The prediction of subject traits using brain data is an important goal in neuroscience, with relevant applications in clinical research, as well as in the study of differential psychology and cognition. While previous research has primarily focused on neuroimaging data, our focus is on the prediction of subject traits from electroencephalography (EEG), a relatively inexpensive, widely available and non-invasive data modality. However, EEG data is complex and needs some form of feature extraction for subsequent prediction. This process is almost always done manually, risking biases and suboptimal decisions. Here, we propose a largely data-driven use of the EEG spectrogram, which reflects macro-scale neural oscillations in the brain. Specifically, the key idea is to use the full spectrogram, reinterpret it as a probability distribution and then leverage advanced machine learning techniques that can handle probability distributions with mathematical rigour and without the need for manual feature extraction [1,2,3]. The resulting techniques Kernel Ride Regression (KRR) and Kernel Mean Embedding Regression (KMER), show superior performance to alternative methods thanks to their capacity to handle nonlinearities in the relation between the EEG spectrogram and the trait of interest. We leveraged this method to predict biological age in a multinational EEG data set, HarMNqEEG [4], showing the method's capacity to generalise across experiments and acquisition setups.
Acknowledgements:
D. Vidaurre is supported by a Novo Nordisk Foundation Emerging Investigator Fellowship (NNF19OC-0054895), an ERC Starting Grant (ERC-StG-2019-850404), and a DFF Project 1 from the Independent Research Fund of Denmark (2034-00054B). This research was funded in part by the Wellcome Trust (215573/Z/19/Z).fro We acknowledge support from PICT 2020-01413.
References:[1] Franke, K. and Gaser, C. (2019). Ten years of brain age as a neuroimaging biomarker of brain ageing: What insights have we gained? Frontiers in Neurology, 10(JUL).
[2] Smith, S. M., Vidaurre, D., Alfaro-Almagro, F., Nichols, T. E., and Miller, K. L. (2019). Estimation of brain age delta from brain imaging. NeuroImage, 200:528–539
[3] Smola, A., Gretton, A., Song, L., and Schölkopf, B. (2007). A Hilbert space embedding for distributions. In Hutter, M., Servedio, R. A., and Takimoto, E., editors, Algorithmic Learning Theory, pages 13–31, Berlin, Heidelberg. Springer Berlin Heidelberg.
[4] Li, M., Wang, Y., Lopez-Naranjo, C., Hu, S., Reyes, R. C. G., Paz-Linares, D., Areces-Gonzalez, A., Hamid, A. I. A., Evans, A. C., Savostyanov, A. N., Calzada-Reyes, A., Villringer, A., Tobon-Quintero, C. A., Garcia-Agustin, D., Yao, D., Dong, L., Aubert-Vazquez, E., Reza, F., Razzaq, F. A., Omar, H., Abdullah, J. M., Galler, J. R., Ochoa-Gomez, J. F., Prichep, L. S., Galan-Garcia, L., Morales-Chacon, L., Valdes-Sosa, M. J., Tröndle, M. Zulkifly, M. F. M., Abdul Rahman, M. R. B., Milakhina, N. S., Langer, N., Rudych, P., Koenig, T., Virues-Alba, T. A., Lei, X., Bringas-Vega, M. L., Bosch-Bayard, J. F., and Valdes-Sosa, P. A. (2022). Harmonized-multinational qeeg norms (harmnqeeg). NeuroImage, 256:119190.
