Towards brain first-aid: a diagnostic device for conscious awareness

Distant Sensor Prediction of Event-Related Potentials

OBJECTIVE

The ability to measure event-related potentials (ERPs) as practical, portable brain vital signs is limited by the physical locations of electrodes. Standard electrode locations embedded within the hair result in challenges to obtaining quality signals in a rapid manner. Moreover, these sites require electrode gel, which can be inconvenient. As electrical activity in the brain is spatially volume distributed, it should be possible to predict ERPs from distant sensor locations at easily accessible mastoid and forehead scalp regions.

METHODS

An artificial neural network was trained on ERP signals recorded from below hairline electrode locations (Tp9, Tp10, Af7, Af8 referenced to Fp1, Fp2) to predict signals recorded at the ideal Cz location.

RESULTS

The model resulted in mean improvements in intraclass correlation coefficient relative to control for all stimulus types (Standard Tones: +9.74%, Deviant Tones: +3.23%, Congruent Words: +15.25%, Incongruent Words: +25.43%) and decreases in RMS Error (Standard Tones: -26.72%, Deviant Tones: -17.80%, Congruent Words: -28.78%, Incongruent Words: -29.61%) compared to the individual distant channels. Measured vs predicted ERP amplitudes were highly and significantly correlated with control for the N100 (R = 0.5, p_adj < 0.05), P300 (R = 0.75, p_adj < 0.01), and N400 (R = 0.75, p_adj < 0.01) ERPs.

CONCLUSION

ERP waveforms at distant channels can be combined using a neural network autoencoder to model the control channel features with better precision than those at individual distant channels. This is the first demonstration of feasibility of predicting evoked potentials and brain vital signs using signals recorded from more distant, practical locations.

SIGNIFICANCE

This solves a key engineering challenge for applications that require portability, comfort, and speed of measurement as design priorities for measurement of event-related potentials across a range of individuals, settings, and circumstances.

Novel Signal Processing Technique for Capture and Isolation of Blink-Related Oscillations Using a Low-Density Electrode Array for Bedside Evaluation of Consciousness

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.

Brain Vital Signs: Expanding From the Auditory to Visual Modality

The critical need for rapid objective, physiological evaluation of brain function at point-of-care has led to the emergence of brain vital signs-a framework encompassing a portable electroencephalography (EEG) and an automated, quick test protocol. This framework enables access to well-established event-related potential (ERP) markers, which are specific to sensory, attention, and cognitive functions in both healthy and patient populations. However, all our applications to-date have used auditory stimulation, which have highlighted application challenges in persons with hearing impairments (e.g., aging, seniors, dementia). Consequently, it has become important to translate brain vital signs into a visual sensory modality. Therefore, the objectives of this study were to: 1) demonstrate the feasibility of visual brain vital signs; and 2) compare and normalize results from visual and auditory brain vital signs. Data were collected from 34 healthy adults (33 ± 13 years) using a 64-channel EEG system. Visual and auditory sequences were kept as comparable as possible to elicit the N100, P300, and N400 responses. Visual brain vital signs were elicited successfully for all three responses across the group (N100: F = 29.8380, p < 0.001; P300: F = 138.8442, p < 0.0001; N400: F = 6.8476, p = 0.01). Initial auditory-visual comparisons across the three components showed attention processing (P300) was found to be the most transferrable across modalities, with no group-level differences and correlated peak amplitudes (rho = 0.7, p = 0.0001) across individuals. Auditory P300 latencies were shorter than visual (p < 0.0001) but normalization and correlation (r = 0.5, p = 0.0033) implied a potential systematic difference across modalities. Reduced auditory N400 amplitudes compared to visual (p = 0.0061) paired with normalization and correlation across individuals (r = 0.6, p = 0.0012), also revealed potential systematic modality differences between reading and listening language comprehension. This study provides an initial understanding of the relationship between the visual and auditory sequences, while importantly establishing a visual sequence within the brain vital signs framework. With both auditory and visual stimulation capabilities available, it is possible to broaden applications across the lifespan.

Accessing knowledge of the 'here and now': a new technique for capturing electromagnetic markers of orientation processing

OBJECTIVE

The ability to orient with respect to the current context (e.g. current time or location) is crucial for daily functioning, and is used to measure overall cognitive health across many frontline clinical assessments. However, these tests are often hampered by their reliance on verbal probes (e.g. 'What city are we in?') in evaluating orientation. Objective, physiology-based measures of orientation processing are needed, but no such measures are currently in existence. We report the initial development of potential brainwave-based markers of orientation processing as characterized using electroencephlography (EEG) and magnetoencephalography (MEG).

APPROACH

An auditory stimulus sequence embedded with words corresponding to orientation-relevant (i.e. related to the 'here and now') and orientation-irrelevant (i.e. unrelated to the current context) conditions was used to elicit orientation processing responses. EEG/MEG data, in concert with clinical assessments, were collected from 29 healthy adults. Analysis at sensor and source levels identified and characterized neural signals related to orientation processing.

MAIN RESULTS

Orientation-irrelevant stimuli elicited increased negative amplitude in EEG-derived event-related potential (ERP) waveforms during the 390-570 ms window (p  <  0.05), with cortical activations across the left frontal, temporal, and parietal regions. These effects are consistent with the well-known N400 response to semantic incongruence. In contrast, ERP responses to orientation-relevant stimuli exhibited increased positive amplitude during the same interval (p  <  0.05), with activations across the bilateral temporal and parietal regions. Importantly, these differential responses were robust at the individual level, with machine-learning classification showing high accuracy (89%), sensitivity (0.88) and specificity (0.90).

SIGNIFICANCE

This is the first demonstration of a neurotechnology platform that elicits, captures, and evaluates electrophysiological markers of orientation processing. We demonstrate neural responses to orientation stimuli that are validated across EEG and MEG modalities and robust at the individual level. The extraction of physiology-based markers through this technique may enable improved objective brain functional evaluation in clinical applications.

Point-of-care brain injury evaluation of conscious awareness: wide scale deployment of portable HCS EEG evaluation

Survivors of severe brain injury may remain in a decreased state of conscious awareness for an extended period of time. Clinical scales are used to describe levels of consciousness but rely on behavioural responses, precipitating misdiagnosis. We have previously utilized event-related potentials (ERPs) to circumvent reliance on behavioural responses. However, practical implementation barriers limit the clinical utility of ERP assessment at point-of-care (POC). To address this challenge, we developed the Halifax Consciousness Scanner (HCS)-a rapid, semi-automated electroencephalography system. The current study evaluated: (i) HCS feasibility in sub-acute, POC settings nationwide; (ii) ERP P300 responses in patients with acquired brain injury versus healthy controls; and (iii) correlations within and between clinical measures and P300 latencies. We assessed 28 patients with severe, chronic impairments from brain injuries and contrasted the results with healthy control data (n = 100). Correlational analyses examined relationships between P300 latencies and the commonly used clinical scales. P300 latencies were significantly delayed in patients compared to healthy controls (P < 0.05). Clinical assessment scores were significantly inter-correlated and correlated significantly with P300 latencies (P < 0.05). In sub-acute and chronic care settings, the HCS provided a physiological measure of neurocognitive processing at POC for patients with severe acquired brain injury, including those with disorders of consciousness.

Developing Brain Vital Signs: Initial Framework for Monitoring Brain Function Changes Over Time

Clinical assessment of brain function relies heavily on indirect behavior-based tests. Unfortunately, behavior-based assessments are subjective and therefore susceptible to several confounding factors. Event-related brain potentials (ERPs), derived from electroencephalography (EEG), are often used to provide objective, physiological measures of brain function. Historically, ERPs have been characterized extensively within research settings, with limited but growing clinical applications. Over the past 20 years, we have developed clinical ERP applications for the evaluation of functional status following serious injury and/or disease. This work has identified an important gap: the need for a clinically accessible framework to evaluate ERP measures. Crucially, this enables baseline measures before brain dysfunction occurs, and might enable the routine collection of brain function metrics in the future much like blood pressure measures today. Here, we propose such a framework for extracting specific ERPs as potential “brain vital signs.” This framework enabled the translation/transformation of complex ERP data into accessible metrics of brain function for wider clinical utilization. To formalize the framework, three essential ERPs were selected as initial indicators: (1) the auditory N100 (Auditory sensation); (2) the auditory oddball P300 (Basic attention); and (3) the auditory speech processing N400 (Cognitive processing). First step validation was conducted on healthy younger and older adults (age range: 22–82 years). Results confirmed specific ERPs at the individual level (86.81–98.96%), verified predictable age-related differences (P300 latency delays in older adults, p < 0.05), and demonstrated successful linear transformation into the proposed brain vital sign (BVS) framework (basic attention latency sub-component of BVS framework reflects delays in older adults, p < 0.05). The findings represent an initial critical step in developing, extracting, and characterizing ERPs as vital signs, critical for subsequent evaluation of dysfunction in conditions like concussion and/or dementia.

Detection of event-related potentials in individual subjects using support vector machines

Event-related potentials (ERPs) are tiny electrical brain responses in the human electroencephalogram that are typically not detectable until they are isolated by a process of signal averaging. Owing to the extremely smallsize of ERP components (ranging from less than 1 μV to tens of μV), compared to background brain rhythms, statistical analyses of ERPs are predominantly carried out in groups of subjects. This limitation is a barrier to the translation of ERP-based neuroscience to applications such as medical diagnostics. We show here that support vector machines (SVMs) are a useful method to detect ERP components in individual subjects with a small set of electrodes and a small number of trials for a mismatch negativity (MMN) ERP component. Such a reduced experiment setup is important for clinical applications. One hundred healthy individuals were presented with an auditory pattern containing pattern-violating deviants to evoke the MMN. Two-class SVMs were then trained to classify averaged ERP waveforms in response to the standard tone (tones that match the pattern) and deviant tone stimuli (tones that violate the pattern). The influence of kernel type, number of epochs, electrode selection, and temporal window size in the averaged waveform were explored. When using all electrodes, averages of all available epochs, and a temporal window from 0 to 900-ms post-stimulus, a linear SVM achieved 94.5 % accuracy. Further analyses using SVMs trained with narrower, sliding temporal windows confirmed the sensitivity of the SVM to data in the latency range associated with the MMN.

A rapid event-related potential (ERP) method for point-of-care evaluation of brain function: development of the Halifax Consciousness Scanner

BACKGROUND

Event-related potentials (ERPs) may provide a non-invasive index of brain function for a range of clinical applications. However, as a lab-based technique, ERPs are limited by technical challenges that prevent full integration into clinical settings.

NEW METHOD

To translate ERP capabilities from the lab to clinical applications, we have developed methods like the Halifax Consciousness Scanner (HCS). HCS is essentially a rapid, automated ERP evaluation of brain functional status. The present study describes the ERP components evoked from auditory tones and speech stimuli. ERP results were obtained using a 5-min test in 100 healthy individuals. The HCS sequence was designed to evoke the N100, the mismatch negativity (MMN), P300, the early negative enhancement (ENE), and the N400. These components reflected sensation, perception, attention, memory, and language perception, respectively. Component detection was examined at group and individual levels, and evaluated across both statistical and classification approaches.

RESULTS

All ERP components were robustly detected at the group level. At the individual level, nonparametric statistical analyses showed reduced accuracy relative to support vector (SVM) machine classification, particularly for speech-based ERPs. Optimized SVM results were MMN: 95.6%; P300: 99.0%; ENE: 91.8%; and N400: 92.3%.

CONCLUSIONS

A spectrum of individual-level ERPs can be obtained in a very short time. Machine learning classification improved detection accuracy across a large healthy control sample. Translating ERPs into clinical applications is increasingly possible at the individual level.

Comparison of foam-based and spring-loaded dry EEG electrodes with wet electrodes in resting and moving conditions

The introduction of dry electrodes for EEG measurements has opened up possibilities of recording EEG outside of standard clinical environments by reducing required preparation and maintenance. However, the signal quality of dry electrodes in comparison with wet electrodes has not yet been evaluated under activities of daily life (ADL) or high motion tasks. In this study, we compared the performances of foam-based and spring-loaded dry electrodes with wet electrodes under three different task conditions: resting state, walking, and cycling. Our analysis showed that signals obtained by the 2 types of dry electrodes and obtained by wet electrodes displayed high correlation for all conditions, while being prone to similar environmental and electrode-based artifacts. Overall, our results suggest that dry electrodes have a similar signal quality in comparison to wet electrodes during motion and may be more practical for use in mobile and real-time motion applications due to their convenience. In addition, we conclude that as with wet electrodes, post-processing can mitigate motion artifacts in ambulatory EEG acquisition.

Novel wireless electroencephalography system with a minimal preparation time for use in emergencies and prehospital care

Background

Although clinical applications such as emergency medicine and prehospital care could benefit from a fast-mounting electroencephalography (EEG) recording system, the lack of specifically designed equipment restricts the use of EEG in these environments.

Methods

This paper describes the design and testing of a six-channel emergency EEG (emEEG) system with a rapid preparation time intended for use in emergency medicine and prehospital care. The novel system comprises a quick-application cap, a device for recording and transmitting the EEG wirelessly to a computer, and custom software for displaying and streaming the data in real-time to a hospital. Bench testing was conducted, as well as healthy volunteer and patient measurements in three different environments: a hospital EEG laboratory, an intensive care unit, and an ambulance. The EEG data was evaluated by two experienced clinical neurophysiologists and compared with recordings from a commercial system.

Results

The bench tests demonstrated that the emEEG system's performance is comparable to that of a commercial system while the healthy volunteer and patient measurements confirmed that the system can be applied quickly and that it records quality EEG data in a variety of environments. Furthermore, the recorded data was judged to be of diagnostic quality by two experienced clinical neurophysiologists.

Conclusions

In the future, the emEEG system may be used to record high-quality EEG data in emergency medicine and during ambulance transportation. Its use could lead to a faster diagnostic, a more accurate treatment, and a shorter recovery time for patients with neurological brain disorders.