An EEG & eye-tracking dataset of ALS patients & healthy people during eye-tracking-based spelling system usage
Tóm tắt
Từ khóa
Tài liệu tham khảo
Brownlee, A. & Bruening, L. M. Methods of communication at end of life for the person with amyotrophic lateral sclerosis. Top.Lang. Disord. 32(2), 168–185 (2012).
Chaudhary, U., Birbaumer, N. & Ramos-Murguialday, A. Brain-computer interfaces for communication and rehabilitation. Nat. Rev. Neurol. 12, 513–525 (2016).
Bauer, G., Gerstenbrand, F. & Rumpl, E. Varieties of the locked-in syndrome. J. Neurol. 221, 77–91 (1979).
Kübler, A. & Birbaumer, N. Brain-computer interfaces and communication in paralysis: Extinction of goal directed thinking in completely paralyzed patients? Clin. Neurophysiol. 119, 2658–2666 (2008).
Calvo, A. et al. Eye Tracking Impact on Quality-of-Life of ALS Patients. 11th International Conference on Computers Helping People with Special Needs, Linz (AT). 5105, 70–77, https://doi.org/10.1007/978-3-540-70540-6_9 (2008).
Beukelman, D., Fager, S. & Nordness, A. Communication Support for People with ALS. Neur. Res. Int 04, 714693 (2011).
Raupp S. Keyboard layout in eye gaze communication access: typical vs. ALS (Doctoral Dissertation, East Carolina University). 2013 January.
Yang, S., Lin, C., Lin, S. & Lee, C. Design of virtual keyboard using blink control method for the severely disabled. Computer methods and programs in biomedicine. 111(2), 410–418 (2013).
Yang, S., Lin, C., Lin, S. & Lee, C. Text Entry by Gaze: Utilizing Eye-Tracking. Text Entry Systems: Mobility, Accessibility, Universality. Chapter 9: p. 175-187 (2007).
Ghosh, S., Sarcar, S., Sharma, M. & Samanta, D. Effective virtual keyboard design with size and space adaptation. 2010 IEEE Students Technology Symposium (TechSym). (2010).
Nguyen, M. H. et al. On-screen keyboard controlled by gaze for Vietnamese people with amyotrophic lateral sclerosis. Technology and Disability 35(no. 1), 53–65, https://doi.org/10.3233/TAD-220391 (2023).
Mehta, P. et al. Prevalence of amyotrophic lateral sclerosis: United States. MMWR Morb Mortal Wkly Rep 2018; 67:1285–1289.
Ngo, T. D. et al. An EEG & eye-tracking dataset of ALS patients & healthy people during eye-tracking-based spelling system usage. figshare https://doi.org/10.6084/m9.figshare.c.6910027.v1 (2024).
Gorges, M. et al. Eye movement defcits are consistent with a staging model of pTDP-43 pathology in Amyotrophic Lateral Sclerosis. PLoS One 10(11), e0142546 (2015).
Jaramillo-Gonzalez. et al. A dataset of EEG and EOG from an auditory EOG-based communication system for patients in locked-in state. Scientific Data. 8. https://doi.org/10.1038/s41597-020-00789-4. 2021.
Kaya, M. et al. A large electroencephalographic motor imagery dataset for electroencephalographic brain computer interfaces. Scientific Data. 5, 180211, https://doi.org/10.1038/sdata.2018.211 (2018).
Ma, J. et al. A large EEG dataset for studying cross-session variability in motor imagery brain-computer interface. Scientific Data. 9. https://doi.org/10.1038/s41597-022-01647-1. 2022.
BNCI Horizon 2020 http://bnci-horizon-2020.eu/database (2020).
PhysioNet: Te research resource for complex physiological signals https://physionet.org/about/database/ (2020).
BrainSignals: Publicly available brain signals EEG MEG ECoG data http://www.brainsignals.de/ (2020).
Cedarbaum, J. M. et al. The ALSFRS-R: a revised ALS functional rating scale that incorporates assessments of respiratory function. J. Neurol. Sci. 169, 1–2 (1999).
Emotiv, 2022. [Online]. Available: https://www.emotiv.com/epoc-flex/. [Accessed 1 November 2023].
Jasper, H. The Ten-Twenty Electrode System of the International Federation. Electroencephalography and Clinical Neurophysiology 10, 371–375 (1958).
Tobii, “Tobii Eye Tracker 5,” Tobii, [Online]. Available: https://gaming.tobii.com/product/eye-tracker-5/. [Accessed 1 November 2023].
MNE package [Online]. Available: https://mne.tools/stable/index.html. [Accessed 1 November 2023].
Ramoser, H., Johannes, M. G. & Gert, P. Optimal spatial filtering of single trial EEG during imagined hand movement. IEEE transactions on rehabilitation engineering 8.4, 441–446 (2000).
Jayarathne, I., Michael, C. & Senaka, A. Person identification from EEG using various machine learning techniques with inter-hemispheric amplitude ratio. PLoS One 15.9, e0238872 (2020).
He, H. & Dongrui, W. Transfer learning for brain–computer interfaces: A Euclidean space data alignment approach. IEEE Transactions on Biomedical Engineering 67.2, 399–410 (2019).
Salami, A., Andreu-Perez, J. & Gillmeister, H. EEG-ITNet: An explainable inception temporal convolutional network for motor imagery classification. IEEE Access 10, 36672–36685 (2022).
Lawhern, V. J. et al. EEGNet: a compact convolutional neural network for EEG-based brain–computer interfaces. Journal of neural engineering 15(5), 056013 (2018).
Sun, Y., Lo, F. P.-W. & Lo, B. EEG-based user identification system using 1D-convolutional long short-term memory neural networks. Expert Systems with Applications 125, 259–267 (2019).