Refining Human-Data Interaction: Advanced Techniques for EEGEyeNet Dataset Precision

• 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)

  Article  Google Scholar 

• 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)

  Google Scholar 

• 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)

• Boukerche, A., Zheng, L., Alfandi, O.: Outlier detection: methods, models, and classification. ACM Comput. Surv. (CSUR) 53(3), 1–37 (2020)

  Article  Google Scholar 

• Boukerche, A., Zheng, L., Alfandi, O.: Outlier detection: methods, models, and classification. ACM Comput. Surv. (CSUR) 53(3), 1–37 (2020). https://doi.org/10.1145/3381028. ISSN 0360-0300

  Article  Google Scholar 

• 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)

  Google Scholar 

• Chen, P., et al.: Hytrel: hypergraph-enhanced tabular data representation learning. In: Advances in Neural Information Processing Systems, vol. 36 (2024)

  Google Scholar 

• Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

• 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)

  Google Scholar 

• 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)

  Google Scholar 

• 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)

  Google Scholar 

• 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)

  Article  Google Scholar 

• Ingolfsson, T.M., et al.: 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). IEEE (2020)

  Google Scholar 

• Jiang, C., Hui, B., Liu, B., Yan, D.: Successfully applying lottery ticket hypothesis to diffusion model. arXiv preprint arXiv:2310.18823 (2023)

• Kastrati, A., Plomecka, M.B., Küchler, J., Langer, N., Wattenhofer, R.: Electrode clustering and bandpass analysis of eeg data for gaze estimation. In: Annual Conference on Neural Information Processing Systems, pp. 50–65. PMLR (2023)

  Google Scholar 

• Kastrati, A., et al.: Eegeyenet: a simultaneous electroencephalography and eye-tracking dataset and benchmark for eye movement prediction. arXiv preprint arXiv:2111.05100 (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., Shen, M., Wang, H., Wang, X., van Rechem, C., Wei, W.: 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)

  Article  Google Scholar 

• Ma, X.: Traffic performance evaluation using statistical and machine learning methods. PhD thesis, The University of Arizona (2022)

  Google Scholar 

• 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)

  Google Scholar 

• Mishra, A.R., et al.: Signeeg v1. 0: Multimodal electroencephalography and signature database for biometric systems. bioRxiv, pp. 2023–09 (2023)

  Google Scholar 

• 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)

  Google Scholar 

• Modesitt, E., Huang Wang, H., Yin, H., Lu, B.: Fusing pretrained vits with tcnet for enhanced eeg regression (2024)

  Google Scholar 

• 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)

  Google Scholar 

• 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)

  Google Scholar 

• Nakano, Y.I., Ishii, R.: Estimating user’s engagement from eye-gaze behaviors in human-agent conversations. In: Proceedings of the 15th International Conference on Intelligent User Interfaces, IUI 2010, pp. 139-148. Association for Computing Machinery, New York (2010). ISBN 9781605585154. https://doi.org/10.1145/1719970.1719990

• Pedroni, A., Bahreini, A., Langer, N.: Automagic: standardized preprocessing of big eeg data. Neuroimage 200, pp. 460–473 (2019). https://doi.org/10.1016/j.neuroimage.2019.06.046

• Pion-Tonachini, L., Kreutz-Delgado, K., Makeig, S.: Iclabel: an automated electroencephalographic independent component classifier, dataset, and website. Neuroimage 198, 181–197 (2019)

  Article  Google Scholar 

• 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

  Chapter  Google Scholar 

• 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

  Chapter  Google Scholar 

• 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)

  Google Scholar 

• Skoglund, M.A., Andersen, M., Shiell, M.M., Keidser, G., Rank, M.L., Rotger-Griful, S.: Comparing in-ear eog for eye-movement estimation with eye-tracking: accuracy, calibration, and speech comprehension. Front. Neurosci. 16, 873201 (2022)

  Google Scholar 

• Tan, J., Zhang, X., Shenghui, W., Song, Z., Chen, S., Huang, Y., Wang, Y.: Audio-induced medial prefrontal cortical dynamics enhances coadaptive learning in brain-machine interfaces. J. Neural Eng. 20(5), 056035 (2023)

  Article  Google Scholar 

• 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)

  Google Scholar 

• 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)

  Article  Google Scholar 

• 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)

  Article  Google Scholar 

• Wang, X., Wang, Z.: Cnn with self-attention in eeg classification. In: International Conference on Human-Computer Interaction, pp. 512–526. Springer, Heidelberg (2022)

  Google Scholar 

• 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 (2022)

  Google Scholar 

• Yang, R., Modesitt, E.: Vit2eeg: leveraging hybrid pretrained vision transformers for eeg data. arXiv preprint arXiv:2308.00454 (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)

  Google Scholar 

• 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)

  Google Scholar 

• 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)

  Article  Google Scholar 

• 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)

  Google Scholar 

• 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)

  Google Scholar 

• Zhao, S., et al.: Deep learning based cetsa feature prediction cross multiple cell lines with latent space representation. Sci. Rep. 14(1), 1878 (2024)

  Google Scholar 

• Zhao, Z., et al.: Le-uda: label-efficient unsupervised domain adaptation for medical image segmentation. IEEE Trans. Med. Imaging 42(3), 633–646 (2022b)

  Google Scholar 

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