Objective: Blink-related oscillations derived from electroencephalography (EEG) have recently emerged as an important measure of awareness. Combined with portable EEG hardware with low-density electrode arrays, this neural marker may crucially augment the existing bedside assessments of consciousness in unresponsive patients. Nonetheless, the close relationship between signal characteristics of the neural response of interest and blink-induced oculomotor artifacts poses particular challenges when measuring blink-related oscillations using a point-of-care platform. This study presents a novel denoising approach based on time-frequency (TF) filtering that exploits the differential temporal and spectral features to isolate the neural response from ocular artifact in a low-density array.
Methods: We investigated the effectiveness of the TF filtering technique using 64-channel EEG data collected in healthy adults, with focal analysis of the Pz and POz channels.
Results: TF filtering showed comparable performance in denoising the signal relative to the established gold-standard independent component analysis approach, with strong similarities in morphological characteristics as measured by intraclass correlations (p < 0.001), extent of artifact rejection based on the ocular contamination index (p < 0.006), as well as time- and frequency-domain signal capture (p < 0.05). Results are robust at the individual and group levels, and are crucially validated using raw data from only four electrodes comprising Pz, POz, Fp2, and T7.
Conclusion: These results demonstrate for the first time that TF filtering enables the successful capture and isolation of the blink-related oscillations response using a four-electrode array.
Significance: This significantly advances the translation of the blink-related oscillations marker to a point-of-care platform for eventual bedside applications.
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