The Influence of Age and Skull Conductivity on Surface and Subdermal Bipolar EEG Leads

Bioelectric source measurements are influenced by the measurement location as well as the conductive properties of the tissues. Volume conductor effects such as the poorly conducting bones or the moderately conducting skin are known to affect the measurement precision and accuracy of the surface electroencephalography (EEG) measurements. This paper investigates the influence of age via skull conductivity upon surface and subdermal bipolar EEG measurement sensitivity conducted on two realistic head models from the Visible Human Project. Subdermal electrodes (a.k.a. subcutaneous electrodes) are implanted on the skull beneath the skin, fat, and muscles. We studied the effect of age upon these two electrode types according to the scalp-to-skull conductivity ratios of 5, 8, 15, and 30 : 1. The effects on the measurement sensitivity were studied by means of the half-sensitivity volume (HSV) and the region of interest sensitivity ratio (ROISR). The results indicate that the subdermal implantation notably enhances the precision and accuracy of EEG measurements by a factor of eight compared to the scalp surface measurements. In summary, the evidence indicates that both surface and subdermal EEG measurements benefit better recordings in terms of precision and accuracy on younger patients

Smart helmet: wearable multichannel ECG & EEG

Modern wearable technologies have enabled continuous recording of vital signs, however, for activities such as cycling, motor-racing, or military engagement, a helmet with embedded sensors would provide maximum convenience and the opportunity to monitor simultaneously both the vital signs and the electroencephalogram (EEG). To this end, we investigate the feasibility of recording the electrocardiogram (ECG), respiration, and EEG from face-lead locations, by embedding multiple electrodes within a standard helmet. The electrode positions are at the lower jaw, mastoids, and forehead, while for validation purposes a respiration belt around the thorax and a reference ECG from the chest serve as ground truth to assess the performance. The within-helmet EEG is verified by exposing the subjects to periodic visual and auditory stimuli and screening the recordings for the steady-state evoked potentials in response to these stimuli. Cycling and walking are chosen as real-world activities to illustrate how to deal with the so-induced irregular motion artifacts, which contaminate the recordings. We also propose a multivariate R-peak detection algorithm suitable for such noisy environments. Recordings in real-world scenarios support a proof of concept of the feasibility of recording vital signs and EEG from the proposed smart helmet

Spatiotemporal techniques in multimodal imaging for brain mapping and epilepsy

Thesis (Ph.D.)--Boston UniversityThis thesis explored multimodal brain imaging using advanced spatiotemporal techniques. The first set of experiments were based on simulations. Much controversy exists in the literature regarding the differences between magnetoencephalography (MEG) and electroencephalography (EEG}, both practically and theoretically. The differences were explored using simulations that evaluated the expected signal-to-noise ratios from reasonable brain sources. MEG and EEG were found to be complementary, with each modality optimally suited to image activity from different areas of the cortical surface. Consequently, evaluations of epileptic patients and general neuroscience experiments will both benefit from simultaneously collected MEG/EEG. The second set of experiments represent an example of MEG combined with magnetic resonance imaging (MRI) and functional MRI (fMRI) applied to healthy subjects. The study set out to resolve two questions relating to shape perception. First, does the brain activate functional areas sequentially during shape perception, as has been suggested in recent literature? Second, which , if any, functional areas are active time-locked with reaction-time? The study found that functional areas are non-sequentially activated, and that area IT is active time-locked with reaction-time. These two points, coupled with the method for multimodal integration , can help further develop our understanding of shape perception in particular, and cortical dynamics in general for healthy subjects. Broadly, these two studies represent practical guidelines for epilepsy evaluations and brain mapping studies. For epilepsy studies, clinicians could combine MEG and EEG to maximize the probability of finding the source of seizures. For brain mapping in general, EEG, MEG, MRI and fMRI can be combined in the methods outlined here to obtain more sophisticated views of cortical dynamics

Functional network and spectral analysis of clinical EEG data to identify quantitative biomarkers and classify brain disorders

Many cognitive and neurological disorders today, such as Autism Spectrum Disorders (ASD) and various forms of epilepsy such as infantile spasms (IS), manifest as changes in voltage activity recorded in scalp electroencephalograms (EEG). Diagnosis of brain disease often relies on the interpretation of complex EEG features through visual inspection by clinicians. Although clinically useful, such interpretation is subjective and suffers from poor inter-rater reliability, which affects clinical care through increased variability and uncertainty in diagnosis. In addition, such qualitative assessments are often binary, and do not parametrically measure characteristics of disease manifestations. Many cognitive disorders are grouped by similar behaviors, but may arise from distinct biological causes, possibly represented by subtle electrophysiological differences. To address this, quantitative analytical tools - such as functional network connectivity, frequency-domain, and time-domain features - are being developed and applied to clinically obtained EEG data to identify electrophysiological biomarkers. These biomarkers enhance a clinician’s ability to accurately diagnose, categorize, and select treatment for various neurological conditions. In the first study, we use spectral and functional network analysis of clinical EEG data recorded from a population of children to propose a cortical biomarker for autism. We first analyze a training set of age-matched (4–8 years) ASD and neurotypical children to develop hypotheses based on power spectral features and measures of functional network connectivity. From the training set of subjects, we derive the following hypotheses: 1) The ratio of the power of the posterior alpha rhythm (8–14 Hz) peak to the anterior alpha rhythm peak is significantly lower in ASD than control subjects. 2) The functional network density is lower in ASD subjects than control subjects. 3) A select group of edges provide a more sensitive and specific biomarker of ASD. We then test these hypotheses in a validation set of subjects and show that both the first and third hypotheses, but not the second, are validated. The validated features successfully classified the data with significant accuracy. These results provide a validated study for EEG biomarkers of ASD based on changes in brain rhythms and functional network characteristics. We next perform a follow-up study that utilizes the same group of ASD and neurotypical subjects, but focuses on differences between these two groups in the sleep state. Motivated by the results from the previous study, we utilize the previously validated biomarkers, including the alpha ratio and the subset of edges found to be a sensitive biomarker of ASD, and test their effectiveness in the sleep state. To complement these frequency domain features, we also investigate the efficacy of several time domain measures. This investigation did not lead to significant findings, which may have important implications for the differences between sleep and wake states in ASD, or perhaps generally for clinical assessment, as well as for the effect of noise on signal in clinically obtained data. Finally, we design a similar analysis framework to investigate a set of clinical EEG data recorded from a population of children with active infantile spasms (IS) (2-16 months), and age-matched neurotypical children, in both wake and sleep states. The goal of this analysis is to develop a quantitative biomarker from the EEG signal, which ultimately we will apply to predict the clinical outcome of children with IS. In addition to spectral and functional network analysis, we calculate time domain features previously found to correlate with seizures. We compare the two populations by each feature individually, test the effects of age on these features, use all features in a linear discriminant model to categorize IS versus neurotypical EEG, and test the findings using a leave-one-out validation test. We find almost every feature tested shows significant population differences between IS and control groups, and that taken together they serve as an effective classifier, with potential to be informative as to disease severity and long-term outcome. Furthermore, analysis of these features reveals two groups, indicating a possibility that these features reflect two distinct qualitative characteristics of IS and seizures

Detection of pathological high-frequency oscillations in refractory epilepsy patients undergoing simultaneous stereo-electroencephalography and magnetoencephalography

BACKGROUND: Stereo-electroencephalography (SEEG) and magnetoencephalography (MEG) have generally been used independently as part of the pre-surgical evaluation of drug-resistant epilepsy (DRE) patients. However, the possibility of simultaneously employing these recording techniques to determine whether MEG has the potential of offering the same information as SEEG less invasively, or whether it could offer a greater spatial indication of the epileptogenic zone (EZ) to aid surgical planning, has not been previously evaluated. METHODS: Data from 24 paediatric and adult DRE patients, undergoing simultaneous SEEG and MEG as part of their pre-surgical evaluation, was analysed employing manual and automated high-frequency oscillations (HFOs) detection, and spectral and source localisation analyses. RESULTS: Twelve patients (50%) were included in the analysis (4 males; mean age=25.08 years) and showed interictal SEEG and MEG HFOs. HFOs detection was concordant between the two recording modalities, but SEEG displayed higher ability of differentiating between deep and superficial epileptogenic sources. Automated HFO detector in MEG recordings was validated against the manual MEG detection method. Spectral analysis revealed that SEEG and MEG detect distinct epileptic events. The EZ was well correlated with the simultaneously recorded data in 50% patients, while 25% patients displayed poor correlation or discordance. CONCLUSIONS: MEG recordings can detect HFOs, and simultaneous use of SEEG and MEG HFO identification facilitates EZ localisation during the presurgical planning stage for DRE patients. Further studies are necessary to validate these findings and support the translation of automated HFO detectors into routine clinical practice

Motor Unit-Driven Identification of Pathological Tremor in Electroencephalograms.

Background: Traditional studies on the neural mechanisms of tremor use coherence analysis to investigate the relationship between cortical and muscle activity, measured by electroencephalograms (EEG) and electromyograms (EMG). This methodology is limited by the need of relatively long signal recordings, and it is sensitive to EEG artifacts. Here, we analytically derive and experimentally validate a new method for automatic extraction of the tremor-related EEG component in pathological tremor patients that aims to overcome these limitations. Methods: We exploit the coupling between the tremor-related cortical activity andmotor unit population firings to build a linearminimummean square error estimator of the tremor component in EEG. We estimated the motor unit population activity by decomposing surface EMG signals into constituent motor unit spike trains, which we summed up into a cumulative spike train (CST). We used this CST to initialize our tremor-related EEG component estimate, which we optimized using a novel approach proposed here. Results: Tests on simulated signals demonstrate that our new method is robust to both noise and motor unit firing variability, and that it performs well across a wide range of spectral characteristics of the tremor. Results on 9 essential (ET) and 9 Parkinson’s disease (PD) patients show a ∼2-fold increase in amplitude of the coherence between the estimated EEG component and the CST, compared to the classical EEG-EMG coherence analysis. Conclusions: We have developed a novel method that allows for more precise and robust estimation of the tremor-related EEG component. This method does not require artifact removal, provides reliable results in relatively short datasets, and tracks changes in the tremor-related cortical activity over time.post-print2672 K

The multifocal visual evoked cortical potential in visual field mapping: a methodological study.

The application of multifocal techniques to the visual evoked cortical potential permits objective electrophysiological mapping of the visual field. The multifocal visual evoked cortical potential (mfVECP) presents several technical challenges. Signals are small, are influenced by a number of sources of noise and waveforms vary both across the visual field and between subjects due to the complex geometry of the visual cortex. Together these factors hamper the ability to distinguish between a mfVECP response from the healthy visual pathway, and a response that is reduced or absent and is therefore representative of pathology. This thesis presents a series of methodological investigations with the aim of maximising the information available in the recorded electrophysiological response, thereby improving the performance of the mfVECP. A novel method of calculating the signal to noise ratio (SNR) of mfVECP waveform responses is introduced. A noise estimate unrelated to the response of the visual cortex to the visual stimulus is created. This is achieved by cross-correlating m-sequences which are created when the orthogonal set of m-sequences are created but are not used to control a stimulus region, with the physiological record. This metric is compared to the approach of defining noise within a delayed time window and shows good correlation. ROC analysis indicates a small improvement in the ability to distinguish between physiological waveform responses and noise. Defining the signal window as 45-250ms is recommended. Signal quality is improved by post-acquisition bandwidth filtering. A wide range of bandwidths are compared and the greatest gains are seen with a bandpass of 3 to 20Hz applied after cross-correlation. Responses evoked when stimulation is delivered using a cathode ray tube (CRT) and a liquid crystal display (LCD) projector system are compared. The mode of stimulus delivery affects the waveshape of responses. A significantly higher SNR is seen in waveforms is shown in waveforms evoked by an m=16 bit m-sequence delivered by a CRT monitor. Differences for shorter m-sequences were not statistically significant. The area of the visual field which can usefully be tested is investigated by increasing the field of view of stimulation from 20° to 40° of radius in 10° increments. A field of view of 30° of radius is shown to provide stimulation of as much of the visual field as possible without losing signal quality. Stimulation rates of 12.5 to 75Hz are compared. Slowing the stimulation rate produced increases waveform amplitudes, latencies and SNR values. The best performance was achieved with 25Hz stimulation. It is shown that a six-minute recording stimulated at 25Hz is superior to an eight-minute, 75Hz acquisition. An electrophysiology system capable of providing multifocal stimulation, synchronising with the acquisition of data from a large number of electrodes and performing cross-correlation has been created. This is a powerful system which permits the interrogation of the dipoles evoked within the complex geometry of the visual cortex from a very large number of orientations, which will improve detection ability. The system has been used to compare the performance of 16 monopolar recording channels in detecting responses to stimulation throughout the visual field. A selection of four electrodes which maximise the available information throughout the visual field has been made. It is shown that a several combinations of four electrodes provide good responses throughout the visual field, but that it is important to have them distributed on either hemisphere and above and below Oz. A series of investigations have indicated methods of maximising the available information in mfVECP recordings and progress the technique towards becoming a robust clinical tool. A powerful multichannel multifocal electrophysiology system has been created, with the ability to simultaneously acquire data from a very large number of bipolar recording channels and thereby detect many small dipole responses to stimulation of many small areas of the visual field. This will be an invaluable tool in future investigations. Performance has been shown to improve when the presence or absence of a waveform is determined by a novel SNR metric, when data is filtered post-acquisition through a 3-20Hz bandpass after cross-correlation and when a CRT is used to deliver the stimulus. The field of view of stimulation can usefully be extended to a radius of 30° when a 60-region dartboard pattern is employed. Performance can be enhanced at the same time as acquisition time is reduced by 25%, by the use of a 25Hz rate of stimulation instead of the frequently employed rate of 75Hz

Sensing the human alpha rhythm using a non-contact electroencephalographic (EEG) electrode

The electroencephalogram is the recording of bioelectrical potentials on the scalp due to neural current sources in the brain and are typically recorded using wet surface electrodes that make ohmic contact with the scalp surface using an electrolyte gel. Unfortunately, wet electrodes are intrusive to the user, problematic for EEG studies requiring high spatial resolution, and are unsuitable for long-duration EEG recordings. Wet electrodes ultimately limit spatial resolution since the gel can short neighboring electrodes. They also do not meet long-duration recording demands since the gel can dry out over time. This dissertation explores the feasibility of measuring the EEG at room temperature, through hair, without scalp contact using two capacitive probe techniques. This is achieved by focusing on measurement of the alpha rhythm, an oscillatory EEG signal that is common among the population and is easily elicited with eye closure. Research results suggest that it is possible to sense the alpha rhythm within 4.0mm of scalp-probe spacing and that the ultra-high impedance fieldmeter probe technique is the most promising. Non-contact recordings are compared to wet electrode recordings and issues related to hair and motion artifact are discussed. Areas critical to the development of this technology are suggested

Ultra low power wearable sleep diagnostic systems

Sleep disorders are studied using sleep study systems called Polysomnography that records several biophysical parameters during sleep. However, these are bulky and are typically located in a medical facility where patient monitoring is costly and quite inefficient. Home-based portable systems solve these problems to an extent but they record only a minimal number of channels due to limited battery life. To surmount this, wearable sleep system are desired which need to be unobtrusive and have long battery life. In this thesis, a novel sleep system architecture is presented that enables the design of an ultra low power sleep diagnostic system. This architecture is capable of extending the recording time to 120 hours in a wearable system which is an order of magnitude improvement over commercial wearable systems that record for about 12 hours. This architecture has in effect reduced the average power consumption of 5-6 mW per channel to less than 500 uW per channel. This has been achieved by eliminating sampled data architecture, reducing the wireless transmission rate and by moving the sleep scoring to the sensors. Further, ultra low power instrumentation amplifiers have been designed to operate in weak inversion region to support this architecture. A 40 dB chopper-stabilised low power instrumentation amplifiers to process EEG were designed and tested to operate from 1.0 V consuming just 3.1 uW for peak mode operation with DC servo loop. A 50 dB non-EEG amplifier continuous-time bandpass amplifier with a consumption of 400 nW was also fabricated and tested. Both the amplifiers achieved a high CMRR and impedance that are critical for wearable systems. Combining these amplifiers with the novel architecture enables the design of an ultra low power sleep recording system. This reduces the size of the battery required and hence enables a truly wearable system.Open Acces