
# Generate 20 second data at 2000 Hz
time <- seq(0, 20, by = 1 / 2000)
signal <- sin( 120 * pi * time) +
sin(time * 20*pi) +
exp(-time^2) *
cos(time * 10*pi) +
rnorm(length(time))
diagnose_channel(signal, srate = 2000)
```

`Notch`

filters and inspect `Periodograms`

```
## ------- Notch filter --------
signal2 <- notch_filter(signal, sample_rate = 2000)
diagnose_channel(signal, signal2, srate = 2000,
name = c("Raw", "Filtered"))
```

Current version of `ravetools`

provides two approaches: `Wavelet`

and
`Multi-taper`

. `Wavelet`

uses the
`Morlet wavelet`

and
obtains both amplitude and phase data, while `Multi-taper`

does not
generate phase data. However, the amplitude obtained from `Multi-taper`

is smoother than `Wavelet`

.

`Wavelet`

:```
## ---------- Wavelet -----------
coef <- morlet_wavelet(
signal2, freqs = seq(1, 100, by = 1),
srate = 2000, wave_num = c(2, 15))
amplitude <- 20 * log10(Mod(coef[]))
# For each frequency, decimate to 100 Hz
downsample_amp <- apply(amplitude, 2, decimate, q = 20)
downsample_time <- decimate(time, q = 20)
par(mfrow = c(1,1))
image(
z = downsample_amp,
x = downsample_time,
y = seq(1, 100, by = 1),
xlab = "Time (s)",
ylab = "Frequency (Hz)",
main = "Amplitude (dB)",
sub = "Wavelet at 2000 Hz, then down-sampled to 100 Hz",
col = matlab_palette()
)
```

`Multi-taper`

Alternatively you can use `Multi-tapers`

to obtain amplitude data. The
algorithm is modified from source code
here. Please credit
them as well if you adopt this approach.

```
## ---------- Multitaper -----------
res <- multitaper(
data = signal2,
fs = 2000,
frequency_range = c(1, 100),
time_bandwidth = 1.5,
window_params = c(2, 0.01),
nfft = 100
)
par(mfrow = c(1,1))
image(
x = res$time,
y = res$frequency,
z = 10 * log10(res$spec),
xlab = "Time (s)",
ylab = 'Frequency (Hz)',
col = matlab_palette(),
main = "Amplitude (dB)"
)
```

`ravetools`

provides imaging co-registration via `NiftyReg`

(`doi.org/10.1117/1.JMI.1.2.024003`

). You can align `CT`

to `MRI`

, or
`MRI`

(T2) to `MRI`

(T1). The method can be body `rigid`

, `affine`

, or
`non-linear`

.

```
source <- system.file("extdata", "epi_t2.nii.gz", package="RNiftyReg")
target <- system.file("extdata", "flash_t1.nii.gz", package="RNiftyReg")
aligned <- register_volume(source, target, verbose = FALSE)
source_img <- aligned$source[[1]]
target_img <- aligned$target
aligned_img <- aligned$image
par(mfrow = c(2, 2), mar = c(0.1, 0.1, 3.1, 0.1))
pal <- grDevices::grey.colors(256, alpha = 1)
image(source_img[,,30], asp = 1, axes = FALSE,
col = pal, main = "Source image")
image(target_img[,,64], asp = 1, axes = FALSE,
col = pal, main = "Target image")
image(aligned_img[,,64], asp = 1, axes = FALSE,
col = pal, main = "Aligned image")
# bucket fill and calculate differences
aligned_img[is.nan(aligned_img) | aligned_img <= 1] <- 1
target_img[is.nan(target_img) | aligned_img <= 1] <- 1
diff <- abs(aligned_img / target_img - 1)
image(diff[,,64], asp = 1, axes = FALSE,
col = pal, main = "Percentage Difference")
```

`RAVE`

paper from `Beauchamp's lab`

```
Magnotti, JF, and Wang, Z, and Beauchamp, MS. RAVE: comprehensive
open-source software for reproducible analysis and visualization of
intracranial EEG data. NeuroImage, 223, p.117341.
```

The `multitaper`

function (MIT License) uses the script derived from
`Prerau's lab`

. The `TinyParallel`

script is derived from `RcppParallel`

package (GPL License) with `TBB`

features removed (only use
`tinythreads`

). The `register_volume`

function uses `NiftyReg`

(BSD
License) developed by `CMIC`

at University College London, UK (its R
implementation is released under GPL license).

```
[1] Magnotti, JF, and Wang, Z, and Beauchamp, MS. RAVE: comprehensive
open-source software for reproducible analysis and visualization of
intracranial EEG data. NeuroImage, 223, p.117341.
[2] Prerau, Michael J, and Brown, Ritchie E, and Bianchi, Matt T, and
Ellenbogen, Jeffrey M, and Purdon, Patrick L. Sleep Neurophysiological
Dynamics Through the Lens of Multitaper Spectral Analysis. Physiology,
December 7, 2016, 60-92.
[3] Modat, M., Cash, D.M., Daga, P., Winston, G.P., Duncan, J.S. and
Ourselin, S., 2014. Global image registration using a symmetric
block-matching approach. Journal of medical imaging, 1(2), pp.024003-024003.
[4] JJ Allaire, Romain Francois, Kevin Ushey, Gregory Vandenbrouck, Marcus
Geelnard and Intel (2022). RcppParallel: Parallel Programming Tools for
'Rcpp'. R package version 5.1.5.
https://CRAN.R-project.org/package=RcppParallel
```

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