Cognitive Load Detection on Commercial EEG Devices: An Optimized Signal Processing Chain

1. Chen, F., Ruiz, N., Choi, E., Epps, J., Khawaja, M.A., Taib, R., Wang, Y.: Multimodal behavior and interaction as indicators of cognitive load. ACM Trans. Interact. Intell. Syst. (TiiS) 2(4), 22 (2012)

   Google Scholar 

2. Gubbi, J., et al.: Internet of Things (IoT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)

   Article  Google Scholar 

3. Misra, P., et al.: A computing platform for development and deployment of sensor data based applications and services, Patent No. WO2013072925 A2

   Google Scholar 

4. Fok, A.W., Ip, H.H.-S.: Personalized education: an exploratory study of learning pedagogies in relation to personalization technologies. In: Liu, W., Shi, Y., Li, Q. (eds.) ICWL 2004. LNCS, vol. 3143, pp. 407–415. Springer, Heidelberg (2004)

   Chapter  Google Scholar 

5. Pal, A., Chatterjee, D., kar, D.: Evaluation and Improvements of on-screen keyboard for Television and Set-top Box, ISCE, Singapore, 14–17 June 2011

   Google Scholar 

6. Hitt, J.M., Kring, J.P., Daskarolise, E., Morris, C., Mouloua, M.: Assessing mental workload with subjective measures: an analytical review of the nasa-tlx index since its inception. Hum. Factors Ergon. Soc. Ann. Meet. 43, 1404 (1999)

   Article  Google Scholar 

7. Haapalainen, E., et al.: Psycho-physiological measures for assessing cognitive load. In: Proceedings of the 12th ACM Ubicomp (2010)

   Google Scholar 

8. Molteni, E., et al.: Frontal brain activation during a working memory task: a time-domain fNIRS study. SPIE BiOS: Biomedical Optics. International Society for Optics and Photonics (2009)

   Google Scholar 

9. Brody, B.A., et al.: The role of frontal and parietal cortex in cognitive processing: tests of spatial and sequence functions. Brain J. Neurology 101(4), 607–633 (1978)

   Article  Google Scholar 

10. Hjorth, B.: EEG analysis based on time domain properties. Electroencephalogr. Clin. Neurophysiol. 29(3), 306–310 (1970)

    Article  Google Scholar 

11. Chatterjee, D., Sinharay, A., Konar, A.: EEG-Based fuzzy cognitive load classification during logical analysis of program segments. In: IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 7–10 July 2013

    Google Scholar 

12. Zarjam, P., Epps, J., Chen, F.: Evaluation of working memory load using EEG signals In: Proceedings of APSIPA Annual Summit and Conference, pp. 715–719 (2010)

    Google Scholar 

13. Wang, Y.L., Liu, J.H., Liu, Y.C.: Automatic removal of ocular artifacts from electroencephalogram using Hilbert-Huang transform. In: The 2nd International Conference on Bioinformatics and Biomedical Engineering, 2008. ICBBE 2008, pp. 2138–2141 (2008)

    Google Scholar 

14. Wojcikiewicz, W., Vidaurre, C., Kawanabe, M.: Stationary common spatial patterns: towards robust classification of non-stationary eeg signals, In: 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 577–580.(2011)

    Google Scholar 

15. Lotte, F., Guan, C.: Regularizing common spatial patterns to improve BCI designs: unified theory and new algorithms. IEEE Trans. Biomed. Eng. 58(2), 355–362 (2011)

    Article  Google Scholar 

16. Sauseng, P., Klimesch, W., Schabus, M., Doppelmayr, M.: Fronto-parietal EEG coherence in theta and upper alpha reflect central executive functions of working memory. Int. J. Psychophysiol. 57(2), 97–103 (2005)

    Article  Google Scholar 

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