Inter-individual single-trial classification of MEG data using M-CCA

Michalke, Leo; Dreyer, Alexander M.; Borst, Jelmer P.; Rieger, Jochem W.

doi:10.1016/j.neuroimage.2023.120079

Cited by 1 publication

(2 citation statements)

References 38 publications

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“…Similar methods have been explored in recent studies aiming to deal with between-subject variability (Michalke et al, 2023;Ravishankar et al, 2021;Zhou et al, 2020). However, to our knowledge, no prior work has applied it successfully to MEG decoding.…”

Section: Discussionmentioning

confidence: 99%

“…For example, Benz ( 2020 ) used hyperalignment on MEG data via procrustes matrix transformation to a common sensor space and showed improvement in evoked fields. Similar methods have been explored in recent studies aiming to deal with between‐subject variability (Michalke et al, 2023 ; Ravishankar et al, 2021 ; Zhou et al, 2020 ). However, to our knowledge, no prior work has applied it successfully to MEG decoding.…”

Section: Discussionmentioning

confidence: 99%

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Group‐level brain decoding with deep learning

Csaky,

van Es,

Jones

et al. 2023

Human Brain Mapping

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Decoding brain imaging data are gaining popularity, with applications in brain‐computer interfaces and the study of neural representations. Decoding is typically subject‐specific and does not generalise well over subjects, due to high amounts of between subject variability. Techniques that overcome this will not only provide richer neuroscientific insights but also make it possible for group‐level models to outperform subject‐specific models. Here, we propose a method that uses subject embedding, analogous to word embedding in natural language processing, to learn and exploit the structure in between‐subject variability as part of a decoding model, our adaptation of the WaveNet architecture for classification. We apply this to magnetoencephalography data, where 15 subjects viewed 118 different images, with 30 examples per image; to classify images using the entire 1 s window following image presentation. We show that the combination of deep learning and subject embedding is crucial to closing the performance gap between subject‐ and group‐level decoding models. Importantly, group models outperform subject models on low‐accuracy subjects (although slightly impair high‐accuracy subjects) and can be helpful for initialising subject models. While we have not generally found group‐level models to perform better than subject‐level models, the performance of group modelling is expected to be even higher with bigger datasets. In order to provide physiological interpretation at the group level, we make use of permutation feature importance. This provides insights into the spatiotemporal and spectral information encoded in the models. All code is available on GitHub (https://github.com/ricsinaruto/MEG-group-decode).

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Section: Discussionmentioning

confidence: 99%