EEG signal detection and analysis with application in educational robotics

• Adam Henrique Moreira Pinto USP
• Roseli Aparecida Francelin Romero USP

DOI: https://doi.org/10.5753/wtdr_ctdr.2020.14951

Resumo

Brain-Computer Interfaces add information to robots directly from users’ brain, allowing for the interpretation of attention, engagement, and even student mistakes. However, most applications still have low accuracy in recognizing this information. In this paper, an Error Related Potential (ErrP) detection system is being proposed. For this, a new database was created by using a serious game and a humanoid robot aiming to force errors and mental state changings of the user. Wavelets and Fourier Transforms were compared to signal feature extraction, classified using both MultiLayer Perceptron (MLP) and Convolutional Neural Networks (CNN). Experiments demonstrate that the wavelet outperformed Fourier transform to extract the ErrP signal, and CNN had a higher accuracy than MLP in the classification.

Palavras-chave: Brain Computer Interface, Biomedical Signals, Robotics

Referências

Breazeal, C.L.: Designing sociable robots. MIT press (2004).

Brigham, E.O., Brigham, E.O.: The fast Fourier transform and its applications, vol. 448. prentice Hall Englewood Cliffs, NJ (1988).

Edwards, A., Edwards, C., Spence, P.R., Harris, C., Gambino, A.: Robots in the classroom: Differences in students’ perceptions of credibility and learning between “teacher as robot” and “robot as teacher”. Computers in Human Behavior (2016).

Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural computation 9(8), 1735– 1780 (1997).

Jasper, H.: Ten-twenty electrode system of the international federation. Electroenceph. Clin. Neurophysiol, 10:371-375 (1958).

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014).

Klem, G.H., Lüders, H.O., Jasper, H.H., Elger, C.: The ten-twenty electrode system of the international federation. the international federation of clinical neurophysiology. Electroencephalography and clinical neurophysiology. Supplement 52, 3–6 (1999).

Pinto, A.H.M., Mijan, M.A.M., Nardari, G.V., Romero, R.A.F.: Comparing features extraction and classification methods to recognize errp signals. In: 2018 Latin American Robotic Symposium, 2018 Brazilian Symposium on Robotics (SBR) and 2018 Workshop on Robotics in Education (WRE). pp. 1–7 (Nov 2018).

Poormehdi Ghaemmaghami, M.: Tracking of humans in video stream using lstm recurrent neural network (2017).

Sanghvi, J., Castellano, G., Leite, I., Pereira, A., McOwan, P.W., Paiva, A.: Automatic analysis of affective postures and body motion to detect engagement with a game companion. In: Proceedings of the 6th international conference on Human-robot interaction. pp. 305–312. ACM (2011).

Sobotta, J.: Atlas da anatomia humana. 22. ed. Rio de Janeiro: Guanabara Koogan (2006).

Spüler, M., Niethammer, C.: Error-related potentials during continuous feedback: using eeg to detect errors of different type and severity. Frontiers in human neuroscience 9, 155 (2015).

Stanković, R.S., Falkowski, B.J.: The haar wavelet transform: its status and achievements. Computers & Electrical Engineering 29(1), 25–44 (2003).

Tatum, W.O.: Ellen r. grass lecture: Extraordinary eeg. The Neurodiagnostic Journal 54(1), 3–21 (2014). https://doi.org/10.1080/21646821.2014.11079932, https://www.tandfonline.com/doi/abs/10.1080/21646821.2014.11079932.

Tozadore, D., Pinto, A., Valentini, J., Camargo, M., Zavarizz, R., Rodrigues, V., Vedrameto, F., Romero, R.: Project r-castle: Robotic-cognitive adaptive system for teaching and learning. IEEE Transactions on Cognitive and Developmental Systems pp. 1–1 (2019). https://doi.org/10.1109/TCDS.2019.2941079

Yasui, Y.: A brainwave signal measurement and data processing technique for daily life applications. Journal of physiological anthropology 28(3), 145–150 (2009).