Abstract
Automatic detection of an individual’s mind-wandering state has implications for designing and evaluating engaging and effective learning interfaces. While it is difficult to differentiate whether an individual is mind-wandering or focusing on the task only based on externally observable behavior, brain-based sensing offers unique insights to internal states. To explore the feasibility, we conducted a study using functional near-infrared spectroscopy (fNIRS) and investigated machine learning classifiers to detect mind-wandering episodes based on fNIRS data, both on an individual level and a group level, specifically focusing on automated window selection to improve classification results. For individual-level classification, by using a moving window method combined with a linear discriminant classifier, we found the best windows for classification and achieved a mean F1-score of 74.8%. For group-level classification, we proposed an individual-based time window selection (ITWS) algorithm to incorporate individual differences in window selection. The algorithm first finds the best window for each individual by using embedded individual-level classifiers and then uses these windows from all participants to build the final classifier. The performance of the ITWS algorithm is evaluated when used with eXtreme gradient boosting, convolutional neural networks, and deep neural networks. Our results show that the proposed algorithm achieved significant improvement compared to the previous state of the art in terms of brain-based classification of mind-wandering, with an average F1-score of 73.2%. This builds a foundation for mind-wandering detection for both the evaluation of multimodal learning interfaces and for future attention-aware systems.
Similar content being viewed by others
References
Friedman JH (1989) Regularized discriminant analysis. J Am Stat Assoc 84(405):165–175
Afergan D, Peck EM, Solovey ET, Jenkins A, Hincks SW, Brown ET, Chang R, Jacob RJ (2014) Dynamic difficulty using brain metrics of workload. In: Proceedings of the 32nd annual ACM conference on Human factors in computing systems—CHI’14, pp 3797–3806. https://doi.org/10.1145/2556288.2557230
Balakrishnama S, Ganapathiraju A (1998) Linear discriminant analysis—a brief tutorial. Inst Signal Inf Process 18(4):1–8
Bandara D, Velipasalar S, Bratt S, Hirshfield L (2018) Building predictive models of emotion with functional near-infrared spectroscopy. Int J Hum–Comput Stud 110:75–85
Bixler R, DMello S (2016) Automatic gaze-based user-independent detection of mind wandering during computerized reading. User Model User-Adapt Interact 26(1):33–68. https://doi.org/10.1007/s11257-015-9167-1
Blanchard N, Bixler R, Joyce T, D’Mello S (2014) Automated physiological-based detection of mind wandering during learning. In: International conference on intelligent tutoring systems. Springer, Cham, pp 55–60. https://doi.org/10.1007/978-3-319-07221-0_7
Bosch N, Dmello S (2019) Automatic detection of mind wandering from video in the lab and in the classroom. IEEE Trans Affect Comput. https://doi.org/10.1109/taffc.2019.2908837
Buccino AP, Keles HO, Omurtag A (2016) Hybrid EEG-fNIRS asynchronous brain–computer interface for multiple motor tasks. PLoS ONE 11(1):1–16. https://doi.org/10.1371/journal.pone.0146610
Champaign J, McCalla G (2015) AttentiveLearner: improving mobile MOOC learning via implicit heart rate tracking. In: International conference on artificial intelligence in education. Springer, Cham, pp 367–376. https://doi.org/10.1007/978-3-319-19773-9
Chance B, Anday E, Nioka S, Zhou S, Hong L, Worden K, Li C, Murray T, Ovetsky Y, Pidikiti D, Thomas R (1998) A novel method for fast imaging of brain function, non-invasively, with light. Opt Express 2(10):411. https://doi.org/10.1364/oe.2.000411
Chawla Keven NV, Bowyer KW, Hall LO, Kegelmeyer WP (2002) SMOTE: Synthetic Minority Over-sampling Technique Nitesh. J Artif Intell Res 16(1):321–357. https://doi.org/10.1613/jair.953
Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the ACM SIGKDD international conference on knowledge discovery and data mining 13–17 August, pp 785–794. https://doi.org/10.1145/2939672.2939785
Cho BH, Lee JM, Ku J, Jang DP, Kim J, Kim IY, Lee JH, Kim SI (2002) Attention enhancement system using virtual reality and EEG biofeedback. In: Proceedings IEEE virtual reality. IEEE, pp 156–163
Christoff K, Gordon AM, Smallwood J, Smith R, Schooler JW (2009) Experience sampling during fMRI reveals default network and executive system contributions to mind wandering. In: Proceedings of the National Academy of Sciences, pp 8719–8724. papers3://publication/uuid/F7FC47FD-5AB1-4FCE-8F30-A99EE1870E01
Combrisson E, Jerbi K (2015) Exceeding chance level by chance: the caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy. J Neurosci Methods 250:126–136
Connolly TM, Boyle EA, MacArthur E, Hainey T, Boyle JM (2012) A systematic literature review of empirical evidence on computer games and serious games. Comput Educ 59(2):661–686
Cui X, Bray S, Bryant DM, Glover GH, Reiss AL (2011) A quantitative comparison of nirs and fmri across multiple cognitive tasks. Neuroimage 54(4):2808–2821
D’Mello S, Olney A, Williams C, Hays P (2012) Gaze tutor: a gaze-reactive intelligent tutoring system. Int J Hum–Comput Stud 70(5):377–398
Durantin G, Dehais F, Delorme A (2015) Characterization of mind wandering using fNIRS. Front Syst Neurosci 9:45
Fox KC, Spreng RN, Ellamil M, Andrews-Hanna JR, Christoff K (2015) The wandering brain: meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes. NeuroImage 111:611–621. https://doi.org/10.1016/j.neuroimage.2015.02.039
Franklin MS, Smallwood J, Schooler JW (2011) Catching the mind in flight: using behavioral indices to detect mindless reading in real time. Psychon Bull Rev 18(5):992–997. https://doi.org/10.3758/s13423-011-0109-6
Guo Y, Liu Y, Oerlemans A, Lao S, Wu S, Lew MS (2016) Deep learning for visual understanding: a review. Neurocomputing 187:27–48
Harrivel AR, Stephens CL, Milletich RJ, Heinich CM, Last MC, Napoli NJ, Abraham NA, Prinzel LJ, Motter MA, Pope AT (2017) Prediction of cognitive states during flight simulation using multimodal psychophysiological sensing. AIAA Inf Syst AIAA Infotech Aerosp 2017:1–10. https://doi.org/10.2514/6.2017-1135
Herff C, Heger D, Fortmann O, Hennrich J, Putze F, Schultz T (2014) Mental workload during n-back task quantified in the prefrontal cortex using fNIRS. Front Hum Neurosci 7:935
Hirshfield LM, Solovey ET, Girouard A, Kebinger J, Jacob RJ, Sassaroli A, Fantini S (2009) Brain measurement for usability testing and adaptive interfaces: an example of uncovering syntactic workload with functional near infrared spectroscopy. In: Proceedings of the SIGCHI conference on human factors in computing systems. ACM, pp 2185–2194
Ho TKK, Gwak J, Park CM, Song JI (2019) Discrimination of mental workload levels from multi-channel fNIRS using deep leaning-based approaches. IEEE Access 7:24392–24403. https://doi.org/10.1109/ACCESS.2019.2900127
Hosseini R, Walsh B, Tian F, Wang S (2018) An fNIRS-based feature learning and classification framework to distinguish hemodynamic patterns in children who stutter. IEEE Trans Neural Syst Rehabil Eng 26(6):1254–1263. https://doi.org/10.1109/TNSRE.2018.2829083
Huppert TJ, Diamond SG, Franceschini MA, Boas DA (2009) HomER: a review of time-series analysis methods for near-infrared spectroscopy of the brain. Appl Opt 48(10):33. https://doi.org/10.1364/AO.48.00D280
Hutt S, Mills C, Bosch N, Krasich K, Brockmole J, D’mello S (2017) Out of the Fr-“Eye”-ing Pan: towards gaze-based models of attention during learning with technology in the classroom. In: Proceedings of the 25th conference on user modeling, adaptation and personalization. ACM, pp 94–103. https://doi.org/10.1145/3079628.3079669
Izzetoglu M, Izzetoglu K, Bunce S, Ayaz H, Devaraj A, Onaral B, Pourrezaei K (2005) Functional near-infrared neuroimaging. IEEE Trans Neural Syst Rehabil Eng 13:153–159. https://doi.org/10.1109/TNSRE.2005.847377
Jha AP, Morrison AB, Dainer-Best J, Parker S, Rostrup N, Stanley EA (2015) Minds at attention: mindfulness training curbs attentional lapses in military cohorts. PloS ONE 10(2)
Jin CY, Borst JP, Vugt MKV (2019) Predicting task-general mind-wandering with EEG. Cogn Affect Behav Neurosci 19:1–15
Kawashima I, Kumano H (2017) Prediction of mind-wandering with electroencephalogram and non-linear regression modeling. Front Hum Neurosci 11(July):1–10. https://doi.org/10.3389/fnhum.2017.00365
Kerous B, Skola F, Liarokapis F (2018) Eeg-based bci and video games: a progress report. Virtual Real 22(2):119–135
Khan MJ, Liu X, Bhutta MR, Hong KS (2016) Drowsiness detection using fNIRS in different time windows for a passive BCI. In: 2016 6th IEEE international conference on biomedical robotics and biomechatronics (BioRob). IEEE, pp 227–231. https://doi.org/10.1109/BIOROB.2016.7523628
Killingsworth MA, Gilbert DT (2010) A wandering mind is an unhappy mind. Science 330(6006):932. https://doi.org/10.1126/science.1192439
Makantasis K, Doulamis A, Doulamis N, Nikitakis A, Voulodimos A (2018) Tensor-based nonlinear classifier for high-order data analysis. In: 2018 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2221–2225
Manly T, Robertson IH, Galloway M, Hawkins K (1999) The absent mind: further investigations of sustained attention to response. Neuropsychologia 37(6):661–670
Mayseless N, Hawthorne G, Reiss AL (2019) Real-life creative problem solving in teams: fNIRS based hyperscanning study. NeuroImage 203(August):116161. https://doi.org/10.1016/j.neuroimage.2019.116161
McKendrick R, Parasuraman R, Ayaz H (2015) Wearable functional near infrared spectroscopy (fNIRS) and transcranial direct current stimulation (tDCS): expanding vistas for neurocognitive augmentation. Front Syst Neurosci. https://doi.org/10.3389/fnsys.2015.00027
McKendrick R, Parasuraman R, Murtza R, Formwalt A, Baccus W, Paczynski M, Ayaz H (2016) Into the wild: neuroergonomic differentiation of hand-held and augmented reality wearable displays during outdoor navigation with functional near infrared spectroscopy. Front Hum Neurosci 10(MAY2016):216. https://doi.org/10.3389/fnhum.2016.00216
Mills C, Mello SD (2015) Toward a real-time (Day) Dreamcatcher: sensor-free detection of mind wandering during online reading. In: International educational data mining society
Mooneyham BW, Schooler JW (2013) The costs and benefits of mind-wandering: a review. Can J Exp Psychol 67(1):11–18. https://doi.org/10.1037/a0031569
Naseer N, Hong KS (2013) Classification of functional near-infrared spectroscopy signals corresponding to the right- and left-wrist motor imagery for development of a brain–computer interface. Neurosci Lett 553:84–89. https://doi.org/10.1016/j.neulet.2013.08.021
Naseer N, Hong KS (2015) fNIRS-based brain–computer interfaces: a review. Front Hum Neurosci 9:3
Naseer N, Hong KS (2015) fNIRS-based brain–computer interfaces: a review. Front Hum Neurosci 9:1–15. https://doi.org/10.3389/fnhum.2015.00172
Nelson BC (2007) Exploring the use of individualized, reflective guidance in an educational multi-user virtual environment. J Sci Educ Technol 16(1):83–97
Ninaus M, Kober SE, Friedrich EV, Dunwell I, Freitas SD, Arnab S, Ott M, Kravcik M, Lim T, Louchart SJJ et al (2014) Neurophysiological methods for monitoring brain activity in serious games and virtual environments: a review. Int J Technol Enhanc Learn 6(1):78
Noori FM, Naseer N, Qureshi NK, Nazeer H, Khan RA (2017) Optimal feature selection from fNIRS signals using genetic algorithms for BCI. Neurosci Lett 647:61–66. https://doi.org/10.1016/j.neulet.2017.03.013
Orihuela-Espina F, Leff DR, James DR, Darzi AW, Yang GZ (2010) Quality control and assurance in functional near infrared spectroscopy (fNIRS) experimentation. Phys Med Biol 55(13):3701–3724. https://doi.org/10.1088/0031-9155/55/13/009
Peck EM, Carlin E, Jacob R (2015) Designing brain–computer interfaces for attention-aware systems. Computer 48(10):34–42. https://doi.org/10.1109/MC.2015.315
Pike MF, Maior HA, Porcheron M, Sharples SC, Wilson ML (2014) Measuring the effect of think aloud protocols on workload using fNIRS. In: Proceedings of the 32nd annual ACM conference on Human factors in computing systems. ACM, pp 3807–3816. https://doi.org/10.1145/2556288.2556974
Pinti P, Scholkmann F, Hamilton A, Burgess P, Tachtsidis I (2018) Current status and issues regarding pre-processing of fNIRS neuroimaging data: an investigation of diverse signal filtering methods within a general linear model framework. Front Hum Neurosci 12:505
Rapp DN (2006) The value of attention aware systems in educational settings. Comput Hum Behav 22(4):603–614
Schmorrow DD, Fidopiastis CM (2015) Phylter: a system for modulating notifications in wearables using physiological sensing. In: International conference on augmented cognition, vol 9183. Springer, Cham, pp 167–177. https://doi.org/10.1007/978-3-319-20816-9
Schooler JW, Smallwood J, Christoff K, Handy TC, Reichle ED, Sayette MA (2011) Meta-awareness, perceptual decoupling and the wandering mind. Trends Cogn Sci 15(7):319–326. https://doi.org/10.1016/j.tics.2011.05.006
Shin J, Müller KR, Hwang HJ (2016) Near-infrared spectroscopy (NIRS)-based eyes-closed brain–computer interface (BCI) using prefrontal cortex activation due to mental arithmetic. Sci Rep 6(October):1–11. https://doi.org/10.1038/srep36203
Shin J, Von Luhmann A, Blankertz B, Kim DW, Jeong J, Hwang HJ, Muller KR (2017) Open access dataset for EEG + NIRS single-trial classification. IEEE Trans Neural Syst Rehabil Eng 25(10):1735–1745. https://doi.org/10.1109/TNSRE.2016.2628057
Shin J, Von Lühmann A, Kim DW, Mehnert J, Hwang HJ, Müller KR (2018) Simultaneous acquisition of eeg and nirs during cognitive tasks for an open access dataset. Sci Data 5:180003
Smallwood J, Schooler JW (2006) The restless mind. Psychol Bull 132(6):946–958. https://doi.org/10.1037/0033-2909.132.6.946
Smallwood J, Fishman DJ, Schooler JW (2007) Counting the cost of an absent mind: mind wandering as an underrecognized influence on educational performance. Psychon Bull Rev 14(2):230–236. https://doi.org/10.3758/BF03194057
Smallwood J, Beach E, Schooler JW, Handy TC (2008) Going awol in the brain: mind wandering reduces cortical analysis of external events. J Cogn Neurosci 20(3):458–469
Solovey ET, Girouard A, Chauncey K, Hirshfield LM, Sassaroli A, Zheng F, Fantini S, Jacob RJK (2009) Using fNIRS brain sensing in realistic HCI settings: experiments and guidelines. In: Proceedings of the 22nd annual ACM symposium on user interface software and technology. ACM
Treacy Solovey E, Afergan D, Peck EM, Hincks SW, Jacob RJ (2015) Designing implicit interfaces for physiological computing: guidelines and lessons learned using fNIRS. ACM Trans Comput–Hum Interact 21(6):35
Voulodimos A, Doulamis N, Doulamis A, Protopapadakis E (2018) Deep learning for computer vision: a brief review. Comput Intell Neurosci 2018:7068349. https://doi.org/10.1155/2018/7068349
Wouters P, Van der Spek ED, Van Oostendorp H (2009) Current practices in serious game research: a review from a learning outcomes perspective. In: Games-based learning advancements for multi-sensory human computer interfaces: techniques and effective practices. IGI Global, pp 232–250
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This work was supported in part by the U.S. National Science Foundation under Grants NCS-1835307, CNS-1711773 and NCS-1835251.
Rights and permissions
About this article
Cite this article
Liu, R., Walker, E., Friedman, L. et al. fNIRS-based classification of mind-wandering with personalized window selection for multimodal learning interfaces. J Multimodal User Interfaces 15, 257–272 (2021). https://doi.org/10.1007/s12193-020-00325-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12193-020-00325-z