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Update Understanding Parameters

tyler
2026-08-18 00:34:43 -07:00
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Understanding-Parameters.md
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@@ -45,11 +45,13 @@ Note: If OPTODE_PLACEMENT is set to False, SHOW_OPTODE_NAMES will be ignored.
Identifies both short and long channels. A channel is the inter-optode distance between a source (transmitter) and detector (receiver) optode. Different fNIRS caps will vary in spatial resolution, and as such the requirements for what constitutes a short or long channel will vary depending on the hardware design and/or user preference. If your design has a short channel included, you will need to use this method. If a channel is longer than the allowable distance, it will be removed entirely. If a short channel is requested but no channels are found below the set threshold, the processing of the participant will fail.
**Parameters:**
- SHORT_CHANNEL (bool, deafult=True): True if the data has a short channel, False if the data does not have a short channel.
- SHORT_CHANNEL_THRESH (float, default=0.015): Maximum distance the short channel can be in metres.
- LONG_CHANNEL_THRESH (float, default=0.045): Maximum distance a channel can be in metres before it is removed.
- SHORT_CHANNELS (bool, deafult=True): True if the data has at least one short channel, False if the data does not have at least one short channel.
- ⚠️LONG_CHANNELS (bool, deafult=True): True if channels exceeding LONG_CHANNELS_THRESH should be removed, False if the channels exceeding LONG_CHANNELS_THRESH should not be removed.
- SHORT_CHANNELS_THRESH (float, default=0.015): Maximum distance that short channels can be in metres.
- LONG_CHANNELS_THRESH (float, default=0.045): Maximum distance that long channels can be in metres.
Note: If SHORT_CHANNEL is set to False, SHORT_CHANNEL_THRESH & SHORT_CHANNEL_REGRESSION will be ignored.
Note: If ⚠️LONG_CHANNELS is set to False, LONG_CHANNELS_THRESH will be ignored.
## Heart Rate
Identifies the heart rate of the participant. Heart rate in fNIRS is considered noise, and as such it is important to identify it and regress it out. Underneath, two methods of calculating heart rate from two different scientific libraries are used in an attempt to get more accurate results. There are options to strips a couple of seconds from the start and end of the file to ensure that the heart rate calculation does not see potential spikes from a cap being placed after recording started or removed before recording ended. Because the calculations for heart rate can spike over time, there are parameters set to encapsulate the heart rate into an acceptable minimum and maximum value. Each individual data point calculation for heart rate will be averaged into a window defined by a user parameter to provide a smoother result.
@@ -57,22 +59,30 @@ Identifies the heart rate of the participant. Heart rate in fNIRS is considered
**Parameters:**
- HEART_RATE (bool, default=True): True if the heart rate should be identified, False if the heart rate should not be identified.
- SECONDS_TO_STRIP_HR (int, default=5): How many seconds to remove at the start and end of the file for the heart rate calculation to eliminate artifacts from cap placement or removal.
- ⚠️HR_LOW_FREQ (float, default=0.8): TBD.
- ⚠️HR_HIGH_FREQ (float, default=2.5): TBD.
- ⚠️HR_SEARCH_MIN (int, default=30): TBD.
- ⚠️HR_SEARCH_MAX (int, default=200): TBD.
- MAX_LOW_HR (int, default=40): Minimum value in BPM the heart rate can be for an individual data point. Any values lower than this will be capped to this value.
- MAX_HIGH_HR (int, default=200): Maximum value in BPM the heart rate can be for an individual data point. Any values higher than this will be capped to this value.
- SMOOTHING_WINDOW_HR (int, default=100): Number of unique datapoints to be averaged into a single data point to create a smoother heart rate.
- ⚠️SMOOTHING_WINDOW_HR (int, default=100): Number of unique datapoints to be averaged into a single data point to create a smoother heart rate.
- HEART_RATE_WINDOW (int, default=25): Purely for visualization. Will create an estimation window ± this value for the BPM calculation.
Note: If HEART_RATE is set to false, SECONDS_TO_STRIP_HR, MAX_LOW_HR, MAX_HIGH_HR, SMOOTHING_WINDOW_HR, & HEART_RATE_WINDOW will be ignored.
Note: If HEART_RATE is set to false, SECONDS_TO_STRIP_HR, ⚠️HR_LOW_FREQ, ⚠️HR_HIGH_FREQ, ⚠️HR_SEARCH_MIN, ⚠️HR_SEARCH_MAX, MAX_LOW_HR, MAX_HIGH_HR, SMOOTHING_WINDOW_HR, HEART_RATE_WINDOW, & SCI_USE_HEAR_RATE_BAND will be ignored.
## Scalp Coupling Index
Calculates the scalp coupling index of the participant over multiple time windows, and will report a pass or fail for each channel and time window based on a user defined threshold. The scalp coupling index is an important quality metric that calculates the quality of the connection between the participants scalp and the fNIRS cap. This process is documented better here at <this link>. The time windows start at time 0, and if the end of the file is not a complete window, the SCI is simply calculated over the remaining time. If the entire channel averaged across all windows do not meet the threshold value, then the channel will be marked as a bad channel.
**Parameters:**
- SCI (bool, default=True): True if SCI should be calculated, False if SCI should not be calculated.
- SCI_USE_HEAR_RATE_BAND (bool, default=True): TBD.
- ⚠️SCI_LOW_FREQ (float, default=0.7): TBD.
- ⚠️SCI_HIGH_FREQ (float, default=1.5): TBD.
- SCI_TIME_WINDOW (int, default=3): Value in seconds to evaluate the signal over repeatedly.
- SCI_THRESHOLD (float, default=0.6): Value between 0-1 where 1 is perfect coupling and 0 is poor coupling. A good threshold value would be at least over 0.5.
Note: If SCI is set to False, SCI_TIME_WINDOW & SCI_THRESHOLD will be ignored.
Note: If SCI is set to False, SCI_USE_HEAR_RATE_BAND, SCI_TIME_WINDOW, & SCI_THRESHOLD will be ignored.
Note: If SCI_USE_HEAR_RATE_BAND is set to False, ⚠️SCI_LOW_FREQ & ⚠️SCI_HIGH_FREQ will be ignored.
## Signal to Noise Ratio
Calculates the signal to noise ratio, and will report a pass or fail for each channel based on a user defined threshold. The signal to noise ratio is an important quality metric that calculates how much of the observed signal was noise versus how much of it was a useful signal. This process is documented better here at <this link>. There is no concrete value at which there is minimal noise, with different individuals reporting varying thresholding values. If the entire channel does not meet the threshold value, then the channel will be marked as a bad channel.
@@ -80,8 +90,12 @@ Calculates the signal to noise ratio, and will report a pass or fail for each ch
**Parameters:**
- SNR (bool, default=True): True if SNR should be calculated, False if SNR should not be calculated.
- SNR_THRESHOLD (float, default=5.0): Any value >=0, where 0 is considered extreme noise.
- ⚠️SNR_SIGNAL_LOW_FREQ (float, default=0.01): TBD.
- ⚠️SNR_SIGNAL_HIGH_FREQ (float, default=0.5): TBD.
- ⚠️SNR_NOISE_LOW_FREQ (float, default=1.0): TBD.
- ⚠️SNR_NOISE_HIGH_FREQ (float, default=10.0): TBD.
Note: If SNR is set to false, SNR_THRESHOLD will be ignored.
Note: If SNR is set to false, SNR_THRESHOLD, ⚠️SNR_SIGNAL_LOW_FREQ, ⚠️SNR_SIGNAL_HIGH_FREQ, ⚠️SNR_NOISE_LOW_FREQ, & ⚠️SNR_NOISE_HIGH_FREQ will be ignored.
## Peak Spectral Power
Calculates the peak spectral power of the participant over multiple time windows, and will report a pass or fail for each channel and window based on a user defined threshold. The peak spectral power is an important quality metric that calculates the amplitude or strength of a frequency component that is most prominent in a particular frequency range or spectrum. This process is documented better here at <this link>. The time windows start at time 0, and if the end of the file is not a complete window, the PSP is simply calculated over the remaining time. If the entire channel averaged across all windows do not meet the threshold value, then the channel will be marked as a bad channel.
@@ -90,17 +104,19 @@ Calculates the peak spectral power of the participant over multiple time windows
- PSP (bool, default=True): True if PSP should be calculated, False if PSP should not be calculated.
- PSP_TIME_WINDOW (int, default=3): Value in seconds to evaluate the signal over repeatedly.
- PSP_THRESHOLD (float, default=0.1): Value between 0-1 where 1 is perfect spectral power and 0 is poor spectral power. A good threshold value would be at least over 0.1
- ⚠️PSP_LOW_FREQ (float, default=0.7): TBD.
- ⚠️PSP_HIGH_FREQ (float, default=1.5): TBD.
Note: If PSP is set to False, PSP_TIME_WINDOW & SCI_THRESHOLD will be ignored.
Note: If PSP is set to False, PSP_TIME_WINDOW, PSP_THRESHOLD, ⚠️PSP_LOW_FREQ, & ⚠️PSP_HIGH_FREQ will be ignored.
## Coefficient of Variation
Calculates the coefficient of variance of the participant. This quality metric is a way to measure the "relative noise" of a signal. If the entire channel averaged does not remain under the threshold value, then the channel will be marked as a bad channel.
**Parameters:**
- CV (bool, default=True): True if the Coefficient of Variation should be calculated, False if the Coefficient of Variation should not be calculated.
- CV_THRESHOLD (int, default=20): Value between 0-100 representing a percentage, where 0 is an extremely steady signal and 100 is a massively fluctuating signal. A good threshold value would be under 25.
- COEFF_VAR (bool, default=True): True if the Coefficient of Variation should be calculated, False if the Coefficient of Variation should not be calculated.
- COEFF_VAR_THRESHOLD (int, default=20): Value between 0-100 representing a percentage, where 0 is an extremely steady signal and 100 is a massively fluctuating signal. A good threshold value would be under 25.
Note: If CV is set to False, CV_THRESHOLD will be ignored.
Note: If COEFF_VAR is set to False, COEFF_VAR_THRESHOLD will be ignored.
## Median Absolute Deviation
Calculates the mean absolute deviation of the participant. This quality metric identifies outliers based on how much their signal swings compared to all other channels. If the entire channel averaged does not remain under the threshold value, then the channel will be marked as a bad channel.
@@ -111,39 +127,41 @@ Calculates the mean absolute deviation of the participant. This quality metric i
Note: If MAD is set to False, MAD_THRESHOLD will be ignored.
## Sensor Dropout
## Power Spectral Density Noise
Calculates how much noise is present when viewing the power spectral density of the data. This quality metric checks for higher frequency noises that should not be present at a certain frequency threshold which is likely indicative of electronic noise. If the channel does not remain under the threshold value after the target frequency, then the channel will be marked as a bad channel.
**Parameters:**
- PSD_NOISE (bool, default=True): True if PSD Noise should be calculated, False if PSD Noise should not be calculated.
- TARGET_FREQ_DIV (int, default=4): Target frequency divisor to examine. If the SNIRF file is at 25Hz and this value is set to 4, the frequency to examine would be 6.25. Not recommended to divide by a big number, as then you will likely be looking at meaningful data.
- DB_LIMIT (int, default=-60): What value the PSD has to be below when the target frequency is encountered.
- ⚠️PSD_MIN_FREQ (float, default=0.1): TBD.
- ⚠️PSD_TARGET_BANDWIDTH (float, default=0.2): TBD.
Note: If PSD_NOISE is set to False, TARGET_FREQ_DIV & DB_LIMIT will be ignored.
Note: If PSD_NOISE is set to False, TARGET_FREQ_DIV, DB_LIMIT, ⚠️PSD_MIN_FREQ, & ⚠️PSD_TARGET_BANDWIDTH will be ignored.
## Channel Variance
## Sensor Dropout
Calculates how much a channel varied throughout the experiment. This quality metric will examine the variance of the first 25% of the recording to the last 25% of the recording and compares the variance between them. If there is low variance, it is likely that the sensor was dislodged at some point during the experiment. If the variance does not meet the threshold value, then the channel will be marked as a bad channel.
**Parameters:**
- CHANNEL_VAR (bool, default=True): True if channel variance should be calculated, False if channel variance should not be calculated.
- CHANNEL_THRESH (float, default=0.05): Value between 0 and 1 representing how much variance to require. A good threshold value would be >=0.05.
- SENSOR_DROPOUT (bool, default=True): True if channel variance should be calculated, False if channel variance should not be calculated.
- SENSOR_DROPOUT_VARIANCE_THRESHOLD (float, default=0.05): Value between 0 and 1 representing how much variance to require. A good threshold value would be >=0.05.
Note: If CHANNEL_VAR is set to False, CHANNEL_THRESH will be ignored.
Note: If SENSOR_DROPOUT is set to False, SENSOR_DROPOUT_VARIANCE_THRESHOLD will be ignored.
## Bad Channels Handling
Determines what to do with channels that were marked as bad in the previous quality steps. There are three options to choose from - “Interpolate”, “Remove”, or “None”. Interpolate will utilize the closest channels to the bad channel and interpolate what the bad channels signal should have been and generate a signal from them. This requires a minimum number of neighbours and the maximum distance a neighbour can be away from the bad channel in metres. If there are not enough neighbours located inside the distance threshold, the offending channel will be unmarked as bad and continue on as if the handling was set to None. Remove will simply drop the channels from memory. None will unflag all the bad channels, and continue as if they are good channels. If bad channels are being removed, an option to completely eliminate a participant is present if they do not meet a specified threshold.
**Parameters:**
- BAD_CHANNELS_HANDLING (list, default=Interpolate): How the bad channels should be dealt with.
- MAX_DIST (float, default=0.03): Maximum distance to look for potential neighbours to interpolate from in meters.
- MIN_NEIGHBOURS (int, default=2): Minimum number of neighbours inside the distance threshold required to interpolate from.
- ⚠️MAX_DIST (float, default=0.03): Maximum distance to look for potential neighbours to interpolate from in meters.
- ⚠️MIN_NEIGHBOURS (int, default=2): Minimum number of neighbours inside the distance threshold required to interpolate from.
- MAX_BAD_CHANNLES (int, default=12): Maximum number of bad channels a participant can have before they are eliminated. This considers hbO and hbR as separate channels, and this value is exclusive.
Note: If BAD_CHANNELS_HANDLING is not set to “Interpolate”, MAX_DIST and MIN_NEIGHBOURS will be ignored.
Note: If BAD_CHANNELS_HANDLING is not set to “Remove”, MAX_BAD_CHANNELS will be ignored.
Note: If BAD_CHANNELS_HANDLING is not set to `Interpolate`, ⚠️MAX_DIST and ⚠️MIN_NEIGHBOURS will be ignored.
Note: If BAD_CHANNELS_HANDLING is not set to `Remove`, MAX_BAD_CHANNELS will be ignored.
## Optical Density
Calculates the optical density of the data. This process is documented better at [this link.](https://mne.tools/stable/generated/mne.preprocessing.nirs.optical_density.html) This process is self contained and is required step, so it does not contain any user exposed parameters.
Calculates the optical density of the data. This process is documented better at [this link.](https://mne.tools/stable/generated/mne.preprocessing.nirs.optical_density.html) This process is self contained and is a required step, so it does not contain any user exposed parameters, nor does it's header appear in the application. Even though it does not appear, since this is a required step all logic is performed regardless.
## Temporal Derivative Distribution Repair filtering
Calculates and applies temporal derivative distribution repair filtering to the data. This is a method that removes baseline shift and spike artifacts from the data. This process is documented better at [this link.](https://mne.tools/stable/generated/mne.preprocessing.nirs.temporal_derivative_distribution_repair.html)
@@ -163,7 +181,14 @@ TBD.
Note: If WAVELET is set to False, IQR, WAVELET_TYPE, & WAVELET_LEVEL will be ignored.
## Haemoglobin Concentration
Calculates the haemoglobin concentration of the data. This process is documented better at [this link](https://mne.tools/stable/generated/mne.preprocessing.nirs.beer_lambert_law.html). This process is self contained and is required step, so it does not contain any user exposed parameters.
Calculates the haemoglobin concentration of the data. This process is documented better at [this link](https://mne.tools/stable/generated/mne.preprocessing.nirs.beer_lambert_law.html). This process is a required step, with all parameters being advanced so the header will only appear if `Show Advanced Parameters` is selected.
**Parameters:**
- ⚠️OVERRIDE_PPF (bool, default=False): TBD.
- ⚠️PPF_LOWER_WAVELENGTH (float, default=6.0): TBD.
- ⚠️PPF_UPPER_WAVELENGTH (float, default=6.0): TBD.
Note: If OVERRIDE_PPF is set to False, PPF_LOWER_WAVELENGTH & PPF_UPPER_WAVELENGTH will be ignored.
## Enhance Negative Correlation
Calculates and applies enhanced negative correlation to the data. This is a signal improvement algorithm that is based on the negative correlation between oxygenated and deoxygenated hemoglobin dynamics. This process is documented better at [this link](https://mne.tools/mne-nirs/dev/generated/mne_nirs.signal_enhancement.enhance_negative_correlation.html).
@@ -176,24 +201,42 @@ Calculates and applies a bandpass filter to the data based on user defined thres
**Parameters:**
- FILTER (bool, default=True): True if a bandpass filter should be applied to the data, False if a bandpass filter should not be applied to the data.
- FILTER_ALGORITHM (list, default=fir): TBD.
- L_FREQ (float, default=0.005): Low cutoff frequency. Any frequencies lower than this value will be removed.
- H_FREQ (float, default=0.03): High cutoff frequency. Any frequencies higher than this value will be removed.
- L_TRANS_BANDWIDTH (float, default=0.002): How wide the transitional period should be on the lower end.
- H_TRANS_BANDWITH (float, default=0.002): How wide the transitional period should be on the higher end.
- ⚠️L_TRANS_BANDWIDTH (float, default=0.002): How wide the transitional period should be on the lower end.
- ⚠️H_TRANS_BANDWITH (float, default=0.002): How wide the transitional period should be on the higher end.
- ⚠️FILTER_LENGTH (str, default=auto): TBD.
- ⚠️FILTER_PHASE (list, default=zero): TBD.
- ⚠️FIR_WINDOW (list, default=hamming): TBD.
- ⚠️FIR_DESIGN (list, default=firwin): TBD.
- ⚠️FILTER_PAD (list, default=reflect_limited): TBD.
- ⚠️FILTER_N_JOBS (int, default=1): TBD.
Note: If FILTER is set to False, L_FREQ, H_FREQ, L_TRANS_BANDWITH, & H_TRANS_BANDWITH will be ignored.
Note: If FILTER is set to False, L_FREQ, H_FREQ, ⚠️L_TRANS_BANDWITH, ⚠️H_TRANS_BANDWITH, ⚠️FILTER_PAD, & ⚠️FILTER_N_JOBS will be ignored.
Note: If FILTER_ALGORITHM is not set to `fir`, ⚠️FILTER_LENGTH, ⚠️FILTER_PHASE, ⚠️FIR_WINDOW, & ⚠️FIR_DESIGN will be ignored.
## Extracting Events
Extracts events from the data. This step currently does not expose any user defined parameters, but may in the future. TBD.
Extracts events from the data. All parameters in this section are advanced so the header will only appear if `Show Advanced Parameters` is selected.
**Parameters:**
- ⚠️EVENTS (bool, default=True): TBD.
- ⚠️EVENTS_ID (str, default=auto): TBD.
- ⚠️EVENT_REGEX (str, default=`^(?![Bb][Aa][Dd]|[Ee][Dd][Gg][Ee]).*$`): TBD.
Note: If ⚠️EVENTS is set to False, ⚠️EVENTS_ID, ⚠️EVENT_REGEX, & ⚠️EPOCHS will be ignored.
## Epoch Calculations
Performs various epoch calculations from the data. Can handle epochs with “shift” or “strict”. TBD.
**Parameters:**
- ⚠️EPOCHS (bool, default=True): TBD.
- EPOCH_HANDLING (list, default=shift): How the epochs should be handled if two or more occur at the exact same time.
- MAX_SHIFT (int, default=5): Number of indexes to look ahead and see if there is a valid index to shift an event to. If no valid indexes are found in this threshold, will treat the event as “strict”. Recommended to keep this value very low to not shift an event far from when it actually occurred.
- ⚠️MAX_SHIFT (int, default=5): Number of indexes to look ahead and see if there is a valid index to shift an event to. If no valid indexes are found in this threshold, will treat the event as “strict”. Recommended to keep this value very low to not shift an event far from when it actually occurred.
- T_MIN (int, default=-5): Seconds before the epoch to be used.
- T_MAX (int, default=15): Seconds after the epoch to be used.
- REJECT_EPOCHS (bool, default=True): TBD.
- ⚠️REJECT_HBO_THRESHOLD (float, default=`8e-06`): TBD.
Note: If EPOCH_HANDLING is not set to “shift”, MAX_SHIFT will be ignored.
@@ -205,13 +248,12 @@ Generates a design matrix from the data. DRIFT_MODEL can be “cosine” or “p
- RESAMPLE_FREQ (int, default=1): Frequency in Hz to downsample to.
- HRF_MODEL (list, default=fir): What HRF model to use. See the “?” on this parameter in the application to get a complete list of valid values.
- STIM_DUR (float, default=0.5): Length of the stimulus for a block design HRF model, otherwise how wide a bin should be in seconds.
- DRIFT_MODEL (list, default=cosine): What type of drift model should be used.
- HIGH_PASS (float, default=0.01): High pass frequency in Hz in case of a cosine drift model.
- DRIFT_ORDER (int, default=1): Order of the drift model in case of a polynomial drift model.
- FIR_DELAYS (range, default=15): Number of bins in case of the HRF_MODEL being “fir”.
- MIN_ONSET (int, default=-24): Minimal onset in seconds relative to frame times.
- OVERSAMPLING (int, default=50): Oversampling factor used in temporal convolutions.
- REMOVE_EVENTS (list, default=?): Remove events matching the names provided before generating the design matrix.
- ⚠️FIR_DELAYS (range, default=15): Number of bins in case of the HRF_MODEL being “fir”.
- ⚠️DRIFT_MODEL (list, default=cosine): What type of drift model should be used.
- ⚠️HIGH_PASS (float, default=0.01): High pass frequency in Hz in case of a cosine drift model.
- ⚠️DRIFT_ORDER (int, default=1): Order of the drift model in case of a polynomial drift model.
- ⚠️MIN_ONSET (int, default=-24): Minimal onset in seconds relative to frame times.
- ⚠️OVERSAMPLING (int, default=50): Oversampling factor used in temporal convolutions.
- SHORT_CHANNEL_REGRESSION (bool, default=True): True if short channel regression should be used when creating the design matrix, False if short channel regression should not be used when creating the design matrix. Requires a short channel to be present in the data of all participants.
Lots of unique combinations of parameters being used or not being used - see the application for more information. TBD.
@@ -221,21 +263,38 @@ Uses the previously generated design matrix to make a first level general linear
**Parameters:**
- NOISE_MODEL (str, default=ar1): Temporal variance model.
- BINS (int, default=0): The maximum number of discrete bins for the AR coef. histogram/clustering.
- N_JOBS (int, default=1): The number of CPUs to use to do the GLM computation. While -1 signifies 'all CPUs', it has been found that limiting this to a single CPU ends up being faster on higher end devices.
- ⚠️BINS (int, default=0): The maximum number of discrete bins for the AR coef. histogram/clustering.
- ⚠️N_JOBS (int, default=1): The number of CPUs to use to do the GLM computation. While -1 signifies 'all CPUs', it has been found that limiting this to a single CPU ends up being faster on higher end devices.
## Finishing Touches
## GLM Results
This stage just involves some under-the-hood calculations that will be used for further analysis. This process is self contained and is required step, so it does not contain any user exposed parameters.
## Channel Results
This stage just involves some under-the-hood calculations that will be used for further analysis. This process is self contained and is required step, so it does not contain any user exposed parameters.
## Region of Interest Results
TBD.
**Parameters:**
- JSON_LOCATION (file, default=`None`): TBD.
## Contrast Results
This stage just involves some under-the-hood calculations that will be used for further analysis. This process is self contained and is required step, so it does not contain any user exposed parameters.
## Finishing Up
This stage just involves some under-the-hood calculations that will be used for further analysis. This process is self contained and is required step, so it does not contain any user exposed parameters.
## Other
Section that contains other variables that can be important but do not affect the analysis in any shape or form.
Section containing other variables that can be important but do not affect the analysis in any shape or form.
**Parameters:**
- TIME_WINDOW_START (int, default=0): Starting value for the x-axis of an image that will only be generated if HRF_MODEL is set to “fir”. Will be deprecated in the future in preference of generating the bounds automatically.
- TIME_WINDOW_END (int, default=15): Ending value for the x-axis of an image that will only be generated if HRF_MODEL is set to “fir”. Will be deprecated in the future in preference of generating the bounds automatically.
- MAX_WORKERS (int, default=6): How many SNIRF files can be processed simultaneously. More powerful devices can specify a higher value, but must have both the CPU and RAM available. It is estimated that an individual participant will require a peak of around 2GB of RAM. Will allow for the value of 'auto' in the future.
- VERBOSITY (bool, default=False): True if the log file should contain more details, False if the log file should only contain minimal details. Should be set to True when raising an issue with the application to provide helpful information to the developer.
- MAX_WORKERS (int, default=6): How many .snirf files can be processed simultaneously. More powerful devices can specify a higher value, but must have both the CPU and RAM available. It is estimated that an individual participant will require a peak of around 2GB of RAM. Will allow for the value of 'auto' in the future.
- ⚠️VERBOSITY (bool, default=False): True if the log file should contain more details, False if the log file should only contain minimal details. Should be set to True when raising an issue with the application to provide helpful information to the developer.
# What's Next
Congratulations! You now understand what each parameter does and hopefully have an idea of the best values for your study. See the [Processing & Analysis](https://git.research.dezeeuw.ca/tyler/flares/wiki/Processing-%26-Analysis) Wiki page to continue your journey.