Abstract
Long-lasting periodic sensory stimulation is increasingly used in neuroscience to study, using electroencephalography (EEG), the cortical processes underlying perception in different modalities. This kind of stimulation can elicit synchronized periodic activity at the stimulation frequency in neuronal populations responding to the stimulus, referred to as a steady-state response (SSR). While the frequency analysis of EEG recordings is particularly well suited to capture this activity, it is limited by the intrinsic noisy nature of EEG signals and the low signal-to-noise ratio (SNR) of some responses. This paper compares and adapts spatial filtering methods for periodicity maximization to enhance the SNR of periodic EEG responses, a key condition to generalize their use as a research or clinical tool. This approach uncovers both temporal dynamics and spatial topographic patterns of SSRs, and is validated using EEG data from 15 healthy subjects exposed to periodic cool and warm stimuli.
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Acknowledgments
DM and CdB are Research Fellows of the Fonds de la Recherche Scientifique - FNRS. The authors gratefully thank Prof. Christian Jutten for insightful discussions.
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Mulders, D., de Bodt, C., Lejeune, N., Mouraux, A., Verleysen, M. (2018). Spatial Filtering of EEG Signals to Identify Periodic Brain Activity Patterns. In: Deville, Y., Gannot, S., Mason, R., Plumbley, M., Ward, D. (eds) Latent Variable Analysis and Signal Separation. LVA/ICA 2018. Lecture Notes in Computer Science(), vol 10891. Springer, Cham. https://doi.org/10.1007/978-3-319-93764-9_48
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DOI: https://doi.org/10.1007/978-3-319-93764-9_48
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