A multidimensional EEG feature extraction attention detection for BCI System


  • Ran Zhang Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
  • Chengyuan Shen Graduate School of Engineering, Saitama Institute of Technology, Fukaya City, Saitama, Japan
  • Linfeng Sui RIKEN Center for Advanced Intelligence Project (AIP), Chuo-ku, Tokyo, Japan
  • Professor Cao RIKEN Center for Advanced Intelligence Project (AIP), Chuo-ku, Tokyo, Japan




Attention Detection, EEG, SVM, BCI, Feature Extraction


Brain-computer interface (BCI) systems are highly valued for their applications in medicine and biology, where accurate analysis of attention levels and electroencephalography (EEG) data is crucial for the success rate of BCI systems. To improve this aspect, we propose to perform attention level determination prior to the execution of the BCI system. To accurately determine the level of attention, we propose a novel feature extraction approach that involves multidimensional feature extraction across multiple frequency bands of EEG data. The multi frequency band waveforms extracted using this method can be cross validated, thereby increasing the robustness of our research. We used publicly available datasets to train a Support Vector Machine (SVM) classifier to develop an efficient attention detection system, and developed a system for collecting EEG data to validate attention levels. The effectiveness and accuracy of the multidimensional feature extraction method was validated by classifying the data collected by the attention detection system. This study highlights the potential of integrating attention detection into BCI systems, pathways for advances in brain science research.


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Acı, Ç. İ., Kaya, M., & Mishchenko, Y. (2019). Distinguishing mental attention states of humans via an eeg-based passive

bci using machine learning methods. Expert Systems with Applications, 134, 153–166. https://doi.org/https:


Ahmed, M. U., Li, L., Cao, J., & Mandic, D. P. (2011). Multivariate multiscale entropy for brain consciousness analysis.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 810–813.


Al-Nafjan, A., & Aldayel, M. (2022). Predict students’ attention in online learning using eeg data. Sustainability,

(11), 6553. https://doi.org/https://doi.org/10.3390/su14116553

Al-Nafjan, A., Hosny, M., Al-Ohali, Y., & Al-Wabil, A. (2017). Review and classification of emotion recognition

based on eeg brain-computer interface system research: A systematic review. Applied Sciences, 7 (12), 1239.


Asadi, S., Roshan, S., & Kattan, M. W. (2021). Random forest swarm optimization-based for heart diseases diagnosis.

Journal of biomedical informatics, 115, 103690. https://doi.org/https://doi.org/10.1016/j.jbi.2021.103690

Bedolla-Ibarra, M. G., Cabrera-Hernandez, M. D. C., Aceves-Fernández, M. A., & Tovar-Arriaga, S. (2022). Classification

of attention levels using a random forest algorithm optimized with particle swarm optimization. Evolving

Systems, 13 (5), 687–702. https://doi.org/https://doi.org/10.1007/s12530-022-09444-2

Blotenberg, I., & Schmidt-Atzert, L. (2019). Towards a process model of sustained attention tests. Journal of Intelligence,

(1), 3. https://doi.org/https://doi.org/10.3390/jintelligence7010003

Cao, Z., & Lin, C.-T. (2017). Inherent fuzzy entropy for the improvement of eeg complexity evaluation. IEEE Transactions

on Fuzzy Systems, 26 (2), 1032–1035. https://doi.org/https://doi.org/10.1109/TFUZZ.2017.2666789

Chiang, H.-S., Chen, M.-Y., & Huang, Y.-J. (2019). Wavelet-based eeg processing for epilepsy detection using fuzzy

entropy and associative petri net. IEEE Access, 7, 103255–103262. https://doi.org/https://doi.org/10.1109/


Cui, G., Zhao, Q., Cao, J., & Cichocki, A. (2014). Hybrid-bci: Classification of auditory and visual related potentials. 2014

Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International

Symposium on Advanced Intelligent Systems (ISIS), 297–300. https://doi.org/https://doi.org/10.1109/SCIS-


Eoh, H. J., Chung, M. K., & Kim, S.-H. (2005). Electroencephalographic study of drowsiness in simulated driving

with sleep deprivation. International Journal of Industrial Ergonomics, 35 (4), 307–320. https://doi.org/https:


Ghassemi, F., Moradi, M. H., Doust, M. T., & Abootalebi, V. (2009). Classification of sustained attention level based

on morphological features of eeg’s independent components. 2009 ICME International Conference on Complex

Medical Engineering, 1–6. https://doi.org/https://api.semanticscholar.org/CorpusID:20999304

Huang, H., Li, R., & Zhang, J. (2023). A review of visual sustained attention: Neural mechanisms and computational

models. PeerJ, 11, e15351. https://doi.org/https://doi.org/10.7717/peerj.15351

Jebelli, H., Hwang, S., & Lee, S. (2018). Eeg-based workers’ stress recognition at construction sites. Automation in

Construction, 93, 315–324. https://doi.org/https://doi.org/10.1016/j.autcon.2018.05.027

Jia, H., Yu, S., Yin, S., Liu, L., Yi, C., Xue, K., Li, F., Yao, D., Xu, P., & Zhang, T. (2023). A model combining

multi branch spectral-temporal cnn, efficient channel attention, and lightgbm for mi-bci classification. IEEE

Transactions on Neural Systems and Rehabilitation Engineering, 31, 1311–1320. https:// doi.org/ https:


Jie, X., Cao, R., & Li, L. (2014). Emotion recognition based on the sample entropy of eeg. Bio-medical materials and

engineering, 24 (1), 1185–1192. https://doi.org/https://doi.org/10.3233/BME-130919

Jing, D., Liu, D., Zhang, S., & Guo, Z. (2020). Fatigue driving detection method based on eeg analysis in low-voltage

and hypoxia plateau environment. International journal of transportation science and technology, 9 (4), 366–376.


Ke, Y., Chen, L., Fu, L., Jia, Y., Li, P., Zhao, X., Qi, H., Zhou, P., Zhang, L., Wan, B., et al. (2014). Visual attention

recognition based on nonlinear dynamical parameters of eeg. Bio-medical materials and engineering, 24 (1),

–355. https://doi.org/https://doi.org/10.3233/bme-130817

Kirmizi-Alsan, E., Bayraktaroglu, Z., Gurvit, H., Keskin, Y. H., Emre, M., & Demiralp, T. (2006). Comparative

analysis of event-related potentials during go/nogo and cpt: Decomposition of electrophysiological markers

of response inhibition and sustained attention. Brain research, 1104 (1), 114–128. https://doi.org/https:


Ko, L.-W., Komarov, O., Hairston, W. D., Jung, T.-P., & Lin, C.-T. (2017). Sustained attention in real classroom

settings: An eeg study. Frontiers in human neuroscience, 11, 388. https://doi.org/https://doi.org/10.3389/


Lichstein, K. L., Riedel, B. W., & Richman, S. L. (2000). The mackworth clock test: A computerized version. The

Journal of psychology, 134 (2), 153–161. https://doi.org/https://doi.org/10.1080/00223980009600858

Liu, N.-H., Chiang, C.-Y., & Chu, H.-C. (2013). Recognizing the degree of human attention using eeg signals from

mobile sensors. sensors, 13 (8), 10273–10286. https://doi.org/https://doi.org/10.3390/s130810273

Peng, C.-J., Chen, Y.-C., Chen, C.-C., Chen, S.-J., Cagneau, B., & Chassagne, L. (2020). An eeg-based attentiveness

recognition system using hilbert–huang transform and support vector machine. Journal of Medical and Biological

Engineering, 40 (2), 230–238. https://doi.org/https://doi.org/10.1007/s40846-019-00500-y

Pincus, S. M., & Goldberger, A. L. (1994). Physiological time-series analysis: What does regularity quantify? American

Journal of Physiology-Heart and Circulatory Physiology, 266 (4), H1643–H1656. https: / / doi. org / https:


Poon, C. C., Lo, B. P., Yuce, M. R., Alomainy, A., & Hao, Y. (2015). Body sensor networks: In the era of big data and

beyond. IEEE reviews in biomedical engineering, 8, 4–16. https://doi.org/https://doi.org/10.1109/RBME.2015.

Song, Y., Crowcroft, J., & Zhang, J. (2012). Automatic epileptic seizure detection in eegs based on optimized sample

entropy and extreme learning machine. Journal of neuroscience methods, 210 (2), 132–146. https://doi.org/https:


Suhail, T. A., Indiradevi, K. P., Suhara, E. M., Suresh, P. A., & Anitha, A. (2021). Electroencephalography based

detection of cognitive state during learning tasks: An extensive approach. Cognition, Brain, Behavior, 25 (2).


Sui, L., Zhao, X., Zhao, Q., Tanaka, T., & Cao, J. (2019). Localization of epileptic foci by using convolutional

neural network based on ieeg. Artificial Intelligence Applications and Innovations: 15th IFIP WG 12.5

International Conference, AIAI 2019, Hersonissos, Crete, Greece, May 24–26, 2019, Proceedings 15, 331–339.


Tuncer, T., Dogan, S., Ertam, F., & Subasi, A. (2021). A dynamic center and multi threshold point based stable feature

extraction network for driver fatigue detection utilizing eeg signals. Cognitive neurodynamics, 15 (2), 223–237.


Wang, D., Kobayashi, T., Cui, G., Watabe, D., & Cao, J. (2016). Bci-based mobile phone using ssvep techniques.

Advances in Cognitive Neurodynamics (V) Proceedings of the Fifth International Conference on Cognitive

Neurodynamics-2015, 379–382. https://doi.org/https://doi.org/10.1007/978-981-10-0207-6_52

Zhang, Y., Guo, H., Zhou, Y., Xu, C., & Liao, Y. (2023). Recognising drivers’ mental fatigue based on eeg multi-

dimensional feature selection and fusion. Biomedical Signal Processing and Control, 79, 104237. https://doi.


Zhao, Q., Onishi, A., Zhang, Y., Cao, J., Zhang, L., & Cichocki, A. (2011). A novel oddball paradigm for affective

bcis using emotional faces as stimuli. Neural Information Processing: 18th International Conference, ICONIP

, Shanghai, China, November 13-17, 2011, Proceedings, Part I 18, 279–286. https://doi.org/https:


Zhao, X., Tanaka, T., Kong, W., Zhao, Q., Cao, J., Sugano, H., & Yoshiday, N. (2018). Epileptic focus localization

based on ieeg by using positive unlabeled (pu) learning. 2018 Asia-Pacific Signal and Information Processing

Association Annual Summit and Conference (APSIPA ASC), 493–497. https://doi.org/https://doi.org/10.





How to Cite

Zhang, R., Shen, C., Sui, L., & Cao, J. (2024). A multidimensional EEG feature extraction attention detection for BCI System. INTERNATIONAL JOURNAL OF COMPUTERS &Amp; TECHNOLOGY, 24, 71–83. https://doi.org/10.24297/ijct.v24i.9638



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