Abstract
Electroencephalography (EEG) analysis is an important domain in the realm of Brain-Computer Interface (BCI) research. To ensure BCI devices are capable of providing practical applications in the real world, brain signal processing techniques must be fast, accurate, and resource-conscious to deliver low-latency neural analytics. This study presents a model that leverages a pre-trained MobileViT alongside Knowledge Distillation (KD) for EEG regression tasks. Our results showcase that this model performs at a level comparable to the previous State-of-the-Art (SOTA) on the EEGEyeNet Absolute Position Task, achieving a Root Mean Squared Error (RMSE) of 53.6, a 3% reduction in accuracy, while being 33% faster and 60% smaller. Our research presents a cost-effective model applicable to resource-constrained devices and contributes to expanding future research on lightweight, mobile-friendly models for EEG regression.
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References
An, S., Bhat, G., Gumussoy, S., Ogras, U.: Transfer learning for human activity recognition using representational analysis of neural networks. ACM Trans. Comput. Healthc. 4(1), 1–21 (2023)
An, S., Tuncel, Y., Basaklar, T., Ogras, U.Y.: A survey of embedded machine learning for smart and sustainable healthcare applications. In:Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing: Use Cases and Emerging Challenges, pp. 127–150. Springer, Heidelberg (2023b)
Bai, S., Kolter, J.Z., Koltun, V.: An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271 (2018)
Cao, Z.: A review of artificial intelligence for eeg-based braincomputer interfaces and applications. Brain Sci. Adv. 6(3), 162–170 (2020). https://doi.org/10.26599/BSA.2020.9050017
Chen, P., Ding, H., Araki, J., Huang, R.: Explicitly capturing relations between entity mentions via graph neural networks for domain-specific named entity recognition. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing, vol. 2: Short Papers, pp. 735–742 (2021)
Chen, P., et al.: Hytrel: hypergraph-enhanced tabular data representation learning. In: Advances in Neural Information Processing Systems, vol. 36 (2024)
Craik, A., He, Y., Contreras-Vidal, J.L.: Deep learning for electroencephalogram (EEG) classification tasks: a review. J. Neural Eng. 16(3), 031001 (2019)
Cui, Y., et al.: Lightweight neural network with knowledge distillation for csi feedback (2024)
Dosovitskiy, A., et al.: An image is worth 16\(\,\times \,\)16 words: transformers for image recognition at scale (2021)
Dou, G., Zhou, Z., Qu, X.: Time majority voting, a pc-based eeg classifier for non-expert users. In: International Conference on Human-Computer Interaction, pp. 415–428. Springer, Heidelberg (2022)
EL Menshawy, M., Benharref, A., Serhani, M.: An automatic mobile-health based approach for eeg epileptic seizures detection. Expert Syst. Appl. 42(20), 7157–7174 (2015). ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2015.04.068. https://www.sciencedirect.com/science/article/pii/S0957417415003103
Farago, E., Law, A.J., Hajra, S.G., Chan, A.D.C.: Blink and saccade detection from forehead eeg. In: 2022 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), pp. 1–6. IEEE (2022)
Fuhl, W., et al.: One step closer to eeg based eye tracking. In: Proceedings of the 2023 Symposium on Eye Tracking Research and Applications, pp. 1–7 (2023)
Gao, D., Zhou, D.: A very lightweight and efficient image super-resolution network. Expert Syst. Appl. 213, 118898 (2023). ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2022.118898. https://www.sciencedirect.com/science/article/pii/S0957417422019169
Gui, S., Song, S., Qin, R., Tang, Y.: Remote sensing object detection in the deep learning era-a review. Remote Sens. 16(2), 327 (2024)
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network (2015)
Ingolfsson, T.M., Hersche, M., Wang, X., Kobayashi, N., Cavigelli, L., Benini, L.: Eeg-tcnet: an accurate temporal convolutional network for embedded motor-imagery brain-machine interfaces. In: 2020 IEEE International Conference on Systems, Man, and Cybernetics (SMC), pp. 2958–2965 (2020). https://doi.org/10.1109/SMC42975.2020.9283028
Jebelli, H., Khalili, M.M., Lee, S.H.: 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 ISBN 978-3-030-00220-6
Jiang, C., Hui, B., Liu, B., Yan, D.: Successfully applying lottery ticket hypothesis to diffusion model. arXiv preprint arXiv:2310.18823 (2023)
Kastrati, A., et al.: Eegeyenet: a simultaneous electroencephalography and eye-tracking dataset and benchmark for eye movement prediction (2021)
Li, H., et al.: Spherehead: stable 3d full-head synthesis with spherical tri-plane representation. arXiv preprint arXiv:2404.05680 (2024)
Lu, Y., Sato, K., Wang, J.: Deep learning based multi-label image classification of protest activities. arXiv preprint arXiv:2301.04212 (2023a)
Lu, Y., et al.: Machine learning for synthetic data generation: a review. arXiv preprint arXiv:2302.04062 (2023b)
Yingzhou, L., Chen, T., Hao, N., Van Rechem, C., Chen, J., Tianfan, F.: Uncertainty quantification and interpretability for clinical trial approval prediction. Health Data Sci. 4, 0126 (2024)
Ma, X.: Traffic performance evaluation using statistical and machine learning methods. PhD thesis, The University of Arizona (2022)
Ma, X., Karimpour, A., Wu, Y.J.: Data-driven transfer learning framework for estimating on-ramp and off-ramp traffic flows. J. Intell. Transport. Syst. 1–14 (2024)
Mehta, S., Rastegari, M.: Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer (2022a)
Mehta, S., Rastegari, M.: Separable self-attention for mobile vision transformers (2022b)
Mishra, A.R., et al.: Signeeg v1. 0: Multimodal electroencephalography and signature database for biometric systems. bioRxiv, pp. 2023–09 (2023)
Modesitt, E., Yang, R., Liu, Q.: Two heads are better than one: a bio-inspired method for improving classification on eeg-et data. In: International Conference on Human-Computer Interaction, pp. 382–390. Springer, Heidelberg (2023)
Modesitt, E., Yin, H., Wang, W.H., Lu, B.: Fusing pretrained vits with tcnet for enhanced eeg regression (2024)
Murungi, N.K., Pham, M.V., Dai, X., Qu, X.: Trends in machine learning and electroencephalogram (eeg): a review for undergraduate researchers. In: International Conference on Human-Computer Interaction, pp. 426–443. Springer, Heidelberg (2023a)
Murungi, N.K., Pham, M.V., Dai, X.C., Qu, X.: Empowering computer science students in electroencephalography (eeg) analysis: a review of machine learning algorithms for eeg datasets. In: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1728–1739 (2023b)
Qu, X., Liu, P., Li, Z., Hickey, T.: Multi-class time continuity voting for EEG classification. In: Frasson, C., Bamidis, P., Vlamos, P. (eds.) BFAL 2020. LNCS (LNAI), vol. 12462, pp. 24–33. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60735-7_3
Qu, X., Mei, Q., Liu, P., Hickey, T.: Using EEG to distinguish between writing and typing for the same cognitive task. In: Frasson, C., Bamidis, P., Vlamos, P. (eds.) BFAL 2020. LNCS (LNAI), vol. 12462, pp. 66–74. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60735-7_7
Rolff, T., Harms, H.M., Steinicke, F., Frintrop, S.: Gazetransformer: gaze forecasting for virtual reality using transformer networks. In: DAGM German Conference on Pattern Recognition, pp. 577–593. Springer, Heidelberg (2022)
Roy, Y., et al.: Deep learning-based electroencephalography analysis: a systematic review. J. Neural Eng. 16(5), 051001 (2019)
Sun, C., Mou, C.: Survey on the research direction of eeg-based signal processing. Front. Neurosci. (2023). ISSN 1662-453X. https://doi.org/10.3389/fnins.2023.1203059
Tan, J., et al.: Audio-induced medial prefrontal cortical dynamics enhances coadaptive learning in brain-machine interfaces. J. Neural Eng. 20(5), 056035 (2023)
Tan, J., Zhang, X., Wu, S., Wang, Y.: State-space model based inverse reinforcement learning for reward function estimation in brain-machine interfaces. In: 2023 45th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), pp. 1–4. IEEE (2023b)
Tang, Y., Song, S., Gui, S., Chao, W., Cheng, C., Qin, R.: Active and low-cost hyperspectral imaging for the spectral analysis of a low-light environment. Sensors 23(3), 1437 (2023)
Wadekar, S.N., Chaurasia, A.: Mobilevitv3: mobile-friendly vision transformer with simple and effective fusion of local, global and input features (2022)
Wang, J., Chang, R., Zhao, Z., Pahwa, R.S.: Robust detection, segmentation, and metrology of high bandwidth memory 3d scans using an improved semi-supervised deep learning approach. Sensors 23(12), 5470 (2023)
Wang, X., Wang, Z.: Cnn with self-attention in eeg classification. In: International Conference on Human-Computer Interaction, pp. 512–526. Springer, Heidelberg (2022)
Wolf, L., et al.: A deep learning approach for the segmentation of electroencephalography data in eye tracking applications. arXiv preprint arXiv:2206.08672 (2022)
Xiang, B., Abdelmonsef, A.: Vector-based data improves left-right eye-tracking classifier performance after a covariate distributional shift. In: International Conference on Human-Computer Interaction, pp. 617–632. Springer, Heidelberg (2022)
Xiao, T., Singh, M., Mintun, E., Darrell, T., Dollár, P., Girshick, R.: Early convolutions help transformers see better (2021)
Xu, X., Yang, L., Yan, Y., Li, C.: Cmfs-net: common mode features suppression network for gaze estimation. In: Proceedings of the 2023 Workshop on Advanced Multimedia Computing for Smart Manufacturing and Engineering, AMC-SME 2023, pp. 25–29. Association for Computing Machinery, New York (2023). ISBN 9798400702730. https://doi.org/10.1145/3606042.3616449
Yang, R., Modesitt, E.: Vit2eeg: leveraging hybrid pretrained vision transformers for eeg data (2023)
Yi, L., Qu, X.: Attention-based cnn capturing eeg recording’s average voltage and local change. In: Artificial Intelligence in HCI: 3rd International Conference, AI-HCI 2022, Held as Part of the 24th HCI International Conference, HCII 2022, Virtual Event, 26 June–1 July 2022, Proceedings, pp. 448–459. Springer, Heidelberg (2022)
Yunoki, I., Berreby, G., D’Andrea, N., Lu, Y., Qu, X.: Exploring ai music generation: a review of deep learning algorithms and datasets for undergraduate researchers. In: International Conference on Human-Computer Interaction, pp. 102–116. Springer, Heidelberg (2023)
Zhang, Z., Tian, R., Sherony, R., Domeyer, J., Ding, Z.: Attention-based interrelation modeling for explainable automated driving. IEEE Trans. Intell. Veh. 8(2), 1564–1573 (2022)
Zhang, Z., Tian, R., Ding, Z.: Trep: transformer-based evidential prediction for pedestrian intention with uncertainty. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 37, pp. 3534–3542 (2023)
Zhao, H., Du, H., Yang, S., Yao, F.: Rec-rn: user representations learning over the knowledge graph for recommendation systems. In: 2022 4th International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), pp. 228–233. IEEE (2022a)
Zhao, S., et al.: Deep learning based cetsa feature prediction cross multiple cell lines with latent space representation. Sci. Rep. 14(1), 1878 (2024)
Zhao, Z., Zhou, F., Xu, K., Zeng, Z., Guan, C., Zhou, S.K.: LE-UDA: label-efficient unsupervised domain adaptation for medical image segmentation. IEEE Trans. Med. Imaging 42(3), 633–646 (2022)
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Liang, T., Damoah, A. (2025). EEGMobile: Enhancing Speed and Accuracy in EEG-Based Gaze Prediction with Advanced Mobile Architectures. In: Kurosu, M., Hashizume, A., Mori, H., Asahi, Y., Schmorrow, D.D., Fidopiastis, C.M. (eds) HCI International 2024 – Late Breaking Papers. HCII 2024. Lecture Notes in Computer Science, vol 15374. Springer, Cham. https://doi.org/10.1007/978-3-031-76803-3_20
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