Abstract
Non-invasive Brain-Computer Interfaces (BCIs) using electroencephalography (EEG) recordings are the most common type of BCI. The detection of Event-Related Potentials (ERP) corresponding to the presentation of visual stimuli is one of the main paradigms in BCI, such as for the detection of the P300 ERP component that is used in the P300 speller. The typing speed and the information transfer rate in a BCI speller are directly related to the single-trial detection performance. It corresponds to the binary classification of brain evoked responses corresponding to the presentation of stimuli representing targets vs. non-targets. Many techniques have been proposed in the literature, ranging from shallow approaches using linear discriminant analysis to hierarchical and deep learning methods. For BCIs that require a calibration session, reducing its duration is critical for the implementation of BCIs in clinical settings. For this reason, data augmentation approaches allowing to increase the size of the training database can improve performance while keeping the same number of trials for the calibration session. In this paper, we propose to generate artificial trials based on the properties of the distribution of the signals after spatial filtering using the coloring transformation. The approach is compared with other approaches on the single-trial detection of ERPs from a public database of 8 subjects with amyotrophic lateral sclerosis. The results support the conclusion that artificial trials based on the coloring transformation can be used for training a classifier. However, they do not provide a substantial improvement then added as a data augmentation technique, compared to data augmentation using examples shifted temporally.
This study was supported by the NIH-R15 NS118581 project.
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References
Baird, H.: Document image defect models. In: Proceedings of the IAPR Workshop on Syntactic and Structural Pattern Recognition, pp. 38–46 (1990)
Cecotti, H., Marathe, A., Ries, A.: Optimization of single-trial detection of event-related potentials through artificial trials. IEEE Trans. Biomed. Eng. 62(9), 2170–2176 (2015)
Cecotti, H., et al.: A robust sensor selection method for P300 brain-computer interfaces. J. Neural Eng. 8, 016001 (2011)
Farwell, L.A., Donchin, E.: Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroencephalogr. Clin. Neurophysiol. 70(6), 510–523 (1988)
Hartmann, K.G., Schirrmeister, R.T., Ball, T.: EEG-GAN: Generative adversarial networks for electroencephalograhic (EEG) brain signals (2018)
Hoffmann, U., Vesin, J., Diserens, K., Ebrahimi, T.: An efficient P300-based brain-computer interface for disabled subjects. J. Neurosci. Meth. 167(1), 115–125 (2008)
Keysers, D., Deselaers, T., Gollan, C., Ney, H.: Deformation models for image recognition. IEEE Trans. Patt. Anal. Mach. Intell. 29(8), 1422–1435 (2007)
Kutas, M., McCarthy, G., Donchin, E.: Augmenting mental chronometry: the p300 as a measure of stimulus evaluation time. Science 197(4305), 792–795 (1977)
Lees, S., et al.: A review of rapid serial visual presentation-based brain–computer interfaces. J. Neural Eng. 15(2), 021001 (2018)
Luck, S.J., Kappenman, E.S.: The Oxford Handbook of Event-Related Potential Components. Oxford University Press, USA (2011)
Luo, Y., Cai, X., Zhang, Y., Xu, J., et al.: Multivariate time series imputation with generative adversarial networks. Adv. Neural Inf. Process. Syst. 31 (2018)
MacKay, D.J.C.: Bayesian interpolation. Neural Comput. 4(3), 415–447 (1992)
Polich, J., Kokb, A.: Cognitive and biological determinants of P300: an integrative review. Biol. Psychol. 41, 103–146 (1995)
Rivet, B., Cecotti, H., Maby, E., Mattout, J.: Impact of spatial filters during sensor selection in a visual p300 brain-computer interface. Brain Topogr. 25(1), 55–63 (2012)
Rivet, B., Souloumiac, A.: Optimal linear spatial filters for event-related potentials based on a spatio-temporal model: asymptotical performance analysis. Signal Process. 93(2), 387–398 (2013)
Rivet, B., Souloumiac, A., Attina, V., Gibert, G.: xDAWN algorithm to enhance evoked potentials: application to brain-computer interface. IEEE Trans. Biomed. Eng. 56(8), 2035–2043 (2009)
Schalk, G., McFarland, D.J., Hinterberger, T., Birbaumer, N., Wolpaw, J.R.: BCI 2000: a general-purpose brain-computer interface (bCI) system. IEEE Trans. Biomed. Eng. 51(6), 1034–1043 (2004)
Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: Proceedings of the 7th International Conference on Document Analysis and Recognition, pp. 958–962 (2003)
Yi, X., Walia, E., Babyn, P.: Generative adversarial network in medical imaging: a review. Med. Image Anal. 58, 101552 (2019)
Özdenizci, O., Erdoğmuş, D.: On the use of generative deep neural networks to synthesize artificial multichannel EEG signals. In: 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 427–430 (2021). https://doi.org/10.1109/NER49283.2021.9441381
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Cecotti, H., Jaimes, S. (2023). Single-Trial Detection of Event-Related Potentials with Artificial Examples Based on Coloring Transformation. In: Santosh, K., Goyal, A., Aouada, D., Makkar, A., Chiang, YY., Singh, S.K. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2022. Communications in Computer and Information Science, vol 1704. Springer, Cham. https://doi.org/10.1007/978-3-031-23599-3_28
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