Abstract
In this study we propose a new machine learning classification method to distinguish brain activity patterns for healthy subjects. We used ElectroEncephaloGraphic (EEG) data associated with five userdefined mental tasks. We collected a data set using the Muse headband with four EEG electrodes (TP9, AF7, AF8, and TP10). Sixteen healthy subjects participated in this six-session experiment. In each session, we instructed them to conduct five different one-minute tasks, we abbreviated the tasks as think, count, recall, breathe and draw. After dealing with noise and outliers, we first fairly compared the performance of existing classifiers, including linear classifiers, non-linear Bayesian classifiers, nearest-neighbor classifiers, ensemble methods, and deep learning, with the same settings. Among these, Random Forest (RF), and Long Short-Term Memory (LSTM) outperform others. We then introduced a new ensemble classifier called Time Continuity Voting (TCV), combining these top two. The timewise cross-validation results showed that TCV could correctly classify the five tasks (20% by chance) with an accuracy of 70% which is at least 6% higher than the top individual classifiers.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Ahangi, A., Karamnejad, M., Mohammadi, N., Ebrahimpour, R., Bagheri, N.: Multiple classifier system for EEG signal classification with application to brain–computer interfaces. Neural Comput. Appl. 23(5), 1319–1327 (2013)
Arifoglu, D., Bouchachia, A.: Activity recognition and abnormal behaviourdetection with recurrent neural networks. Proc. Comput. Sci. 110, 86–93 (2017)
Bashivan, P., Rish, I., Yeasin, M., Codella, N.: Learning representationsfrom EEG with deep recurrent-convolutional neural networks. ar**v preprint ar**v:1511.06448 (2015)
Bird, J.J., Manso, L.J., Ribeiro, E.P., Ekart, A., Faria, D.R.: A study onmental state classification using EEG-based brain-machine interface. In: 2018 International Conference on Intelligent Systems (IS), pp. 795–800. IEEE (2018)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Coyle, D., Principe, J., Lotte, F., Nijholt, A.: Guest editorial: brain/neuronal-computer game interfaces and interaction. IEEE Trans. Comput. Intell. AI Games 5(2), 77–81 (2013)
Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16(3), 031001 (2019)
Deeb, F.A., DiLillo, A., Hickey, T.: Using spinoza log data to enhance CS1 pedagogy. In: McLaren, B., Reilly, R., Zvacek, S., Uhomoibhi, J. (eds.) International Conference on Computer Supported Education. Communications in Computer and Information Science, vol. 1022, pp. 14–36. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-21151-6_2
Devlaminck, D., et al.: From circular ordinal regression to multilabel classification. In: Proceedings of the 2010 Workshop on Preference Learning (European Conference on Machine Learning, ECML), p. 15 (2010)
Freund, Y., Schapire, R.E.: A decision-theoretic generalization of on-line learning and an application to boosting. J. Comput. Syst. Sci. 55(1), 119–139 (1997)
Gang, P., et al.: User-driven intelligent interface on the basis of multimodal augmented reality and brain computer interaction for people with functional disabilities. In: Arai, K., Kapoor, S., Bhatia, R. (eds.) Future of Information and Communication Conference. Advances in Intelligent Systems and Computing, vol. 886, pp. 612–631. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-03402-3_43
Jain, A.K., Duin, R.P.W., Mao, J.: Statistical pattern recognition: a review. IEEE Trans. Pattern Anal. Mach. Intell. 22(1), 4–37 (2000)
Kwak, N.S., Müller, K.R., Lee, S.W.: A convolutional neural network for steady state visual evoked potential classification under ambulatory environment. PloS One 12(2), e0172578 (2017)
Lindig-León, C., Bougrain, L.: Comparison of sensorimotor rhythms in EEG signals during simple and combined motor imageries over the contra and ipsilateral hemispheres. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 3953–3956. IEEE (2015)
Lotte, F.: Signal processing approaches to minimize or suppress calibration time in oscillatory activity-based brain–computer interfaces. Proc. IEEE 103(6), 871–890 (2015)
Lotte, F., et al.: A review of classification algorithms for EEG-based brain–computer interfaces: a 10 year update. J. Neural Eng. 15(3), 031005 (2018)
Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain–computer interfaces. J. Neur. Eng. 4(2), R1 (2007)
Mihajlović, V., Grundlehner, B., Vullers, R., Penders, J.: Wearable, wireless EEG solutions in daily life applications: what are we missing? IEEE J. Biomed. Health Inform. 19(1), 6–21 (2014)
Poulsen, A.T., Kamronn, S., Dmochowski, J., Parra, L.C., Hansen, L.K.: EEG in the classroom: synchronised neural recordings during video presentation. Sci. Rep. 7, 43916 (2017)
Qu, X., Hall, M., Sun, Y., Sekuler, R., Hickey, T.J.: A personalized reading coach using wearable EEG sensors-a pilot study of brainwave learning analytics. In: CSEDU (2), pp. 501–507 (2018)
Qu, X., Sun, Y., Sekuler, R., Hickey, T.: EEG markers of stem learning. In: IEEE Frontiers in Education Conference (FIE), pp. 1–9. IEEE (2018)
Saeb, S., Lonini, L., Jayaraman, A., Mohr, D.C., Kording, K.P.: Voodoo machine learning for clinical predictions. Biorxiv ar**v:059774 (2016)
Seeck, M., et al.: The standardized EEG electrode array of the IFCN. Clin. Neurophysiol. 128(10), 2070–2077 (2017)
Sha, L., Hong, P.: Neural knowledge tracing. In: Frasson, C., Kostopoulos, G. (eds.) International Conference on Brain Function Assessment in Learning. Lecture Notes in Computer Science, vol. 10512, pp. 108–117. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67615-9_10
Tarimo, W.T., Deeb, F.A., Hickey, T.J.: Early detection of at-risk students in CS1 using teachback/spinoza. J. Comput. Sci. Coll. 31(6), 105–111 (2016)
Zander, T.O., Kothe, C.: Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. J. Neural Eng. 8(2), 025005 (2011)
Zeyl, T., Yin, E., Keightley, M., Chau, T.: Partially supervised P300 speller adaptation for eventual stimulus timing optimization: target confidence is superior to error-related potential score as an uncertain label. J. Neural Eng. 13(2), 026008 (2016)
Zhang, J., Wu, Y., Bai, J., Chen, F.: Automatic sleep stage classification based on sparse deep belief net and combination of multiple classifiers. Trans. Inst. Meas. Control 38(4), 435–451 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Qu, X., Liu, P., Li, Z., Hickey, T. (2020). Multi-class Time Continuity Voting for EEG Classification. In: Frasson, C., Bamidis, P., Vlamos, P. (eds) Brain Function Assessment in Learning. BFAL 2020. Lecture Notes in Computer Science(), vol 12462. Springer, Cham. https://doi.org/10.1007/978-3-030-60735-7_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-60735-7_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60734-0
Online ISBN: 978-3-030-60735-7
eBook Packages: Computer ScienceComputer Science (R0)