Abstract
Electroencephalogram (EEG) is a non-invasive technology with high temporal resolution, widely used in Brain-Computer Interfaces (BCIs) for mental workload (MWL) classification. However, numerous EEG channels in current devices can make them bulky, uncomfortable, and time-consuming to operate in real-life scenarios. A Riemannian geometry approach has gained attention for channel selection to address this issue. In particular, Riemannian geometry employs covariance matrices of EEG signals to identify the optimal set of EEG channels, given a specific covariance estimator and desired channel number. However, previous studies have not thoroughly assessed the limitations of various covariance estimators, which may influence the analysis results. In this study, we aim to investigate the impact of different covariance estimators, namely Empirical Covariance (EC), Shrunk Covariance (SC), Ledoit-Wolf (LW), and Oracle Approximating Shrinkage (OAS), along with the influence of channel numbers on the process of EEG channel selection. We also examine the performance of selected channels using diverse deep learning models, namely Stacked Gated Recurrent Unit (GRU), Bidirectional Gated Recurrent Unit (BGRU), and BGRU-GRU models, using a publicly available MWL EEG dataset. Our findings show that although no universally optimal channel number exists, employing as few as four channels can achieve an accuracy of 0.940 (±0.036), enhancing practicality for real-world applications. In addition, we discover that the BGRU model, when combined with OAS covariance estimators and a 32-channel configuration, demonstrates superior performance in MWL classification tasks compared to other estimator combinations. Indeed, this study provides insights into the effectiveness of various covariance estimators and the optimal channel subsets for highly accurate MWL classification. These findings can potentially advance the development of EEG-based BCI applications.
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References
Alotaiby, T., El-Samie, F.E.A., Alshebeili, S.A., Ahmad, I.: A review of channel selection algorithms for EEG signal processing. EURASIP J. Adv. Signal Process. 2015, 1–21 (2015)
Alyasseri, Z.A.A., Khader, A.T., Al-Betar, M.A., Alomari, O.A.: Person identification using EEG channel selection with hybrid flower pollination algorithm. Pattern Recogn. 105, 107393 (2020)
Arvaneh, M., Guan, C., Ang, K.K., Quek, C.: Optimizing the channel selection and classification accuracy in EEG-based BCI. IEEE Trans. Biomed. Eng. 58(6), 1865–1873 (2011)
Baig, M.Z., Aslam, N., Shum, H.P.: Filtering techniques for channel selection in motor imagery EEG applications: a survey. Artif. Intell. Rev. 53, 1207–1232 (2020)
Baig, M.Z., Aslam, N., Shum, H.P., Zhang, L.: Differential evolution algorithm as a tool for optimal feature subset selection in motor imagery EEG. Expert Syst. Appl. 90, 184–195 (2017)
Barachant, A., Bonnet, S.: Channel selection procedure using Riemannian distance for BCI applications. In: 2011 5th International IEEE/EMBS Conference on Neural Engineering, pp. 348–351. IEEE (2011)
Belakhdar, I., Kaaniche, W., Djemal, R., Ouni, B.: Single-channel-based automatic drowsiness detection architecture with a reduced number of EEG features. Microprocess. Microsyst. 58, 13–23 (2018)
Borghini, G., Astolfi, L., Vecchiato, G., Mattia, D., Babiloni, F.: Measuring neurophysiological signals in aircraft pilots and car drivers for the assessment of mental workload, fatigue and drowsiness. Neurosci. Biobehav. Rev. 44, 58–75 (2014)
Brouwer, A.M., Hogervorst, M.A., Van Erp, J.B., Heffelaar, T., Zimmerman, P.H., Oostenveld, R.: Estimating workload using EEG spectral power and ERPS in the n-back task. J. Neural Eng. 9(4), 045008 (2012)
Chen, S., Sun, Y., Wang, H., Pang, Z.: Channel selection based similarity measurement for motor imagery classification. In: 2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), pp. 542–548. IEEE (2020)
Herff, C., Heger, D., Fortmann, O., Hennrich, J., Putze, F., Schultz, T.: Mental workload during n-back task-quantified in the prefrontal cortex using fNIRS. Front. Hum. Neurosci. 7, 935 (2014)
Hinss, M.F., et al.: An EEG dataset for cross-session mental workload estimation: passive BCI competition of the neuroergonomics conference 2021 (2021). https://doi.org/10.5281/zenodo.5055046. The project was validated by the local ethical committee of the University of Toulouse (CER number 2021-342)
Islam, M.R., Barua, S., Ahmed, M.U., Begum, S., Di Flumeri, G.: Deep learning for automatic EEG feature extraction: an application in drivers’ mental workload classification. In: Longo, L., Leva, M.C. (eds.) H-WORKLOAD 2019. CCIS, vol. 1107, pp. 121–135. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-32423-0_8
Jin, J., Miao, Y., Daly, I., Zuo, C., Hu, D., Cichocki, A.: Correlation-based channel selection and regularized feature optimization for mi-based BCI. Neural Netw. 118, 262–270 (2019)
Kingphai, K., Moshfeghi, Y.: On time series cross-validation for deep learning classification model of mental workload levels based on EEG signals. In: Nicosia, G., et al. (eds.) LOD 2022, Part II. LNCS, vol. 13811, pp. 402–416. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-25891-6_30
Lachaux, J.P., Axmacher, N., Mormann, F., Halgren, E., Crone, N.E.: High-frequency neural activity and human cognition: past, present and possible future of intracranial EEG research. Prog. Neurobiol. 98(3), 279–301 (2012)
Lan, T., Erdogmus, D., Adami, A., Pavel, M., Mathan, S.: Salient EEG channel selection in brain computer interfaces by mutual information maximization. In: 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, pp. 7064–7067. IEEE (2006)
Lim, C.G., et al.: A brain-computer interface based attention training program for treating attention deficit hyperactivity disorder. PLoS ONE 7(10), e46692 (2012)
Lin, B.S., Huang, Y.K., Lin, B.S.: Design of smart EEG cap. Comput. Methods Programs Biomed. 178, 41–46 (2019)
Miller, E.K., Cohen, J.D.: An integrative theory of prefrontal cortex function. Annu. Rev. Neurosci. 24(1), 167–202 (2001)
Mognon, A., Jovicich, J., Bruzzone, L., Buiatti, M.: ADJUST: an automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology 48(2), 229–240 (2011)
Mzurikwao, D., et al.: A channel selection approach based on convolutional neural network for multi-channel EEG motor imagery decoding. In: 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE), pp. 195–202. IEEE (2019)
Qu, T., Jin, J., Xu, R., Wang, X., Cichocki, A.: Riemannian distance based channel selection and feature extraction combining discriminative time-frequency bands and riemannian tangent space for mi-bcis. J. Neural Eng. 19(5), 056025 (2022)
Roy, R.N., et al.: Retrospective on the first passive brain-computer interface competition on cross-session workload estimation. Front. Neuroergon. 3 (2022)
Roy, Y., Banville, H., Albuquerque, I., Gramfort, A., Falk, T.H., Faubert, J.: Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 16(5), 051001 (2019)
Santiago-Espada, Y., Myer, R.R., Latorella, K.A., Comstock Jr., J.R.: The multi-attribute task battery II (MATB-II) software for human performance and workload research: a user’s guide. Technical report (2011)
Shen, J., et al.: An optimal channel selection for EEG-based depression detection via kernel-target alignment. IEEE J. Biomed. Health Inform. 25(7), 2545–2556 (2020)
Shi, B., Wang, Q., Yin, S., Yue, Z., Huai, Y., Wang, J.: A binary harmony search algorithm as channel selection method for motor imagery-based BCI. Neurocomputing 443, 12–25 (2021)
Tanaka, M., Ishii, A., Watanabe, Y.: Neural effects of mental fatigue caused by continuous attention load: a magnetoencephalography study. Brain Res. 1561, 60–66 (2014)
Varshney, A., Ghosh, S.K., Padhy, S., Tripathy, R.K., Acharya, U.R.: Automated classification of mental arithmetic tasks using recurrent neural network and entropy features obtained from multi-channel eeg signals. Electronics 10(9), 1079 (2021)
Wang, Z.M., Hu, S.Y., Song, H.: Channel selection method for EEG emotion recognition using normalized mutual information. IEEE Access 7, 143303–143311 (2019)
Yang, S., Yin, Z., Wang, Y., Zhang, W., Wang, Y., Zhang, J.: Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders. Comput. Biol. Med. 109, 159–170 (2019)
Yin, Z., Zhang, J.: Cross-session classification of mental workload levels using EEG and an adaptive deep learning model. Biomed. Signal Process. Control 33, 30–47 (2017)
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Kingphai, K., Moshfeghi, Y. (2024). On Channel Selection for EEG-Based Mental Workload Classification. In: Nicosia, G., Ojha, V., La Malfa, E., La Malfa, G., Pardalos, P.M., Umeton, R. (eds) Machine Learning, Optimization, and Data Science. LOD 2023. Lecture Notes in Computer Science, vol 14506. Springer, Cham. https://doi.org/10.1007/978-3-031-53966-4_30
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