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Author: search.filters.author.O'Reilly, Jamie A.Subject: search.filters.subject.recurrent neural network

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    Evaluating Recurrent Neural Network Blind Source Separation of Event-Related Potentials Using Simulated Data
    (2026-01-01)
    O'Reilly, Jamie A.
    Event-related potential (ERP) waveforms reflect spatiotemporal summation of potential differences resulting from the activity of current sources within the brain. Separating ERPs into underlying source waveforms and their scalp distributions is a potentially valuable for analyzing neurophysiology associated with psychophysiological events. However, ground-truth sources are unknown for real ERP data. This study aimed to evaluate recurrent neural network blind source separation (RNN-BSS) of ERP waveforms using simulated ERP data with known groundtruth source waveforms and scalp distributions. Simulated ERP waveforms were generated from seven simulated source waveforms and scalp distributions. Two simulations with positiveand negative-going source waveforms were evaluated to explore the effect of RNN source signal rectification on source representations. The source waveforms and scalp distributions extracted from applying RNN-BSS to simulated data were compared with ground-truth using Pearson's correlation coefficient. Source waveforms and scalp distributions extracted by RNN-BSS were highly correlated with their ground-truth counterparts. However, where scalp distributions of ground-truth sources were highly correlated, they were not separated perfectly by RNN-BSS. Negative-going simulations produced inverted scalp distributions. Overall, these results demonstrate efficacy of RNNBSS applied to simulated ERP waveforms with seven sources. Further evaluations are required to determine the limits of source separation when scalp distributions are highly correlated, and the influence of number of electrodes in RNN-BSS.
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    Localized estimation of electromagnetic sources underlying event-related fields using recurrent neural networks
    (2023-08-01)
    O'Reilly, Jamie A.
    ;
    Zhu, Judy D.
    ;
    Sowman, Paul F.
    Objective. To use a recurrent neural network (RNN) to reconstruct neural activity responsible for generating noninvasively measured electromagnetic signals. Approach. Output weights of an RNN were fixed as the lead field matrix from volumetric source space computed using the boundary element method with co-registered structural magnetic resonance images and magnetoencephalography (MEG). Initially, the network was trained to minimise mean-squared-error loss between its outputs and MEG signals, causing activations in the penultimate layer to converge towards putative neural source activations. Subsequently, L1 regularisation was applied to the final hidden layer, and the model was fine-tuned, causing it to favour more focused activations. Estimated source signals were then obtained from the outputs of the last hidden layer. We developed and validated this approach with simulations before applying it to real MEG data, comparing performance with beamformers, minimum-norm estimate, and mixed-norm estimate source reconstruction methods. Main results. The proposed RNN method had higher output signal-to-noise ratios and comparable correlation and error between estimated and simulated sources. Reconstructed MEG signals were also equal or superior to the other methods regarding their similarity to ground-truth. When applied to MEG data recorded during an auditory roving oddball experiment, source signals estimated with the RNN were generally biophysically plausible and consistent with expectations from the literature. Significance. This work builds on recent developments of RNNs for modelling event-related neural responses by incorporating biophysical constraints from the forward model, thus taking a significant step towards greater biological realism and introducing the possibility of exploring how input manipulations may influence localised neural activity.