Establishing Ground Truth on Pyschophysiological Models for Training Machine Learning Algorithms: Options for Ground Truth Proxies

One of the core aspects of human-human interaction is the ability to recognize and respond to the emotional and cognitive states of the other person, leaving human-computer interaction systems, at their core, to perform many of the same tasks.

[1]  Mark R. Costa,et al.  Truthiness: Challenges Associated with Employing Machine Learning on Neurophysiological Sensor Data , 2016, HCI.

[2]  Thierry Pun,et al.  DEAP: A Database for Emotion Analysis ;Using Physiological Signals , 2012, IEEE Transactions on Affective Computing.

[3]  Cristina Conati,et al.  Probabilistic assessment of user's emotions in educational games , 2002, Appl. Artif. Intell..

[4]  Shion Guha,et al.  Machine Learning and Grounded Theory Method: Convergence, Divergence, and Combination , 2016, GROUP.

[5]  José Carlos Núñez,et al.  Self-regulated profiles and academic achievement. , 2008, Psicothema.

[6]  Keith W. Brawner Data Sharing: Low-Cost Sensors for Affect and Cognition , 2014, EDM.

[7]  Jonathan A. Smith Reflecting on the development of interpretative phenomenological analysis and its contribution to qualitative research in psychology , 2004 .

[8]  Brian Knutson,et al.  Inferring affect from fMRI data , 2014, Trends in Cognitive Sciences.

[9]  Stephen H. Fairclough,et al.  Classification Accuracy from the Perspective of the User: Real-Time Interaction with Physiological Computing , 2015, CHI.

[10]  G. Kennedy,et al.  All roads lead to Rome: Tracking students’ affect as they overcome misconceptions , 2016 .

[11]  Ashish Kapoor,et al.  Multimodal affect recognition in learning environments , 2005, ACM Multimedia.

[12]  Neville A. Stanton,et al.  Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence , 2014 .

[13]  Li Zhang,et al.  Intelligent facial emotion recognition and semantic-based topic detection for a humanoid robot , 2013, Expert Syst. Appl..

[14]  Michael W. Boyce,et al.  Interpretative Phenomenological Analysis for Military Tactics Instruction , 2017 .

[15]  Neil T. Heffernan,et al.  Predicting College Enrollment from Student Interaction with an Intelligent Tutoring System in Middle School , 2013, EDM.

[16]  L. Rothkrantz,et al.  Toward an affect-sensitive multimodal human-computer interaction , 2003, Proc. IEEE.

[17]  Cristina Conati,et al.  Predicting Affect from Gaze Data during Interaction with an Intelligent Tutoring System , 2014, Intelligent Tutoring Systems.

[18]  V. Braun,et al.  Using thematic analysis in psychology , 2006 .

[19]  S. Hart,et al.  Development of NASA-TLX (Task Load Index): Results of Empirical and Theoretical Research , 1988 .

[20]  Keith Brawner,et al.  Modeling Learner Mood In Realtime Through Biosensors For Intelligent Tutoring Improvements , 2013 .

[21]  Judith Redi,et al.  Crowdsourcing Empathetic Intelligence , 2016, ACM Trans. Intell. Syst. Technol..

[22]  Mark H. Chignell,et al.  Mental workload dynamics in adaptive interface design , 1988, IEEE Trans. Syst. Man Cybern..

[23]  Nicu Sebe,et al.  A Quality Adaptive Multimodal Affect Recognition System for User-Centric Multimedia Indexing , 2016, ICMR.

[24]  Jennifer Healey,et al.  Toward Machine Emotional Intelligence: Analysis of Affective Physiological State , 2001, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Martin Porcheron,et al.  Measuring the effect of think aloud protocols on workload using fNIRS , 2014, CHI.

[26]  Bert Arnrich,et al.  Towards an Emotional Engagement Model: Can Affective States of a Learner be Automatically Detected in a 1: 1 Learning Scenario? , 2016, UMAP.

[27]  Terence Sim,et al.  The CMU Pose, Illumination, and Expression (PIE) database , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[28]  Eugénio C. Oliveira,et al.  A Hybrid Approach at Emotional State Detection: Merging Theoretical Models of Emotion with Data-Driven Statistical Classifiers , 2013, 2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT).

[29]  Chris Berka,et al.  Drowsiness/alertness algorithm development and validation using synchronized EEG and cognitive performance to individualize a generalized model , 2011, Biological Psychology.

[30]  Maja Pantic,et al.  This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. IEEE TRANSACTIONS ON AFFECTIVE COMPUTING , 2022 .

[31]  Chris Berka,et al.  Real-Time Analysis of EEG Indexes of Alertness, Cognition, and Memory Acquired With a Wireless EEG Headset , 2004, Int. J. Hum. Comput. Interact..