For nearly four decades, the N400 has been an important brainwave marker of semantic processing. It can be recorded non-invasively from the scalp using electrical and/or magnetic sensors, but largely within the restricted domain of research laboratories specialized to run specific N400 experiments. However, there is increasing evidence of significant clinical utility for the N400 in neurological evaluation, particularly at the individual level. To enable clinical applications, we recently reported a rapid evaluation framework known as “brain vital signs” that successfully incorporated the N400 response as one of the core components for cognitive function evaluation. The current study characterized the rapidly evoked N400 response to demonstrate that it shares consistent features with traditional N400 responses acquired in research laboratory settings—thereby enabling its translation into brain vital signs applications. Data were collected from 17 healthy individuals using magnetoencephalography (MEG) and electroencephalography (EEG), with analysis of sensor-level effects as well as evaluation of brain sources. Individual-level N400 responses were classified using machine learning to determine the percentage of participants in whom the response was successfully detected. The N400 response was observed in both M/EEG modalities showing significant differences to incongruent versus congruent condition in the expected time range (p < 0.05). Also as expected, N400-related brain activity was observed in the temporal and inferior frontal cortical regions, with typical left-hemispheric asymmetry. Classification robustly confirmed the N400 effect at the individual level with high accuracy (89%), sensitivity (0.88) and specificity (0.90). The brain vital sign N400 characteristics were highly consistent with features of the previously reported N400 responses acquired using traditional laboratory-based experiments. These results provide important evidence supporting clinical translation of the rapidly acquired N400 response as a potential tool for assessments of higher cognitive functions.

Blink-related oscillations (BROs) are a recently discovered neurophysiological response associated with spontaneous blinking, distinct from the well-known oculomotor and visual suppression effects. BROs strongly activate the bilateral precuneus along with other cortical regions involved in visuospatial processing and associative episodic memory, and are believed to represent environmental monitoring processes that occur following blink-induced visual interruptions. Although these responses have been reported across multiple imaging modalities under both resting and cognitive loading conditions, it is yet unknown whether these responses also exist under external sensory stimulation conditions. To address this, we investigated BRO responses in healthy adults using 64-channel electroencephalography (EEG), while participants underwent passive external auditory and visual stimulation. Our results showed that BRO responses are present under both auditory and visual stimulation conditions (p ​< ​0.05), with similar temporal and spectral features compared to rest. However, visual stimulation did result in decreased BRO amplitude compared to auditory and resting conditions (p ​< ​0.05), suggesting decreased neuronal resources for processing blink-related information in the visual but not auditory environment. There were also additional pre-blink spectral changes in the visual condition compared to rest (p ​< ​0.05), which suggest that passive visual stimulation induces neural preparatory processes occurring in anticipation of the upcoming blink event. Together, these findings provide new and compelling evidence that blink-related neural processes are modulated not only by the internal cognitive loading due to simultaneous task demands, but also by competing external sensory requirements. This highlights the link between blinking and cognition, and further demonstrates the importance of BROs as a new window into brain function.

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 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 exhibits 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 4 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 4-electrode array. Significance: This significantly advances the translation of the blink-related oscillations marker to a point-of-care platform for eventual bedside applications.

Translating EEG technology into real world applications is limited by a variety of challenges, one of which is signal noise from portable, low-density EEG devices operating in uncontrolled environments. We have developed an automated general-purpose framework, the EEG Quality Index, for the objective assessment of EEG signal quality. This framework currently uses 6 metrics that are shown to be highly sensitive to various typical artifacts. Normalized scores for each metric can be used in a framework to provide indications of the overall quality of the recorded EEG signal. Using simulated clean and artifact-contaminated EEG in a Support Vector Machine classifier, the EEG Quality Index metrics can identify artifact-contaminated EEG with 90.75% accuracy, 97.7% sensitivity, and 83.8% specificity. This is the first demonstration of a general-purpose framework for the automated and objective assessment of the quality of EEG data. With portable, low-density EEG systems being increasingly adopted in real-world clinical applications, this type of quality assessment is important.