Development and Characterization of Ear-EEG for Real-Life Brain-Monitoring

Functional brain monitoring methods for neuroscience and medical diagnostics have until recently been limited to laboratory settings. However, there is a great potential for studying the human brain in the everyday life, with measurements performed in more realistic real-life settings. Electroencephalography (EEG) can be measured in real-life using wearable EEG equipment. Current wearable EEG devices are typically based on scalp electrodes, causing the devices to be visible and often uncomfortable to wear for long-term recordings. Ear-EEG is a method where EEG is recorded from electrodes placed in the ear. The Ear-EEG supports non-invasive long-term recordings of EEG in real-life in a discreet way. This Ph.D. project concerns the characterization and development of ear-EEG for real-life brain-monitoring. This was addressed through characterization of physiological artifacts in real-life settings, development and characterization of dry-contact electrodes for real-life ear-EEG acquisition, measurements of ear-EEG in real-life, and development of a method for mapping cortical sources to the ear. Characterization of physiological artifacts showed a similar artifact level for recordings from ear electrodes and temporal lobe scalp electrodes. Dry-contact electrodes and flexible earpieces were developed to increase the comfort and user-friendliness of the ear-EEG. In addition, electronic instrumentation was developed to allow implementation in a hearing-aid-sized ear-EEG device. Ear-EEG measurements performed in real-life settings with the dry-contact electrodes, were comparable to temporal lobe scalp EEG, when referenced to a Cz scalp electrode. However, the recordings showed that further development of the earpieces and electrodes are needed to obtain a satisfying recording quality, when the reference is located close to or in the ear. Mapping of the electric fields from well-defined cortical sources to the ear, showed good agreement with previous ear-EEG studies and has the potential to provide valuable information for future development of the ear-EEG method. The Ph.D. project showed that ear-EEG measurements can be performed in real-life, with dry-contact electrodes. The brain processes studied, were established with comparable clarity on recordings from temporal lobe scalp and ear electrodes. With further development of the earpieces, electrodes, and electronic instrumentation, it appears to be realistic to implement ear-EEG into unobtrusive and user-friendly devices for monitoring of human brain processes in real-life

A Brief Exposition on Brain-Computer Interface

Brain-Computer Interface is a technology that records brain signals and translates them into useful commands to operate a drone or a wheelchair. Drones are used in various applications such as aerial operations, where pilot’s presence is impossible. The BCI can also be used for patients suffering from brain diseases who lose their body control and are unable to move to satisfy their basic needs. By taking advantage of BCI and drone technology, algorithms for Mind-Controlled Unmanned Aerial System can be developed. This paper deals with the classification of BCI & UAV, methodologies of BCI, the framework of BCI, neuro-imaging methods, BCI headset options, BCI platforms, electrode types & their placement, and the result of feature extraction technique (FFT) with 72.5% accuracy

Biophysical Source Modeling of Some Exogenous and Endogenous Components of the Human Event-Related Potential

Methods of dipole localization were applied to human scalp-recorded electrical activity associated with a simple auditory cognitive discrimination task. Human neuroanatomy and neurophysiology were reviewed from a biophysical standpoint in order to describe the probable neurogenesis of electrical activity in the brain and on the surface of the head. Topographic electroencephalography (EEG) analysis and source localization methods were historically reviewed in detail, followed by a brief review of the history of non-invasive evoked potential (EP) and magnetic field measurements of human central nervous system activity. Four well known simple cognitive tasks were considered that were known to elicit non-obligatory brain responses, and the odd-ball task chosen. Three subjects listened to a series of two tones, one frequent and one rare, and counted the rare tones. During task performance, 40 to 46 channels of EEG activity were recorded from their scalps. From the EEG data, average evoked potentials (aEP) were calculated for the frequent and rare conditions. From these a difference response was calculated. All three of these EPs were plotted as equipotential maps over a schematic of a head for topographic display and the major distribution features discussed. These aEPs and maps matched those previously reported in the literature. From estimates of the spatial electrical power over the head, four peak components were selected for analysis by equivalent source modeling (ESM). These were designated the FP40, FP100, FP200, and FP350, where FP stands for field power. ESM demonstrated that one centrally located point dipole or two bilaterally symmetric dipoles could model the empirical data quite well. These results were discussed in relation to other topographic studies, as well as studies of intracranial recordings, lesions, and animal models. The source locations found were consistent with auditory cortical locations for the obligatory sensory peaks (FP40, FP100, FP200) and with brainstem locations as the source of the FP350 cognitive event-related peak.</p

Development of a practical and mobile brain-computer communication device for profoundly paralyzed individuals

Thesis (Ph.D.)--Boston UniversityBrain-computer interface (BCI) technology has seen tremendous growth over the past several decades, with numerous groundbreaking research studies demonstrating technical viability (Sellers et al., 2010; Silvoni et al., 2011). Despite this progress, BCIs have remained primarily in controlled laboratory settings. This dissertation proffers a blueprint for translating research-grade BCI systems into real-world applications that are noninvasive and fully portable, and that employ intelligent user interfaces for communication. The proposed architecture is designed to be used by severely motor-impaired individuals, such as those with locked-in syndrome, while reducing the effort and cognitive load needed to communicate. Such a system requires the merging of two primary research fields: 1) electroencephalography (EEG)-based BCIs and 2) intelligent user interface design. The EEG-based BCI portion of this dissertation provides a history of the field, details of our software and hardware implementation, and results from an experimental study aimed at verifying the utility of a BCI based on the steady-state visual evoked potential (SSVEP), a robust brain response to visual stimulation at controlled frequencies. The visual stimulation, feature extraction, and classification algorithms for the BCI were specially designed to achieve successful real-time performance on a laptop computer. Also, the BCI was developed in Python, an open-source programming language that combines programming ease with effective handling of hardware and software requirements. The result of this work was The Unlock Project app software for BCI development. Using it, a four-choice SSVEP BCI setup was implemented and tested with five severely motor-impaired and fourteen control participants. The system showed a wide range of usability across participants, with classification rates ranging from 25-95%. The second portion of the dissertation discusses the viability of intelligent user interface design as a method for obtaining a more user-focused vocal output communication aid tailored to motor-impaired individuals. A proposed blueprint of this communication "app" was developed in this dissertation. It would make use of readily available laptop sensors to perform facial recognition, speech-to-text decoding, and geo-location. The ultimate goal is to couple sensor information with natural language processing to construct an intelligent user interface that shapes communication in a practical SSVEP-based BCI

Real-time detection of auditory : steady-state brainstem potentials evoked by auditory stimuli

The auditory steady-state response (ASSR) is advantageous against other hearing techniques because of its capability in providing objective and frequency specific information. The objectives are to reduce the lengthy test duration, and improve the signal detection rate and the robustness of the detection against the background noise and unwanted artefacts.Two prominent state estimation techniques of Luenberger observer and Kalman filter have been used in the development of the autonomous ASSR detection scheme. Both techniques are real-time implementable, while the challenges faced in the application of the observer and Kalman filter techniques are the very poor SNR (could be as low as −30dB) of ASSRs and unknown statistics of the noise. Dual-channel architecture is proposed, one is for the estimate of sinusoid and the other for the estimate of the background noise. Simulation and experimental studies were also conducted to evaluate the performances of the developed ASSR detection scheme, and to compare the new method with other conventional techniques. In general, both the state estimation techniques within the detection scheme produced comparable results as compared to the conventional techniques, but achieved significant measurement time reduction in some cases. A guide is given for the determination of the observer gains, while an adaptive algorithm has been used for adjustment of the gains in the Kalman filters.In order to enhance the robustness of the ASSR detection scheme with adaptive Kalman filters against possible artefacts (outliers), a multisensory data fusion approach is used to combine both standard mean operation and median operation in the ASSR detection algorithm. In addition, a self-tuned statistical-based thresholding using the regression technique is applied in the autonomous ASSR detection scheme. The scheme with adaptive Kalman filters is capable of estimating the variances of system and background noise to improve the ASSR detection rate

Visual Evoked Potentials: Analysis of the Fovea and Perifovea

Visual evoked potentials (VEP) were used to measure how stimulus properties (pattern VEP check sizes/spatial frequency) and retinal eccentricity (fovea versus perifovea) interact to give rise to the final VEP response. The purposes of this study were to investigate how stimulus check size (spatial frequencies) and retinal eccentricity affect the VEP response, re-examine whether the cortical magnification factor is applicable to VEP measures, and to determine optimal sized VEP checks for foveal and perifoveal stimuli. In this study, we used a foveal target that was a two degree circle with a diameter of 3.6 cm; a perifoveal target that was a circular ten degree annulus with an inner diameter of 3.6 cm and an outer diameter of 17.5 cm; and a full field target that was a ten degree circle with a diameter of 17.5 cm. These stimuli were chosen because they stimulate approximately the same amount of cortical area. VEPs were performed on ten healthy adult subjects monocularly through the dominant eye. Measurements of the implicit time (N1 and P1) and amplitude (N1-P1) were taken using four different VEP check sizes, 0.23, 0.52, 0.83, and 1.78 degrees). The findings of this study indicate that differences exist in sensitivity to specific check sizes (spatial frequencies) depending on the type of VEP measure used (implicit time or amplitude) and area of retina stimulated. These results are not consistent with a single stimulus being optimal for all measures and that there is a complex interaction between visual targets and responses

Applicability of subcortical EEG metrics of synaptopathy to older listeners with impaired audiograms

Emerging evidence suggests that cochlear synaptopathy is a common feature of sensorineural hearing loss, but it is not known to what extent electrophysiological metrics targeting synaptopathy in animals can be applied to people, such as those with impaired audiograms. This study investigates the applicability of subcortical electrophysiological measures associated with synaptopathy, i.e., auditory brainstem responses (ABRs) and envelope following responses (EFRs), to older participants with high-frequency sloping audiograms. The outcomes of this study are important for the development of reliable and sensitive synaptopathy diagnostics in people with normal or impaired outer-hair-cell function. Click-ABRs at different sound pressure levels and EFRs to amplitude-modulated stimuli were recorded, as well as relative EFR and ABR metrics which reduce the influence of individual factors such as head size and noise floor level on the measures. Most tested metrics showed significant differences between the groups and did not always follow the trends expected from synaptopathy. Age was not a reliable predictor for the electrophysiological metrics in the older hearing-impaired group or young normal-hearing control group. This study contributes to a better understanding of how electrophysiological synaptopathy metrics differ in ears with healthy and impaired audiograms, which is an important first step towards unravelling the perceptual consequences of synaptopathy.(C) 2019 Elsevier B.V. All rights reserved