DOCSLIB.ORG

Predictive Decoding of Neural Data Yaroslav O

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Predictive Decoding of Neural Data Yaroslav O New Jersey Institute of Technology Digital Commons @ NJIT Dissertations Theses and Dissertations Spring 2009 Predictive decoding of neural data Yaroslav O. Halchenko New Jersey Institute of Technology Follow this and additional works at: https://digitalcommons.njit.edu/dissertations Part of the Computer Sciences Commons Recommended Citation Halchenko, Yaroslav O., "Predictive decoding of neural data" (2009). Dissertations. 901. https://digitalcommons.njit.edu/dissertations/901 This Dissertation is brought to you for free and open access by the Theses and Dissertations at Digital Commons @ NJIT. It has been accepted for inclusion in Dissertations by an authorized administrator of Digital Commons @ NJIT. For more information, please contact [email protected]. Cprht Wrnn & trtn h prht l f th Untd Stt (tl , Untd Stt Cd vrn th n f phtp r thr rprdtn f prhtd trl. Undr rtn ndtn pfd n th l, lbrr nd rhv r thrzd t frnh phtp r thr rprdtn. On f th pfd ndtn tht th phtp r rprdtn nt t b “d fr n prp thr thn prvt td, hlrhp, r rrh. If , r rt fr, r ltr , phtp r rprdtn fr prp n x f “fr tht r b lbl fr prht nfrnnt, h ntttn rrv th rht t rf t pt pn rdr f, n t jdnt, flfllnt f th rdr ld nvlv vltn f prht l. l t: h thr rtn th prht hl th r Inttt f hnl rrv th rht t dtrbt th th r drttn rntn nt: If d nt h t prnt th p, thn lt “ fr: frt p t: lt p n th prnt dl rn h n tn lbrr h rvd f th prnl nfrtn nd ll ntr fr th pprvl p nd brphl th f th nd drttn n rdr t prtt th dntt f I rdt nd flt. ABSTRACT PREDICTIVE DECODING OF NEURAL DATA by Yaroslav 0. Halchenko In the last five decades the number of techniques available for non-invasive functional imaging has increased dramatically. Researchers today can choose from a variety of imaging modalities that include EEG, MEG, PET, SPECT, MRI, and fMRI. This doctoral dissertation offers a methodology for the reliable analysis of neural data at different levels of investigation. By using statistical learning algorithms the proposed approach allows single-trial analysis of various neural data by decoding them into variables of interest. Unbiased testing of the decoder on new samples of the data provides a generalization assessment of decoding performance reliability. Through consecutive analysis of the constructed decoder's sensitivity it is possible to identify neural signal components relevant to the task of interest. The proposed methodology accounts for covariance and causality structures present in the signal. This feature makes it more powerful than conventional univariate methods which currently dominate the neuroscience field. Chapter 2 describes the generic approach toward the analysis of neural data using statistical learning algorithms. Chapter 3 presents an analysis of results from four neural data modalities: extracellular recordings, EEG, MEG, and fMRI. These examples demonstrate the ability of the approach to reveal neural data components which cannot be uncovered with conventional methods. A further extension of the methodology, Chapter 4 is used to analyze data from multiple neural data modalities: EEG and fMRI. The reliable mapping of data from one modality into the other provides a better understanding of the underlying neural processes. By allowing the spatial-temporal exploration of neural signals under loose moeig assumios i emoes oeia ias i e aaysis o eua aa ue o oewise ossie owa moe misseciicaio e oose meooogy as ee omaie io a ee a oe souce yo amewok o saisica eaig ase aa aaysis is amewok yMA is escie i Cae 5 PREDICTIVE DECODING OF NEURAL DATA by Yaroslav 0. Halchenko A Dissertation Submitted to the Faculty of New Jersey Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Computer Science Department of Computer Science May 2009 Coyig © 9 y Yaosa aceko A IGS ESEE APPROVAL PAGE PREDICTIVE DECODING OF NEURAL DATA Yaroslav 0. Halchenko Dr. Jason RI,. Wang, Dissertation Co-Advisor Date Professor of Computer Science, NJIT Dr. Stephen J. Hanson, Dissertation Co-Advisor Date Professor, 'Psychology, Rutgers-Newark Dr. Zoi-Heleni Michalopoulou, Committee Member Date Professor of Mathematical Sciences & Electrical and Computer Engineering, NJIT Dr. David Nassimi, Committee Member Date Associate Professor of Computer Science, NJIT / Dr. Chengjun Liu, Committee Member Date Associate Professor of Computer Science, NJIT Dr. Barak A. Pearlmutter, Committee Member Date Professor of Computer Science, Hamilton Institute, National University of Ireland, Maynooth BIOGRAPHICAL SKETCH Author: Yaosa aceko Degree: oco o iosoy Date: May 9 Undergraduate and Graduate Education: • oco o iosoy i Comue Sciece ew esey Isiue o ecoogy ewak USA 9 • Mase o Sciece i Comue Sciece Uiesiy o ew Meico Auqueque M USA • Mase o Sciece i ase a Ooeecoic Egieeig iysya Sae Uiesiy iysya Ukaie 1999 • aceos egee i ase a Ooeecoic Egieeig iysya Sae Uiesiy iysya Ukaie 199 Major: Comue Sciece Presentations and Publications: J. amsey S aso C aso Y aceko A oack a C Gymou Si oems o causa ieece om MI I sumissio A oack Y aceko a S aso ecoig e age-scae sucue o ai ucio y cassiyig mea saes acoss iiiuas sycoogica Sciece I ess M ake Y aceko Seeeg a M uges e yMA Maua Aaiae oie a //wwwymaog/yMA-Maua M ake Y aceko Seeeg S aso ay a S oma yMA A yo ooo o muiaiae ae aaysis o MI aa euoiomaics 7(137-53 Ma 9 M ake Y aceko Seeeg E Oiei I ü J. W iege C S ema ay S aso a S oma yMA A uiyig aoac o e aaysis o euoscieiic aa oies i euoiomaics 3 (3 9 iv S. J. Hanson and Y. O. Halchenko. Brain reading using full brain support vector machines for object recognition: There is no face identification area. Neural Computation, 20 (2):486-503, February 2008. S. J. Hanson, C. Hanson, Y. O. Halchenko, T. Matsuka, and A. Zaimi. Bottom-up and top- down brain functional connectivity underlying comprehension of everyday visual action. Brain Struct Funct, 212(3-4):231-44, 2007. S. J. Hanson, D. Rebbechi, C. Hanson, and Y. O. Halchenko. Dense mode clustering in brain maps. Magn Reson Imaging, 25(9):1249-62, 2007. Y. O. Halchenko, S. J. Hanson, and B. A. Pearlmutter. Multimodal integration: fMRI, MRI, EEG, and MEG. In L. Landini, V. Positano, and M. F. Santarelli, editors, Advanced Image Processing in Magnetic Resonance Imaging, Signal Processing and Communications, chapter 8, pages 223-265. Dekker, 2005. ISBN 0824725425. S. J. Hanson, T. Matsuka, C. Hanson, D. Rebbechi, Y. O. Halchenko, A. Zaimi, and B. A. Pearlmutter. Structural equation modeling of neuroimaging data: Exhaustive search and Markov chain Monte Carlo. In Human Brain Mapping, 2004. Y. O. Halchenko, S. J. Hanson, and B. A. Pearlmutter. Fusion of functional brain imaging modalities using 1-norms signal reconstruction. In Proceedings of the Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA, 2004. Y. O. Halchenko, B. A. Pearlmutter, S. J. Hanson, and A. Zaimi. Fusion of functional brain imaging modalities via linear programming. Biomedizinische Technik (Biomedical Engineering), 48(2):102-104, 2004. L. I. Timchenko, Y. F. Kutaev, A. A. Gertsiy, Y. O. Halchenko, L. V. Zahoruiko, and T. Mansur. Method for image coordinate definition on extended laser paths. In S. B. Gurevich, Z. T. Nazarchuk, and L. I. Muraysky, editors, Optoelectronic and Hybrid Optical/Digital Systems for Image and Signal Processing, volume 4148:1, pages 19-26. SPIE, 2000. L. I. Timchenko, Y. F. Kutaev, A. A. Gertsiy, L. V. Zahoruiko, Y. O. Halchenko, and T. Mansur. Approach to parallel-hierarchical network learning for real-time image sequence recognition. In J. W. V. Miller, S. S. Solomon, and B. G. Batchelor, editors, Machine Vision Systems for Inspection and Metrology VIII, volume 3836:1, pages 71-81. SPIE, 1999. L. I. Timchenko, A. A. Gertsiy, L. V. Zahoruiko, Y. F. Kutaev, and Y. O. Halchenko. Pre-processing of extended laser path images. In Industrial Lasers and Inspection: Diagnostic Imaging Technologies and Industrial Applications, Munich, Germany, June 1999. EOS/SPIE International Symposium. [3827-26]. L. I. Tymchenko, J. Scorukova, S. Markov, and Y. O. Halchenko. Image segmentation on the basis of spatial connected features. In Visnyk VSTU, volume 4, pages 39-43. VSTU University Press, Vinnytsya, Ukraine, 1998. In Ukrainian. T. B. Martinyuk, A. V. Kogemiako, and Y. 0. Halchenko. The model of associative processor for numerical data sorting. In Visnyk VSTU, volume 2, pages 19-23. VSTU University Press, Vinnytsya, Ukraine, 1997. In Ukrainian. L. I. Tymchenko, J. Scorukova, J. Kutaev, S. Markov, T. Martynuk, and Y. 0. Halchenko. Method spatial connected segmentation of images. In The Third All-Ukrainian International Conference Ukrobraz, Kijiv, Ukraine, November 1996. In Ukrainian. vi ii LIST OF TERMS Abbreviation ANOVA Analysis of Variance BCI Brain Computer Interface BEM Boundary Elements Method BMA Bayesian Model Averaging BOLD Blood Oxygenation Level Dependent DC Direct Current DECD Distributed ECD ECD Equivalent Current Dipole EEG Electroencephalography EIT Electrical Impedance Tomography EMF Electromotive Force F/MEG EEG and/or MEG EM Expectation Maximization EMSI Electromagnetic Source Imaging EPI Echo Planar Imaging ER Event-related ERF Event-related Magnetic Field (in MEG) ERP Event-related Potential (in EEG) FDA Food and Drug Administration FDM Finite Difference Method FEM Finite Elements Method FFA Fusiform Face Area FG Fusiform Gyrus FLIRT FMRIB's Linear Image Registration Tool FMRI Functional MRI FMRIB Oxford Centre for Functional MRI of the Brain FOSS Free and Open Source Software FSL FMRIB Software Library GLM General Linear Model
Load more

Recommended publications
  • Exploring the Role of Language in Two Systems for Categorization Kayleigh Ryherd University of Connecticut - Storrs, [email protected]
    University of Connecticut OpenCommons@UConn Doctoral Dissertations University of Connecticut Graduate School 4-26-2019 Exploring the Role of Language in Two Systems for Categorization Kayleigh Ryherd University of Connecticut - Storrs, [email protected] Follow this and additional works at: https://opencommons.uconn.edu/dissertations Recommended Citation Ryherd, Kayleigh, "Exploring the Role of Language in Two Systems for Categorization" (2019). Doctoral Dissertations. 2163. https://opencommons.uconn.edu/dissertations/2163 Exploring the Role of Language in Two Systems for Categorization Kayleigh Ryherd, PhD University of Connecticut, 2019 Multiple theories of category learning converge on the idea that there are two systems for categorization, each designed to process different types of category structures. The associative system learns categories that have probabilistic boundaries and multiple overlapping features through iterative association of features and feedback. The hypothesis-testing system learns rule-based categories through explicit testing of hy- potheses about category boundaries. Prior research suggests that language resources are necessary for the hypothesis-testing system but not for the associative system. However, other research emphasizes the role of verbal labels in learning the probabilistic similarity-based categories best learned by the associative system. This suggests that language may be relevant for the associative system in a different way than it is relevant for the hypothesis-testing system. Thus, this study investigated the ways in which language plays a role in the two systems for category learning. In the first experiment, I tested whether language is related to an individual’s ability to switch between the associative and hypothesis-testing systems. I found that participants showed remarkable ability to switch between systems regardless of their language ability.
    [Show full text]
  • Psychology Software for Python Release 3.0.2
    PsychoPy - Psychology software for Python Release 3.0.2 Jonathan Peirce Jan 29, 2019 Contents 1 About PsychoPy 1 2 General issues 3 3 Installation 29 4 Manual install 31 5 Getting Started 35 6 Builder 43 7 Coder 75 8 Running studies online 95 9 Reference Manual (API) 103 10 Troubleshooting 325 11 Recipes (“How-to”s) 329 12 Frequently Asked Questions (FAQs) 341 13 Resources (e.g. for teaching) 343 14 For Developers 345 15 PsychoPy Experiment file format (.psyexp) 359 Python Module Index 363 i ii CHAPTER 1 About PsychoPy 1.1 Citing PsychoPy If you use this software, please cite one of the publications that describe it. • Peirce, J. W., & MacAskill, M. R. (2018). Building Experiments in PsychoPy. London: Sage. • Peirce J. W. (2009). Generating stimuli for neuroscience using PsychoPy. Frontiers in Neuroinformatics, 2 (10), 1-8. doi:10.3389/neuro.11.010.2008 • Peirce, J. W. (2007). PsychoPy - Psychophysics software in Python. Journal of Neuroscience Methods, 162 (1-2):8-13 doi:10.1016/j.jneumeth.2006.11.017 Citing these papers gives the reviewer/reader of your study information about how the system works and it attributes some credit for its original creation. Academic assessment (whether for promotion or even getting appointed to a job in the first place) prioritises publications over making useful tools for others. Citations provide a way for the developers to justify their continued involvement in the development of the package. 1 PsychoPy - Psychology software for Python, Release 3.0.2 2 Chapter 1. About PsychoPy CHAPTER 2 General issues These are issues that users should be aware of, whether they are using Builder or Coder views.
    [Show full text]
  • Travma Sonrası Stres Bozukluğu Tedavisinde Bilişsel Ve Davranışçı Yaklaşımlar 28
    Hasan Kalyoncu Üniversitesi Psikoloji Bölümü www.hku.edu.tr Havalimanı Yolu Üzeri 8.Km 27410 Şahinbey/GAZİANTEP +90 (342) 211 80 80 - [email protected] EDİTÖR Doç. Dr. Şaziye Senem BAŞGÜL YARDIMCI EDİTÖR Arş. Gör. Saadet YAPAN YAYIN KURULU Prof. Dr. Mehmet Hakan TÜRKÇAPAR Prof. Dr. Mücahit ÖZTÜRK Prof. Dr. Bengi SEMERCİ Prof. Dr. Osman Tolga ARICAK Doç. Dr. Şaziye Senem BAŞGÜL Doç. Dr. Hanna Nita SCHERLER Öğr. Gör. Mehmet DİNÇ Öğr. Gör. Ferhat Jak İÇÖZ Öğr. Gör. Mediha ÖMÜR Arş. Gör. Saadet YAPAN Arş. Gör. Mahmut YAY Arş. Gör. Feyza TOPÇU DANIŞMA KURULU Prof. Dr. Can TUNCER Doç. Dr. Zümra ÖZYEŞİL Yrd. Doç. Dr. Itır TARI CÖMERT Dr. Özge MERGEN Dr. Akif AVCU KAPAK TASARIM Uğur Servet KARALAR GRAFİK UYGULAMA Yakup BAYRAM 0342 211 80 80 psikoloji.hku.edu.tr [email protected] Havalimanı Yolu Üzeri 8. Km 27410 Şahinbey/GAZİANTEP Sonbahar ve ilkbahar sayıları olarak yılda iki kere çıkar. İÇİNDEKİLER Önsöz 4 Editörden 5 Bilimsel Bir Araştırmanın Yol Haritası 6 Psikoterapilere Varoluşçu Bir Bakış 10 Anne, Baba ve Çocuk Tarafından Algılanan Ebeveyn Kabul-Ret ve Kontrolünün Çocuğun Duygu Düzenleme Becerisi İle İlişkisi 13 Fabrika İşçilerinde Stres ve Depresyon Arasındaki İlişki 20 Travma Sonrası Stres Bozukluğu Tedavisinde Bilişsel ve Davranışçı Yaklaşımlar 28 Araştırmalar ve Olgu Değerlendirmelerinde Nöro-Psikolojik Testler ve Bilgisayar Programlarının Bütünleştirilmesi: Bir Gözden Geçirme 32 Tez Özetleri 35 ÖNSÖZ Değerli okurlarımız, Hasan Kalyoncu Üniversitesi Psikoloji Bölümü öğretim üyeleri ve öğ- rencilerinin girişimiyle yayınlanan Psikoloji Araştırmaları dergisinin yeni bir sayısıyla yeniden karşınızdayız. Yolculuğumuz dördüncü sayı- sına ulaştı. Psikoloji Araştırmaları dergisinin bu sayısında, klinik çalışmalar ve göz- den geçirme derleme yazıları yer alıyor.
    [Show full text]
  • A Glance at Psychophysics Software Programs
    Basic and Clinical Spring 2011, Volume 2, Number 3 A Glance at Psychophysics Software Programs Ali Yoonessi 1,2, Ahmad Yoonessi 3 1. School of Advanced Medical Technologies, Tehran University of Medical Sciences, Tehran, Iran. 2. Iranian National Center for Addiction Studies, Tehran University of Medical Sciences, Tehran, Iran. 2. McGill Vision Research, McGill University, Canada Article info: A B S T R A C T Received: 23 January 2011 First Revision: 14 February 2011 Accepted: 2 March 2011 Visual stimulation with precise control of stimulus has transformed the field of psychophysics since the introduction of personal computers. Luminance and chromatic features of stimulus, timing, and position of the stimulus are the main features that could be defined using programs written specifically for psychophysical experiments. In this manuscript, software used for the psychophysical experiments have been reviewed and evaluated for ease of use, license, popularity, and expandability. 1. Introduction and Psychopy4. Psychtoolbox is a toolbox written for Matlab, a commercially available software for signal ince the foundation of psychophysics by processing. Psychopy is an open-source platform-inde- Ibn-Haytham in the 4th century (Solar Hi- pendent program written under Python, a very popular jri, S.H.), scientists have been puzzled by programming tool. Visionegg is another open-source the complexity of visual experience1. His software for generating stimuli. Other programs include S method of controlled experiment made him Presentation, e-Prime, and Psykinematix. These pro- the pioneer of scientific methods 2. With development grams are commercial products and the prices vary up of personal computers in 1970s, vision scientists real- to more than 1000$.
    [Show full text]
  • Pyflies: a Domain-Specific Language for Designing Experiments In
    applied sciences Article PyFlies: A Domain-Specific Language for Designing Experiments in Psychology Igor Dejanovi´c 1,* , Mirjana Dejanovi´c 2,*, Jovana Vidakovi´c 3 and Siniša Nikoli´c 1 1 Faculty of Technical Sciences, University of Novi Sad, 21000 Novi Sad, Serbia; [email protected] 2 Faculty of Medicine, University of Priština-Kosovska Mitrovica, 38220 Kosovska Mitrovica, Serbia 3 Faculty of Sciences, University of Novi Sad, 21000 Novi Sad, Serbia; [email protected] * Correspondence: [email protected] (I.D.); [email protected] (M.D.); Tel.: +381-21-485-4562 (I.D.) Abstract: The majority of studies in psychology are nowadays performed using computers. In the past, access to good quality software was limited, but in the last two decades things have changed and today we have an array of good and easily accessible open-source software to choose from. However, experiment builders are either GUI-centric or based on general-purpose programming languages which require programming skills. In this paper, we investigate an approach based on domain-specific languages which enables a text-based experiment development using domain- specific concepts, enabling practitioners with limited or no programming skills to develop psychology tests. To investigate our approach, we created PyFlies, a domain-specific language for designing experiments in psychology, which we present in this paper. The language is tailored for the domain of psychological studies. The aim is to capture the essence of the experiment design in a concise and highly readable textual form. The editor for the language is built as an extension for Visual Studio Code, one of the most popular programming editors today.
    [Show full text]
  • A Proposal for a Data-Driven Approach to the Influence of Music
    METHODS published: 20 August 2021 doi: 10.3389/fcvm.2021.699145 A Proposal for a Data-Driven Approach to the Influence of Music on Heart Dynamics Ennio Idrobo-Ávila 1*, Humberto Loaiza-Correa 1, Flavio Muñoz-Bolaños 2, Leon van Noorden 3 and Rubiel Vargas-Cañas 4 1 Escuela de Ingeniería Eléctrica y Electrónica, PSI - Percepción y Sistemas Inteligentes, Universidad del Valle, Cali, Colombia, 2 Departamento de Ciencias Fisiológicas, CIFIEX - Ciencias Fisiológicas Experimentales, Universidad del Cauca, Popayán, Colombia, 3 Department of Art, Music, and Theatre Sciences, IPEM—Institute for Systematic Musicology, Ghent University, Ghent, Belgium, 4 Departamento de Física, SIDICO - Sistemas Dinámicos, Instrumentación y Control, Universidad del Cauca, Popayán, Colombia Electrocardiographic signals (ECG) and heart rate viability measurements (HRV) provide information in a range of specialist fields, extending to musical perception. The ECG signal records heart electrical activity, while HRV reflects the state or condition of the autonomic nervous system. HRV has been studied as a marker of diverse psychological and physical diseases including coronary heart disease, myocardial infarction, and stroke. HRV has also been used to observe the effects of medicines, the impact of exercise and the analysis of emotional responses and evaluation of effects of various quantifiable Edited by: elements of sound and music on the human body. Variations in blood pressure, levels of Xiang Xie, stress or anxiety, subjective sensations and even changes in emotions constitute multiple First Affiliated Hospital of Xinjiang aspects that may well-react or respond to musical stimuli. Although both ECG and Medical University, China HRV continue to feature extensively in research in health and perception, methodologies Reviewed by: Ming-Jie Wang, vary substantially.
    [Show full text]
  • Psychopy 1. Kafli „Building Experiments in Psychopy“ Einu Sinni Var
    7/4/2018 PsychoPy 1. kafli Kynning „Building Experiments in PsychoPy“ • Tökum fyrir kafla 1-9: 1. Introduction 2. Building your first experiment 3. Using images – a study into face perception 4. Timing and brief stimuli: Posner cueing 5. Creating dynamic stimuli (revealing text and moving stimuli) 6. Providing feedback: simple Code Components 7. Ratings: Measure the Big 5 personality constructs 8. Randomization, blocks and counterbalancing: a bilingual Stroop task 9. Using the mouse for input: creating a visual search task Einu sinni var... • Fyrst: Sálfræðitilraunir keyrðar á sérhæfðum vélbúnaði – Dæmi: Hraðsjá (tachistoscope) • Síðar: Forritarar útbúa tilraunir • Enn síðar: Forritunarmál notendavænni • Núna: Hægt að útbúa flóknar tilraunir án forritunarkunnáttu með sérhæfðum forritum – Dæmi: PsychoPy 1 7/4/2018 Önnur tilraunaforrit • Krefjast forritunarkunnáttu, fólk skrifar kóða (texta) sem segir tölvunni hvað á að gera: – Psychophysics Toolbox – Presentation • Krefjast ekki forritunarkunnáttu, nota myndrænt notendaviðmót, bjóða ekki auðveldlega upp á flóknar tilraunir – Eprime – PsyScope – OpenSesame PsychoPy • Ókeypis og opið (open source) • Keyrir á margs konar stýrikerfum • Öflugt og sveigjanlegt • Hefur tvenns konar notendaviðmót – Coder • Skrifa skipanir á textaformi (Python forritunarmál) – Builder • Grafískt notendaviðmót (graphical user interface, GUI) – Ræður einnig við litla kóðabúta (code components, textaskipanir) • Við fjöllum aðallega um Builder Stýrikerfi • PsychoPy keyrir á Windows, Mac og Linux • Hér kennt á Windows-útgáfuna
    [Show full text]
  • An Introduction to the Objective Psychophysics Toolbox (O Ptb)
    An introduction to the Objective Psychophysics Toolbox (o_ptb) 1* 1 1 Thomas Hartmann , Nathan Weisz 2 1Centre for Cognitive Neuroscience and Department of Psychology, Paris-Lodron Universität 3 Salzburg, Hellbrunnerstraße 34/II, 5020 Salzburg, Austria 4 Correspondence 5 Thomas Hartmann 6 [email protected] 7 Keywords: Open Source; Matlab; Psychophysics; EEG; MEG; fMRI; Reaction Time; Stimulus 8 Presentation 9 Abstract 10 The Psychophysics Toolbox (PTB) is one of the most popular toolboxes for the development of 11 experimental paradigms. It is a very powerful library, providing low-level, platform independent 12 access to the devices used in an experiment such as the graphics and the sound card. While this low- 13 level design results in a high degree of flexibility and power, writing paradigms that interface the 14 PTB directly might lead to code that is hard to read, maintain, reuse and debug. Running an 15 experiment in different facilities or organizations further requires it to work with various setups that 16 differ in the availability of specialized hardware for response collection, triggering and presentation 17 of auditory stimuli. The Objective Psychophysics Toolbox (o_ptb) provides an intuitive, unified and 18 clear interface, built on top of the PTB that enables researchers to write readable, clean and concise 19 code. In addition to presenting the architecture of the o_ptb, the results of a timing accuracy test are 20 presented. Exactly the same Matlab code was run on two different systems, one of those using the 21 VPixx system. Both systems showed sub-millisecond accuracy. 22 1 Introduction 23 Writing the source code for an experimental paradigm is one of the most crucial and critical steps of 24 a study.
    [Show full text]
  • Psychopy - Psychology Software for Python Release 1.90.2
    PsychoPy - Psychology software for Python Release 1.90.2 Jonathan Peirce Jun 01, 2018 CONTENTS 1 About PsychoPy 1 2 General issues 3 3 Installation 31 4 Manual install 33 5 Getting Started 37 6 Builder 43 7 Coder 73 8 Running studies online 93 9 Reference Manual (API) 101 10 Troubleshooting 267 11 Recipes (“How-to”s) 271 12 Frequently Asked Questions (FAQs) 283 13 Resources (e.g. for teaching) 285 14 For Developers 289 15 PsychoPy Experiment file format (.psyexp) 303 Python Module Index 307 Index 309 i ii CHAPTER ONE ABOUT PSYCHOPY 1.1 Citing PsychoPy If you use this software, please cite one of the papers that describe it. 1. Peirce, JW (2007) PsychoPy - Psychophysics software in Python. J Neurosci Methods, 162(1-2):8-13 2. Peirce JW (2009) Generating stimuli for neuroscience using PsychoPy. Front. Neuroinform. 2:10. doi:10.3389/neuro.11.010.2008 Citing these papers gives the reviewer/reader of your study information about how the system works, it also attributes some credit for its original creation, and it means provides a way to justify the continued development of the package. 1 PsychoPy - Psychology software for Python, Release 1.90.2 2 Chapter 1. About PsychoPy CHAPTER TWO GENERAL ISSUES These are issues that users should be aware of, whether they are using Builder or Coder views. 2.1 Monitor Center PsychoPy provides a simple and intuitive way for you to calibrate your monitor and provide other information about it and then import that information into your experiment. Information is inserted in the Monitor Center (Tools menu), which allows you to store information about multiple monitors and keep track of multiple calibrations for the same monitor.
    [Show full text]
  • Designing Eeg Experiments for Studying the Brain
    DESIGNING EEG EXPERIMENTS FOR STUDYING THE BRAIN https://www.elsevier.com/books-and-journals/book-companion/9780128111406 Designing EEG Experiments for Studying the Brain: Design Code and Example Datasets Aamir Saeed Malik and Hafeez Ullah Amin Resources available: Table of Contents: - Chapter Abstracts - Chapter Data - Glossary DESIGNING EEG EXPERIMENTS FOR STUDYING THE BRAIN Design Code and Example Datasets AAMIR SAEED MALIK HAFEEZ ULLAH AMIN Universiti Teknologi PETRONAS, Perak, Malaysia Academic Press is an imprint of Elsevier 125 London Wall, London EC2Y 5AS, United Kingdom 525 B Street, Suite 1800, San Diego, CA 92101-4495, United States 50 Hampshire Street, 5th Floor, Cambridge, MA 02139, United States The Boulevard, Langford Lane, Kidlington, Oxford OX5 1GB, United Kingdom Copyright © 2017 Elsevier Inc. All rights reserved. No part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. Details on how to seek permission, further information about the Publisher’s permissions policies and our arrangements with organizations such as the Copyright Clearance Center and the Copyright Licensing Agency, can be found at our website: www.elsevier.com/permissions. This book and the individual contributions contained in it are protected under copyright by the Publisher (other than as may be noted herein). Notices Knowledge and best practice in this field are constantly changing. As new research and experience broaden our understanding, changes in research methods, professional practices, or medical treatment may become necessary. Practitioners and researchers must always rely on their own experience and knowledge in evaluating and using any information, methods, compounds, or experiments described herein.
    [Show full text]
  • An Open-Source Cross-Platform Graphical Experiment Builder for Psychtoolbox with Built-In Performance Optimization
    Version: September 25, 2021 PsyBuilder: an open-source cross-platform graphical experiment builder for Psychtoolbox with built-in performance optimization Zhicheng Lin1,3†, Zhe Yang1,2†, Chengzhi Feng1, Yang Zhang1* 1 Department of Psychology, Soochow University, Suzhou, Jiangsu, China 215000 2 School of Computer Science & Technology, Soochow University, Suzhou, Jiangsu, China 215000 3 Applied Psychology Program, The Chinese University of Hong Kong, Shenzhen, China 518172 † Joint first authors with equal contributions *Correspondence should be addressed to Dr. Yang Zhang at Department of Psychology, Soochow University, Suzhou, Jiangsu, 215123, China. E-mail: [email protected] Acknowledgments The study was supported by the National Natural Science Foundation of China (32171049, 32071045), the Natural Science Foundation of Jiangsu Province (BK20201411), Guangdong Basic and Applied Basic Research Foundation (2019A1515110574), and the City & University Strategy Soochow University Leading Research Team in the Humanities and Social Sciences. 1 Abstract Psychtoolbox is among the most popular open-source software packages for stimulus presentation and response collection. It provides flexibility and power in the choice of stimuli and responses, combined with precision in control and timing. However, Psychtoolbox requires coding in MATLAB (or its equivalent, such as Octave). Scripting is challenging to learn and can lead to timing inaccuracies unwittingly. It can also be time-consuming and error-prone even for experienced users. We have developed the first general-purpose graphical experiment builder for Psychtoolbox, called PsyBuilder, for both new and experienced users. The builder allows users to graphically implement sophisticated experimental tasks through intuitive drag-and-drop without the need to script. The output codes have built-in optimized timing precision and come with detailed comments to facilitate customization.
    [Show full text]
  • University of Groningen Opensesame Mathot, Sebastiaan; Schreij, Daniel
    University of Groningen OpenSesame Mathot, Sebastiaan; Schreij, Daniel; Theeuwes, Jan Published in: Behavior Research Methods DOI: 10.3758/s13428-011-0168-7 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2012 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Mathot, S., Schreij, D., & Theeuwes, J. (2012). OpenSesame: An open-source, graphical experiment builder for the social sciences. Behavior Research Methods, 44(2), 314-324. https://doi.org/10.3758/s13428-011-0168-7 Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons). The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: https://www.rug.nl/library/open-access/self-archiving-pure/taverne- amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.
    [Show full text]