Abstract
Background: Electroencephalography (EEG)-derived event-related potentials (ERPs) provide information about a variety of brain functions, but often suffer from low inherent signal-to-noise ratio (SNR). To overcome the low SNR, techniques that pool data from multiple sensors have been applied. However, such pooling implicitly assumes that the SNR among sensors is equal, which is not necessarily valid. This study presents a novel approach for signal pooling that accounts for differential SNR among sensors. Methods: The new technique involves pooling together signals from multiple EEG channels weighted by their respective SNRs relative to the overall SNR of all channels. We compared ERP responses derived using this new technique with those derived using both individual channels as well as traditional averaged-based channel pooling. The outcomes were evaluated in both simulated data and real data from healthy adult volunteers (n = 37). Responses corresponding to a range of ERP components indexing auditory sensation (N100), attention (P300) and language processing (N400) were evaluated. Results: Simulation results demonstrate that, compared to traditional pooling technique, the new SNR-weighted channel pooling technique improved ERP response effect size in cases of unequal noise among channels (p’s < 0.001). Similarly, results from real-world experimental data showed that the new technique resulted in significantly greater ERP effect sizes compared to either traditional pooling or individual channel approach for all three ERP components (p’s < 0.001). Furthermore, the new channel pooling approach also resulted in larger ERP signal amplitudes as well as greater differences among experimental conditions (p’s < 0.001). Conclusion: These results suggest that the new technique improves the capture of ERP responses relative to traditional techniques. As such, SNR-weighted channel pooling can further enable widespread applications of ERP techniques, especially those that require rapid assessments in noisy out-of-laboratory environments.
Highlights
In recent years, advances in portable electroencephalography (EEG) technology have increasingly enabled the development of point-of-care, objective, physiology-based measurements of brain function, such as the brain vital sign monitoring [1,2,3]
The improvement in signal capture provided by the dynamic SNR-weighted (dSNRw) technique over the traditional pooling approach was significant when the channels being combined had varying noise levels (p < 0.001), but no significant differences were observed when the same level of noise was present in the channels being pooled
The effect size measures for the equal noise scenario were highly correlated across the two Effect size measurements on simulation data showed that the dSNRw combinatorial technique better captured the difference between the event-related potentials (ERPs) waveform pairs compared to traditional channel pooling (Figure 3)
Summary
Advances in portable electroencephalography (EEG) technology have increasingly enabled the development of point-of-care, objective, physiology-based measurements of brain function, such as the brain vital sign monitoring [1,2,3]. Event-related potentials (ERPs) extracted from EEG can provide physiology-based measures to augment existing behaviour-based assessments of brain function, which had been shown to be 4.0/). Potentials (ERPs) extracted from EEG can provide physiology-based measures to augment existing behaviour-based assessments of brain function, which had been shown to be highly subjective and potentially error-prone [4,5,6]. Results: Simulation results demonstrate that, compared to traditional pooling technique, the new SNR-weighted channel pooling technique improved ERP response effect size in cases of unequal noise among channels (p’s < 0.001). Results from real-world experimental data showed that the new technique resulted in significantly greater ERP effect sizes compared to either traditional pooling or individual channel approach for all three ERP components (p’s < 0.001)
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