PsPM：心理生理数据分析软件包

(图源自：https://bachlab.github.io/PsPM/)

第一部分：软件介绍

PsPM是一款可用于SCR(皮肤电)、ECG、EMG等心理生理信号模型分析的软件。如：在PsPM中能够实现SCR的GLM（General Linear Convolution Models）和DCM（Dynamic Causal Modelling）等的分析。

PsPM支持Spike，Biopac，arioPort，(exported) ADInstruments LabChart， (exported) Biograph Infiniti, (exported) MindMedia BioTrace, Dataq/Windaq，AckKnowledge, ScanPhysLog, EDF, (exported) Eyelink, Matlab, and Text files等多种软件的数据文件或格式。

第二部分：软件下载与安装

2.1 软件下载

可以将下列链接复制到浏览器中获取最新的软件安装包：

https://github.com/bachlab/PsPM/releases 

2.2 软件安装

1.将压缩包解压到任意文件夹下。

2.并将软件包的文件夹添加到Matlab路径中（Set path >> Add Folder >>PsPM文件所在位置）。

3.在matlab中的Command中输入“pspm”启动软件。

第三部分：软件使用介绍视频

软件使用的视频可以通过链接：https://tube.switch.ch/channels/13e2563b?order=alphabetical&view=list 进行查阅。

近期（2020.04.06-05.14）软件开发者正在进行为期7期的软件使用线上培训，目前已经进行了5期，还剩2期，感兴趣的可以通过 https://bachlab.github.io/PsPM/courses/ 获取线上参与的具体时间和方式。

第四部分：参考文献

General background of PsPM and related methods

·      BachDR & Friston KJ (2013). Model-based analysis of skin conductance responses:Towards causal models in psychophysiology. Psychophysiology, 50(1),15-22. 

·      BachDR, Castegnetti G, Korn CW, Gerster S, Melinscak F, Moser T (2018).Psychophysiological modelling - current state and future directions. Psychophysiology, 55,e13209. 

Models for skin conductance responses

Skin conductance response function

·      Anindirect test of the peripheral SCR model, and a development of the SCRF ispublished in: Bach DR, Flandin G, Friston KJ, Dolan RJ (2010). Modellingevent-related skin conductance responses. International Journal ofPsychophysiology, 75, 349-356.  [pdf]

·      Adirect test, using intraneural recording and stimulation, is provided in:Gerster S, Namer B, Elam M, Bach DR (2018). Testing a linear time invariantmodel for skin conductance responses by intraneural recording andstimulation. Psychophysiology, 55, e12986. 

GLM for evoked skin conductance responses (eSCR)

·      Theapproach was introduced in: Bach DR, Flandin G, Friston KJ, Dolan RJ (2009).Time-series analysis for rapid event-related skin conductance responses. Journalof Neuroscience Methods, 184, 224-234.  The latestrecommendations for an improved algorithm are published here: Bach DR, FristonKJ, Dolan RJ (2013).

·      Animproved algorithm for model-based analysis of evoked skin conductanceresponses. Biological Psychology, 94, 490-497.  A directcomparison with the software Ledalab shows significant superiority of PsPM torecover known states of sympathetic arousal: Bach DR (2014).

·      Ahead-to-head comparison of SCRalyze and Ledalab, two model-based methods forskin conductance analysis. Biological Psychology, 103, 63-88. 

Non-linear model for event-related SCR

·      Thiswas described in: Bach DR, Daunizeau J, Friston KJ, Dolan RJ (2010). Dynamiccausal modelling of anticipatory skin conductance responses. BiologicalPsychology, 85, 163-70. 

·      Thelatest recommendations for an improved algorithm are published here: Staib M,Castegnetti G, Bach DR (2015). Optimising a model-based approach to inferringfear learning from skin conductance responses. Journal of NeuroscienceMethods, 255, 131-138. 

Models for spontaneous skin conductance fluctuations (SF)

·      TheAUC model, a test for LTI assumptions, and a modified SCRF for SF wereintroduced in: Bach DR, Friston KJ, Dolan RJ (2010). Analytic measures forquantification of arousal from spontaneous skin conductance fluctuations. InternationalJournal of Psychophysiology, 76, 52-55. 

·      TheDCM for spontaneous fluctuations is described in: Bach DR, Daunizeau J, KuelzowN, Friston KJ, Dolan RJ (2011). Dynamic causal modelling of spontaneousfluctuations in skin conductance. Psychophysiology, 48,252-257. 

·      Afast inversion approximation building on Matching Pursuit is introduced in:Bach DR, Staib M (2015). A matching pursuit algorithm for inferring tonicsympathetic arousal from spontaneous skin conductance fluctuations. Psychophysiology,in press. 

Models for pupil data

·      Theilluminance model and its application to cognitive paradigms was introduced in:Korn CW & Bach DR (2016). A solid frame for the window on cognition:Modeling event-related pupil responses. Journal of Vision, 16,28. 

·      Themodel for fear-conditioned pupil size responses (fcPSR) was introduced in: KornCK, Staib M, Tzovara A, Castegnetti G, Bach DR (2017). A pupil size responsemodel to assess fear learning. Psychophysiology, 54,330-343. 

Models for heart data

·      Thepre-processing pipeline and the model for evoked heart period responses (eHPR)was introduced in: Paulus PC, Castegnetti G, & Bach DR (2016). Modelingevent-related heart period responses. Psychophysiology, 53, 837-846. 

·      Themodel for fear-conditioned heart period responses (fcHPR) was introduced in:Castegnetti G, Tzovara A, Staib M, Paulus PC, Hofer N, & Bach DR (2016).Modelling fear-conditioned bradycardia in humans. Psychophysiology, 53,930-939. 

Models for respiration data

·      Thepre-processing pipeline and the model for evoked respiratory responses wasintroduced in: Bach DR, Gerster S, Tzovara A, Castegnetti G (2016). A linearmodel for event-related respiration responses. Journal of NeuroscienceMethods, 270, 174-155. 

·      Themodel for fear-conditioned respiration amplitude responses (fcRAR) wasintroduced in: Castegnetti G, Tzovara A, Staib M, Gerster S, Bach DR (2017).Assessing fear learning via conditioned respiratory amplitude responses. Psychophysiology, 54,215-223.   

Model for startle-eye blink EMG

·      KhemkaS, Tzovara A, Gerster S, Quednow BB, Bach DR (2017). Modelling startle eyeblink electromyogram to assess fear learning. Psychophysiology, 54,202-214.