S. H. Fairclough, Fundamentals of physiological computing, Interact. Comput, vol.21, pp.133-145, 2008.

S. H. Fairclough, L. Venables, and A. Tattersall, The influence of task demand and learning on the psychophysiological response, Int. J. Psychophysiol, vol.56, pp.171-184, 2005.

P. Gerjets, C. Walter, W. Rosenstiel, M. Bogdan, and T. O. Zander, Cognitive state monitoring and the design of adaptive instruction in digital environments: lessons learned from cognitive workload assessment using a passive brain-computer interface approach, Front. Neurosci, vol.8, p.385, 2014.

D. Heger, C. Herff, F. Putze, R. Mutter, and T. Schultz, Continuous affective states recognition using functional near infrared spectroscopy, Brain Computer Interfaces, vol.1, pp.113-125, 2014.
DOI : 10.1080/2326263x.2014.912884

C. Herff, O. Fortmann, C. Tse, X. Cheng, F. Putze et al., Hybrid fNIRS-EEG based discrimination of 5 levels of memory load, 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER), pp.5-8, 2015.

C. Herff, D. Heger, O. Fortmann, J. Hennrich, F. Putze et al., Mental workload during n-back task quantified in the prefrontal cortex using fnirs, Front. Hum. Neurosci, vol.7, p.935, 2014.

S. Koelstra, C. Mühl, M. Soleymani, J. Lee, A. Yazdani et al., Deap: A database for emotion analysis; using physiological signals, IEEE Trans. Affect. Comput, vol.3, pp.18-31, 2012.
DOI : 10.1109/t-affc.2011.15
URL : https://infoscience.epfl.ch/record/165467/files/28_koelstra_ieeetac12.pdf

S. Koelstra, A. Yazdani, M. Soleymani, C. Mühl, J. Lee et al., Single trial classification of EEG and peripheral physiological signals for recognition of emotions induced by music videos, International Conference on Brain Informatics, pp.89-100, 2010.

C. A. Kothe and S. Makeig, Estimation of task workload from EEG data: new and current tools and perspectives, Annual International Conference of the IEEE, pp.6547-6551, 2011.

F. Lotte, L. Bougrain, A. Cichocki, M. Clerc, M. Congedo et al., A review of classification algorithms for EEG-based brain-computer interfaces: a 10 year update, J. Neural Eng, vol.15, p.31005, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01846433

C. Mühl, C. Jeunet, L. , and F. , EEG-based workload estimation across affective contexts, Front. Neurosci, vol.8, p.114, 2014.

F. Putze, S. Hesslinger, C. Tse, Y. Huang, C. Herff et al., Hybrid fNIRS-EEG based classification of auditory and visual perception processes, Front. Neurosci, vol.8, p.373, 2014.

F. Putze, J. Hild, R. Kärgel, C. Herff, A. Redmann et al., Locating user attention using eye tracking and EEG for spatio-temporal event selection, Proceedings of the 2013 International Conference on Intelligent User Interfaces, pp.129-136, 2013.

J. Shin, A. Von-lühmann, B. Blankertz, D. Kim, J. Jeong et al., Open access dataset for EEG+NIRS single-trial classification, IEEE Trans. Neural Syst. Rehabil. Eng, vol.25, pp.1735-1745, 2017.
DOI : 10.1109/tnsre.2016.2628057

W. K. So, S. W. Wong, J. N. Mak, C. , and R. H. , An evaluation of mental workload with frontal EEG, PLoS ONE, vol.12, p.174949, 2017.

, Conflict of Interest Statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest

. Copyright-©-;-putze, L. Mühl, and H. Fairclough, This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, 2018.