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update references in help texts #703

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22 changes: 15 additions & 7 deletions src/pspm_convert_ecg2hb.m
Original file line number Diff line number Diff line change
Expand Up @@ -33,12 +33,25 @@
% │ [def. 0.36s].
% └.channel_action: ['add'/'replace', default as 'replace']
% Defines whether the new channel should be added or
% the previous outputs of this function should be replaced.
%
% ● Output
% channel_index: index of channel containing the processed data
% quality_info: generated if options.debugmode == 1
%
% ● Reference
% Pan J & Tomkins WJ (1985). A Real-Time QRS Detection Algorithm. IEEE
% Transactions on Biomedical Engineering, 32, 230-236.
% [1] Adjusted algorithm:
% Paulus PC, Castegnetti G, & Bach DR (2016). Modeling event-related
% heart period responses. Psychophysiology, 53, 837-846.
% [2] Original algorithm:
% Pan J & Tomkins WJ (1985). A Real-Time QRS Detection Algorithm. IEEE
% Transactions on Biomedical Engineering, 32, 230-236.
%
% ● History
% Introduced in PsPM 3.0
% Written in 2013-2015 Philipp C Paulus & Dominik R Bach
% (Technische Universitaet Dresden, University of Zurich)
% Updated in 2022 Teddy Chao
% ● Developer's Notes
% ▶︎ Changes from the original Pan & Tompkins algorithm
% filter: P. & T. intend to achieve a pass band from 5-15 Hz with a
Expand Down Expand Up @@ -99,11 +112,6 @@
%
% R: Vector of the same length as the raw data, containing
% information on the position of the QRS complexes.
% ● History
% Introduced in PsPM 3.0
% Written in 2013-2015 Philipp C Paulus & Dominik R Bach
% (Technische Universitaet Dresden, University of Zurich)
% Updated in 2022 Teddy Chao

%% Initialise
global settings
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5 changes: 4 additions & 1 deletion src/pspm_convert_ecg2hb_amri.m
Original file line number Diff line number Diff line change
Expand Up @@ -70,13 +70,16 @@
% does not replace the raw data channel, but a previously
% stored heartbeat channel.
% (Default: 'replace')
% ● Output
% ● Outputs
% sts: status marker showing whether the function works normally.
% channel_index: index of channel containing the processed data
%
% ● References
% [1] Liu, Zhongming, et al. "Statistical feature extraction for artifact
% removal from concurrent fMRI-EEG recordings." Neuroimage 59.3 (2012):
% 2073-2087.
% [2] http://www.amri.ninds.nih.gov/software.html
%
% ● History
% Written in 2019 by Eshref Yozdemir (University of Zurich)
% Updated in 2022 by Teddy Chao
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5 changes: 5 additions & 0 deletions src/pspm_convert_hb2hp.m
Original file line number Diff line number Diff line change
Expand Up @@ -36,6 +36,11 @@
% heart periods in seconds. Default is 0.2.
% ● Output
% channel_index: index of channel containing the processed data
%
% % ● Reference
% [1] Paulus PC, Castegnetti G, & Bach DR (2016). Modeling event-related
% heart period responses. Psychophysiology, 53, 837-846.
%
% ● History
% Introduced in PsPM 3.0
% Written in 2008-2015 by Dominik R Bach (Wellcome Trust Centre for Neuroimaging)
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20 changes: 12 additions & 8 deletions src/pspm_dcm.m
Original file line number Diff line number Diff line change
Expand Up @@ -145,16 +145,20 @@
% the trials use much less than available amount of samples in both case
% (1) and (2). Instead, we aim to use as much data as possible in (1), and
% perform (2) only if this edge case is not present.
%
% ● References
% 1.Bach DR, Daunizeau J, Friston KJ, Dolan RJ (2010).
% Dynamic causal modelling of anticipatory skin conductance changes.
% Biological Psychology, 85(1), 163-70
% 2.Staib, M., Castegnetti, G., & Bach, D. R. (2015).
% Optimising a model-based approach to inferring fear learning from
% skin conductance responses.
% Journal of Neuroscience Methods, 255, 131-138.
% [1] Model development:
% Bach DR, Daunizeau J, Friston KJ, Dolan RJ (2010). Dynamic causal
% modelling of anticipatory skin conductance changes. Biological
% Psychology, 85(1), 163-70
% [2] Model validation and improvement:
% Staib, M., Castegnetti, G., & Bach, D. R. (2015). Optimising a
% model-based approach to inferring fear learning from skin
% conductance responses. Journal of Neuroscience Methods, 255,
% 131-138.
%
% ● History
% Introduced in PsPM 5.1.0
% Introduced in PsPM 3.0
% Written in 2010-2021 by Dominik R Bach (Wellcome Centre for Human Neuroimaging, UCL)

%% 1 Initialise
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7 changes: 4 additions & 3 deletions src/pspm_emg_pp.m
Original file line number Diff line number Diff line change
Expand Up @@ -29,9 +29,10 @@
% ● Output
% channel_index: index of channel containing the processed data
% ● References
% [1] Khemka S, Tzovara A, Gerster S, Quednow BB, Bach DR (2016).
% Modeling Startle Eyeblink Electromyogram to Assess Fear Learning.
% Psychophysiology
% [1] Khemka S, Tzovara A, Gerster S, Quednow BB, Bach DR (2017).
% Modelling startle eye blink electromyogram to assess fear learning.
% Psychophysiology, 54, 202-214.
%
% ● History
% Introduced in PsPM 3.1
% Written in 2009-2016 by Tobias Moser (University of Zurich)
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96 changes: 72 additions & 24 deletions src/pspm_glm.m
Original file line number Diff line number Diff line change
Expand Up @@ -94,6 +94,78 @@
% .stats_exclude but not used further.
% ● Outputs
% glm: a structure 'glm' which is also written to file
% ● References
%
% Skin conductance response analysis
% ----------------------------------
% [1] GLM for SCR:
% Bach DR, Flandin G, Friston KJ, Dolan RJ (2009). Time-series analysis for
% rapid event-related skin conductance responses. Journal of Neuroscience
% Methods, 184, 224-234.
% [2] Canonical skin conductance response function, and GLM assumptions:
% Bach DR, Flandin G, Friston KJ, Dolan RJ (2010). Modelling event-related
% skin conductance responses. International Journal of Psychophysiology,
% 75, 349-356.
% [3] Validating GLM assumptions with intraneural recordings:
% Gerster S, Namer B, Elam M, Bach DR (2018). Testing a linear time
% invariant model for skin conductance responses by intraneural
% recording and stimulation. Psychophysiology, 55, e12986.
% [4] Fine-tuning of SCR CLM:
% Bach DR, Friston KJ, Dolan RJ (2013). An improved algorithm for
% model-based analysis of evoked skin conductance responses. Biological
% Psychology, 94, 490-497.
% [5] SCR GLM validation and comparison with Ledalab:
% Bach DR (2014). A head-to-head comparison of SCRalyze and Ledalab, two
% model-based methods for skin conductance analysis. Biological Psychology,
% 103, 63-88.
%
% Pupil size analysis
% -------------------
% [6] GLM for fear-conditioned pupil dilation:
% Korn CK, Staib M, Tzovara A, Castegnetti G, Bach DR (2017).
% A pupil size response model to assess fear learning.
% Psychophysiology, 54, 330-343.
%
% Heart rate/period analysis
% --------------------------
% [7] GLM for evoked heart period responses:
% Paulus PC, Castegnetti G, & Bach DR (2016). Modeling event-related
% heart period responses. Psychophysiology, 53, 837-846.
% [8] GLM for fear-conditioned bradycardia:
% Castegnetti G, Tzovara A, Staib M, Paulus PC, Hofer N, & Bach DR
% (2016). Modelling fear-conditioned bradycardia in humans.
% Psychophysiology, 53, 930-939.
%
% Respiration analysis
% --------------------
% [9] GLM for evoked respiratory responses:
% Bach DR, Gerster S, Tzovara A, Castegnetti G (2016). A linear model
% for event-related respiration responses. Journal of Neuroscience
% Methods, 270, 174-155.
% [10] GLM for fear-conditioned respiration amplitude responses
% Castegnetti G, Tzovara A, Staib M, Gerster S, Bach DR (2017).
% Assessing fear learning via conditioned respiratory amplitude
% responses. Psychophysiology, 54, 215-223.
%
% Startle eye-blink analysis
% --------------------------
% [11] GLM for startle eye-blink responses:
% Khemka S, Tzovara A, Gerster S, Quednow B and Bach DR (2017)
% Modeling Startle Eyeblink Electromyogram to Assess
% Fear Learning. Psychophysiology
%
% Eye gaze analysis
% -----------------
% [12] GLM for saccadic scanpath speed
% Xia Y, Melinščak F, Bach DR (2020). Saccadic scanpath length: an
% index for human threat conditioning. Behavior Research Methods, 53,
% 1426-1439.
%
% ● History
% Introduced in PsPM 3.1
% Written in 2008-2016 by Dominik R Bach (Wellcome Trust Centre for Neuroimaging)
% Maintained in 2022 by Teddy Chao (UCL)
%
% ● Developer's Notes
% TIMING - multiple condition file(s) or struct variable(s):
% The structure is equivalent to SPM2/5/8/12 (www.fil.ion.ucl.ac.uk/spm),
Expand Down Expand Up @@ -121,30 +193,6 @@
% names = {'condition a', 'condition b'};
% onsets = {[1 2 3], [4 5 6]};
% save('testfile', 'names', 'onsets');
% ● References
% [1] GLM for SCR:
% Bach DR, Flandin G, Friston KJ, Dolan RJ (2009). Time-series analysis for
% rapid event-related skin conductance responses. Journal of Neuroscience
% Methods, 184, 224-234.
% [2] SCR: Canonical response function, and GLM assumptions:
% Bach DR, Flandin G, Friston KJ, Dolan RJ (2010). Modelling event-related
% skin conductance responses. International Journal of Psychophysiology,
% 75, 349-356.
% [3] Fine-tuning of SCR CLM:
% Bach DR, Friston KJ, Dolan RJ (2013). An improved algorithm for
% model-based analysis of evoked skin conductance responses. Biological
% Psychology, 94, 490-497.
% [4] SCR GLM validation and comparison with Ledalab:
% Bach DR (2014). A head-to-head comparison of SCRalyze and Ledalab, two
% model-based methods for skin conductance analysis. Biological Psychology,
% 103, 63-88.
% [5] SEBR GLM: Khemka S, Tzovara A, Gerster S, Quednow B and Bach DR (2017)
% Modeling Startle Eyeblink Electromyogram to Assess
% Fear Learning. Psychophysiology
% ● History
% Introduced in PsPM 3.1
% Written in 2008-2016 by Dominik R Bach (Wellcome Trust Centre for Neuroimaging)
% Maintained in 2022 by Teddy Chao (UCL)

%% 1 Initialise
global settings
Expand Down
4 changes: 4 additions & 0 deletions src/pspm_process_illuminance.m
Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,10 @@
% out: has same size as ldata and contains either the
% processed data or contains the path to the .mat file
% where the data has been stored to
% ● References
% Korn CW & Bach DR (2016). A solid frame for the window on cognition:
% Modelling event-related pupil responses. Journal of Vision, 16:28,1-6.
%
% ● History
% Introduced In PsPM 3.1
% Written in 2015 by Tobias Moser, Christoph Korn (University of Zurich)
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6 changes: 6 additions & 0 deletions src/pspm_resp_pp.m
Original file line number Diff line number Diff line change
Expand Up @@ -33,6 +33,12 @@
% corresponding channel should be replaced.
% ● Output
% channel_index: index of channel containing the processed data
%
% ● References
% [1] Bach DR, Gerster S, Tzovara A, Castegnetti G (2016). A linear model
% for event-related respiration responses. Journal of Neuroscience
% Methods, 270, 174-155.
%
% ● History
% Introduced in PsPM 3.0
% Written in 2015 by Dominik R Bach (Wellcome Trust Centre for Neuroimaging)
Expand Down
22 changes: 14 additions & 8 deletions src/pspm_sf.m
Original file line number Diff line number Diff line change
Expand Up @@ -52,15 +52,21 @@
% │ display intermediate windows.
% └──.missingthresh: [numeric] [default: 2] [unit: second]
% threshold value for controlling missing epochs.
%
% ● References
% 1.[DCM for SF]
% Bach DR, Daunizeau J, Kuelzow N, Friston KJ, Dolan RJ (2010). Dynamic
% causal modelling of spontaneous fluctuations in skin conductance.
% Psychophysiology, 48, 252-257.
% 2.[AUC measure]
% Bach DR, Friston KJ, Dolan RJ (2010). Analytic measures for the
% quantification of arousal from spontanaeous skin conductance
% fluctuations. International Journal of Psychophysiology, 76, 52-55.
% [1] DCM for SF:
% Bach DR, Daunizeau J, Kuelzow N, Friston KJ, Dolan RJ (2010). Dynamic
% causal modelling of spontaneous fluctuations in skin conductance.
% Psychophysiology, 48, 252-257.
% [2] MP approximation:
% Bach DR, Staib M (2015). A matching pursuit algorithm for inferring
% tonic sympathetic arousal from spontaneous skin conductance
% fluctuations. Psychophysiology, 52, 1106-12.
% [3] AUC for SF:
% Bach DR, Friston KJ, Dolan RJ (2010). Analytic measures for the
% quantification of arousal from spontanaeous skin conductance
% fluctuations. International Journal of Psychophysiology, 76, 52-55.
%
% ● Developer's Note
% the output also contains a field .time that contains the inversion time
% in ms (for DCM and MP)
Expand Down
6 changes: 6 additions & 0 deletions src/pspm_sf_auc.m
Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,12 @@
% Bach DR, Friston KJ, Dolan RJ (2010). Analytic measures for the
% quantification of arousal from spontanaeous skin conductance
% fluctuations. International Journal of Psychophysiology, 76, 52-55.
%
% ● References
% [1] Bach DR, Friston KJ, Dolan RJ (2010). Analytic measures for the
% quantification of arousal from spontanaeous skin conductance
% fluctuations. International Journal of Psychophysiology, 76, 52-55.
%
% ● History
% Introduced In PsPM 3.0
% Written in 2008-2015 by Dominik R Bach (Wellcome Trust Centre for Neuroimaging)
Expand Down
2 changes: 2 additions & 0 deletions src/pspm_sf_dcm.m
Original file line number Diff line number Diff line change
Expand Up @@ -32,10 +32,12 @@
% [numeric] [default: 2] [unit: second]
% threshold value for controlling missing epochs,
% which is originally inherited from SF
%
% ● References
% Bach DR, Daunizeau J, Kuelzow N, Friston KJ, & Dolan RJ (2011). Dynamic
% causal modelling of spontaneous fluctuations in skin conductance.
% Psychophysiology, 48, 252-57.
%
% ● History
% Introduced In PsPM 3.0
% Written in 2008-2015 by Dominik R Bach (Wellcome Trust Centre for Neuroimaging)
Expand Down
4 changes: 4 additions & 0 deletions src/pspm_sf_mp.m
Original file line number Diff line number Diff line change
Expand Up @@ -34,6 +34,10 @@
% false. If set to true this will add a further field 'D' to the
% output struct. Default is false.
% ● References
% [1] Bach DR, Staib M (2015). A matching pursuit algorithm for inferring
% tonic sympathetic arousal from spontaneous skin conductance
% fluctuations. Psychophysiology, 52, 1106-12.
%
% ● History
% Introduced in PsPM 3.0
% Written in 2008-2015 by Dominik R Bach (UZH, WTCN) last edited 18.08.2014
Expand Down
66 changes: 36 additions & 30 deletions src/pspm_tam.m
Original file line number Diff line number Diff line change
Expand Up @@ -4,29 +4,6 @@
% trial-averaged data. pspm_tam starts by extracting and averaging signal segments of
% length `model.window` from each data file individually, then averages
% these mean segments and finally fits an LTI model.
% ● Developer's Notes
% The fitting process is a residual least square minimisation where the
% predicted value is calculated as following:
% Y_predicted = input_function (*) basis_function
% with (*) represents a convolution. Only parameters of the input
% function are optimised.
% ---
% TIMING - multiple condition file(s) or struct variable(s):
% The structure is equivalent to SPM2/5/8/12 (www.fil.ion.ucl.ac.uk/spm),
% such that SPM files can be used.
% The file contains the following variables:
% - names: a cell array of string for the names of the experimental
% conditions
% - onsets: a cell array of number vectors for the onsets of events for
% each experimental condition, expressed in seconds, marker numbers, or
% samples, as specified in timeunits
% - durations (optional, default 0): a cell array of vectors for the
% duration of each event. You need to use 'seconds' or 'samples' as time
% units
% e.g. produce a simple multiple condition file by typing
% names = {'condition a', 'condition b'};
% onsets = {[1 2 3], [4 5 6]};
% save('testfilcircle_degreee', 'names', 'onsets');
% ● Arguments
% ┌───────model: [struct]
% │ ▶︎ mandatory
Expand Down Expand Up @@ -99,18 +76,47 @@
% Default value: determined by pspm_overwrite.
% ● Outputs
% tam: a structure 'tam' which is also written to file
%
% ● Reference
% Korn, C. W., & Bach, D. R. (2016). A solid frame for the window on
% cognition: Modeling event-related pupil responses. Journal of Vision,
% 16(3), 28. https://doi.org/10.1167/16.3.28
% Abivardi, A., Korn, C.W., Rojkov, I. et al. Acceleration of inferred
% neural responses to oddball targets in an individual with bilateral
% amygdala lesion compared to healthy controls. Sci Rep 13, 14550 (2023).
% https://doi.org/10.1038/s41598-023-41357-1
% [1] Model development:
% Korn CW & Bach DR (2016). A solid frame for the window on cognition:
% Modelling event-related pupil responses. Journal of Vision, 16:28,
% 1-6. https://doi.org/10.1167/16.3.28
% [2] Model application:
% Abivardi A, Korn CW, Rojkov I, Gerster S, Hurlemann R, Bach DR
% (2023). Acceleration of inferred neural responses to oddball
% targets in an individual with bilateral amygdala lesion compared to
% healthy controls. Scientific Reports, 13, 41357.
% https://doi.org/10.1038/s41598-023-41357-1
%
% ● History
% Introduced In PsPM 4.2
% Written in 2020 by Ivan Rojkov (University of Zurich)
% Maintained in 2022 by Teddy Chao (UCL)
% ● Developer's Notes
% The fitting process is a residual least square minimisation where the
% predicted value is calculated as following:
% Y_predicted = input_function (*) basis_function
% with (*) represents a convolution. Only parameters of the input
% function are optimised.
% ---
% TIMING - multiple condition file(s) or struct variable(s):
% The structure is equivalent to SPM2/5/8/12 (www.fil.ion.ucl.ac.uk/spm),
% such that SPM files can be used.
% The file contains the following variables:
% - names: a cell array of string for the names of the experimental
% conditions
% - onsets: a cell array of number vectors for the onsets of events for
% each experimental condition, expressed in seconds, marker numbers, or
% samples, as specified in timeunits
% - durations (optional, default 0): a cell array of vectors for the
% duration of each event. You need to use 'seconds' or 'samples' as time
% units
% e.g. produce a simple multiple condition file by typing
% names = {'condition a', 'condition b'};
% onsets = {[1 2 3], [4 5 6]};
% save('testfilcircle_degreee', 'names', 'onsets');


%% Initialise
global settings
Expand Down
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