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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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    Neural Generators of Intensity Mismatch Negativity Modelled with a Recurrent Neural Network : A Pilot Study on the Role of Sound Level Transitions
    (2023-01-01)
    Srivastava, Chandan K.
    ;
    Gupta, Rashmi
    ;
    O'Reilly, Jamie A.
    Intensity mismatch negativity (MMN) is an electrophysiological response to auditory oddball stimulation with unexpected changes in sound pressure level. Studies using only quieter deviant stimuli leave uncertainty regarding the nature of resulting MMN, with louder deviant stimuli potentially evoking an opposite polarity amplitude shift due to intensity modulation of the auditory evoked response. To test this hypothesis, we conducted a pilot study with three subjects listening to an intensity oddball paradigm with 80 dB standards and 70 dB and 90 dB deviants. Event-related potentials and deviant-minus-standard difference waveforms were analyzed to determine whether MMN elicited by 70 dB and 90 dB reflects simple intensity modulation. We modelled the resulting neural activity with a recurrent neural network (RNN) to estimate generative signals from a distributed source space computed with a template head co-registered with standard 10-20 system electrode locations. Event-related potential waveforms suggest that intensity MMN elicited by quieter and louder auditory stimuli cannot be explained simply by intensity modulation of the auditory evoked response, given that both produced negative amplitudes from 0.1 to 0.25 s. Results from RNN-based source estimation are consistent with bilateral thalamic and right-prefrontal contributions to intensity MMN evoked by rising and falling sound level transitions.