Abstract
The purpose of this paper is to use the low-cost EEG device to collect brain signal and use the neural network algorithm to classify the attention level based on the recorded EEG data as input. Fifteen volunteers participated in the experiment. The Emotiv Insight headset was used to record the brain signal during participants performing the Visual Attention Colour Pattern Recognition (VACPR) test. The test was divided into 2 tasks namely task A for stimulating the participant to be attentive and task B for stimulating the participant to be inattention. Later, the recorded raw EEG signal passed through a Notch filter and Independent Component Analysis (ICA) to filter out the noise. After that, Power Spectral Density (PSD) was used to calculate the power value of pre-processed EEG signal to verify whether the recorded EEG signal is consistent with the mental state stimulated during task A and task B before performing classification. Since EEG signals exhibit significantly complex behaviour with dynamic and non-linear characteristics, Convolutional Neural Network (CNN) shows great promise in helping to classify EEG signal due to its capacity to learn good feature representation from the signals. An accuracy of 76% was achieved, indicating the feasibility of using Emotiv Insight with CNN for attention level classification.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Das, M., Bennett, D.M., Dutton, G.N.: Visual attention as an important visual function: an outline of manifestations, diagnosis and management of impaired visual attention. Br. J. Ophthalmol. 91(11), 1556–1560 (2007). https://doi.org/10.1136/bjo.2006.104844
Tóth, B., et al.: Attention and speech-processing related functional brain networks activated in a multi-speaker environment. PLOS ONE 14(2), e0212754 (2019)
Shestyuk, A.Y., Kasinathan, K., Karapoondinott, V., Knight, R.T., Gurumoorthy, R.: Individual EEG measures of attention, memory, and motivation predict population level TV viewership and Twitter engagement. PLoS ONE 14(3), 1–27 (2019). https://doi.org/10.1371/journal.pone.0214507
Aliakbaryhosseinabadi, S., Kamavuako, E.N., Jiang, N., Farina, D., Mrachacz-Kersting, N.: Classification of EEG signals to identify variations in attention during motor task execution. J. Neurosci. Methods 284, 27–34 (2017). https://doi.org/10.1016/j.jneumeth.2017.04.008
Tan, B.H.: Using a Low-cost EEG Sensor to Detect Mental States (2012)
Van Hal, B., Rhodes, S., Dunne, B., Bossemeyer, R.: Low-cost EEG-based sleep detection. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2014, pp. 4571–4574 (2014). https://doi.org/10.1109/EMBC.2014.6944641
Zabcikova, M.: Visual and auditory stimuli response, measured by Emotiv Insight headset. MATEC Web Conf. 292, 01024 (2019). https://doi.org/10.1051/matecconf/201929201024
KumarAhirwal, M., londhe, D.N.: Power spectrum analysis of EEG signals for estimating visual attention. Int. J. Comput. Appl. 42(15), 34–40 (2012). https://doi.org/10.5120/5769-7993
Jebelli, H., Khalili, M.M., Lee, S.: Mobile EEG-based workers’ stress recognition by applying deep neural network. In: Mutis, I., Hartmann, T. (eds.) Advances in Informatics and Computing in Civil and Construction Engineering, pp. 173–180. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-00220-6_21
Borst, J., Schneider, D., Walsh, M., Anderson, J.: Stages of processing in associative recognition: evidence from behavior, EEG, and classification. J. Cogn. Neurosci. 25(12), 2151–2166 (2013). https://doi.org/10.1162/jocn_a_00457
Stoet, G.: PsyToolkit: a software package for programming psychological experiments using Linux. Behav. Res. Methods 42(4), 1096–1104 (2010). https://doi.org/10.3758/BRM.42.4.1096
Lim, Z.Y., Sim, K.S., Tan, S.C.: An evaluation of left and right brain dominance using electroencephalogram signal. Eng. Lett. 28(4), 1358–1367 (2020)
Gola, M., Magnuski, M., Szumska, I., Wróbel, A.: EEG beta band activity is related to attention and attentional deficits in the visual performance of elderly subjects. Int. J. Psychophysiol. 89(3), 334–341 (2013). https://doi.org/10.1016/j.ijpsycho.2013.05.007
Toa, C.K., Sim, K.S., Tan, S.C.: Electroencephalogram-based attention level classification using convolution attention memory neural network. IEEE Access 9, 58870–58881 (2021). https://doi.org/10.1109/ACCESS.2021.3072731
Abhang, P.A., Gawali, B.W., Mehrotra, S.C.: Chapter 3: Technical aspects of brain rhythms and speech parameters. In: Abhang, P.A., Gawali, B.W., Mehrotra, S.C. (eds.). Introduction to EEG- and Speech-Based Emotion Recognition, pp. 51–79. Academic Press, New York (2016)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Toa, C.K., Sim, K.S., Tan, S.C. (2021). Emotiv Insight with Convolutional Neural Network: Visual Attention Test Classification. In: Wojtkiewicz, K., Treur, J., Pimenidis, E., Maleszka, M. (eds) Advances in Computational Collective Intelligence. ICCCI 2021. Communications in Computer and Information Science, vol 1463. Springer, Cham. https://doi.org/10.1007/978-3-030-88113-9_28
Download citation
DOI: https://doi.org/10.1007/978-3-030-88113-9_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-88112-2
Online ISBN: 978-3-030-88113-9
eBook Packages: Computer ScienceComputer Science (R0)