Study and analysis of motion artifacts for ambulatory electroencephalography

Motion artifacts contribute complexity in acquiring clean electroencephalography (EEG) data. It is one of the major challenges for ambulatory EEG. The performance of mobile health monitoring, neurological disorders diagnosis and surgeries can be significantly improved by reducing the motion artifacts. Although different papers have proposed various novel approaches for removing motion artifacts, the datasets used to validate those algorithms are questionable. In this paper, a unique EEG dataset was presented where ten different activities were performed. No such previous EEG recordings using EMOTIV EEG headset are available in research history that explicitly mentioned and considered a number of daily activities that induced motion artifacts in EEG recordings. Quantitative study shows that in comparison to correlation coefficient, the coherence analysis depicted a better similarity measure between motion artifacts and motion sensor data. Motion artifacts were characterized with very low frequency which overlapped with the Delta rhythm of the EEG. Also, a general wavelet transform based approach was presented to remove motion artifacts. Further experiment and analysis with more similarity metrics and longer recording duration for each activity is required to finalize the characteristics of motion artifacts and henceforth reliably identify and subsequently remove the motion artifacts in the contaminated EEG recordings

Safety and EEG data quality of concurrent high-density EEG and high-speed fMRI at 3 Tesla

Concurrent EEG and fMRI is increasingly used to characterize the spatial-temporal dynamics of brain activity. However, most studies to date have been limited to conventional echo-planar imaging (EPI). There is considerable interest in integrating recently developed high-speed fMRI methods with high-density EEG to increase temporal resolution and sensitivity for task-based and resting state fMRI, and for detecting interictal spikes in epilepsy. In the present study using concurrent high-density EEG and recently developed high-speed fMRI methods, we investigate safety of radiofrequency (RF) related heating, the effect of EEG on cortical signal-to-noise ratio (SNR) in fMRI, and assess EEG data quality.The study compared EPI, multi-echo EPI, multi-band EPI and multi-slab echo-volumar imaging pulse sequences, using clinical 3 Tesla MR scanners from two different vendors that were equipped with 64- and 256-channel MR-compatible EEG systems, respectively, and receive only array head coils. Data were collected in 11 healthy controls (3 males, age range 18-70 years) and 13 patients with epilepsy (8 males, age range 21-67 years). Three of the healthy controls were scanned with the 256-channel EEG system, the other subjects were scanned with the 64-channel EEG system. Scalp surface temperature, SNR in occipital cortex and head movement were measured with and without the EEG cap. The degree of artifacts and the ability to identify background activity was assessed by visual analysis by a trained expert in the 64 channel EEG data (7 healthy controls, 13 patients).RF induced heating at the surface of the EEG electrodes during a 30-minute scan period with stable temperature prior to scanning did not exceed 1.0° C with either EEG system and any of the pulse sequences used in this study. There was no significant decrease in cortical SNR due to the presence of the EEG cap (p > 0.05). No significant differences in the visually analyzed EEG data quality were found between EEG recorded during high-speed fMRI and during conventional EPI (p = 0.78). Residual ballistocardiographic artifacts resulted in 58% of EEG data being rated as poor quality.This study demonstrates that high-density EEG can be safely implemented in conjunction with high-speed fMRI and that high-speed fMRI does not adversely affect EEG data quality. However, the deterioration of the EEG quality due to residual ballistocardiographic artifacts remains a significant constraint for routine clinical applications of concurrent EEG-fMRI

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

New directions in EEG measurement: an investigation into the fidelity of electrical potential sensor signals

Low frequency noise performance is the key indicator in determining the signal to noise ratio of a capacitively coupled sensor when used to acquire electroencephalogram signals. For this reason, a prototype Electric Potential Sensor device based on an auto-zero operational amplifier has been developed and evaluated. The absence of 1/f noise in these devices makes them ideal for use with signal frequencies ~10 Hz or less. The active electrodes are designed to be physically and electrically robust and chemically and biochemically inert. They are electrically insulated (anodized) and have diameters of 12 mm or 18 mm. In both cases, the sensors are housed in inert stainless steel machined housings with the electronics fabricated in surface mount components on a printed circuit board compatible with epoxy potting compounds. Potted sensors are designed to be immersed in alcohol for sterilization purposes. A comparative study was conducted with a commercial wet gel electrode system. These studies comprised measurements of both free running electroencephalogram and Event Related Potentials. Quality of the recorded electroencephalogram was assessed using three methods of inspection of raw signal, comparing signal to noise ratios, and Event Related Potentials noise analysis. A strictly comparable signal to noise ratio was observed and the overall conclusion from these comparative studies is that the noise performance of the new sensor is appropriate

Characterizing the Noise Associated with Sensor Placement and Motion Artifacts and Overcoming its Effects for Body-worn Physiological Sensors

Wearable sensors for continuous physiological monitoring have the potential to change the paradigm for healthcare by providing information in scenarios not covered by the existing clinical model. One key challenge for wearable physiological sensors is that their signal-to-noise ratios are low compared to those of their medical grade counterparts in hospitals. Two primary sources of noise are the sensor-skin contact interface and motion artifacts due to the user’s daily activities. These are challenging problems because the initial sensor placement by the user may not be ideal, the skin conditions can change over time, and the nature of motion artifacts is not predictable. The objective of this research is twofold. The first is to design sensors with reconfigurable contact to mitigate the effects of misplaced sensors or changing skin conditions. The second is to leverage signal processing techniques for accurate physiological parameter estimation despite the presence of motion artifacts. In this research, the sensor contact problem was specifically addressed for dry-contact electroencephalography (EEG). The proposed novel extension to a popular existing EEG electrode design enabled reconfigurable contact to adjust to variations in sensor placement and skin conditions over time. Experimental results on human subjects showed that reconfiguration of contact can reduce the noise in collected EEG signals without the need for manual intervention. To address the motion artifact problem, a particle filter based approach was employed to track the heart rate in cardiac signals affected by the movements of the user. The algorithm was tested on cardiac signals from human subjects running on a treadmill and showed good performance in accurately tracking heart rate. Moreover, the proposed algorithm enables fusion of multiple modalities and is also computationally more efficient compared to other contemporary approaches

Tutorial. Surface EMG detection, conditioning and pre-processing: Best practices

This tutorial is aimed primarily to non-engineers, using or planning to use surface electromyography (sEMG) as an assessment tool for muscle evaluation in the prevention, monitoring, assessment and rehabilitation fields. The main purpose is to explain basic concepts related to: (a) signal detection (electrodes, electrode–skin interface, noise, ECG and power line interference), (b) basic signal properties, such as amplitude and bandwidth, (c) parameters of the front-end amplifier (input impedance, noise, CMRR, bandwidth, etc.), (d) techniques for interference and artifact reduction, (e) signal filtering, (f) sampling and (g) A/D conversion, These concepts are addressed and discussed, with examples. The second purpose is to outline best practices and provide general guidelines for proper signal detection, conditioning and A/D conversion, aimed to clinical operators and biomedical engineers. Issues related to the sEMG origin and to electrode size, interelectrode distance and location, have been discussed in a previous tutorial. Issues related to signal processing for information extraction will be discussed in a subsequent tutorial

EEG Characterization During Motor Tasks That Are Difficult for Movement Disorder Patients

Movement disorders are a group of syndromes that often arise due to neurological abnormalities. Approximately 40 million Americans are affected by some form of movement disorder, significantly impacting patients’ quality of life and their ability to live independently. Deep brain stimulation (DBS) is one treatment that has shown promising results in the past couple decades, however, the currently used open-loop system has several drawbacks. By implementing a closed-loop or adaptive DBS (aDBS) system, the need for expensive parameter reprogramming sessions would be reduced, side-effects may be relieved, and habituation could be avoided. Several biomarkers, for example signals or activity derived from electroencephalogram (EEG), could potentially be used as a feedback source for aDBS. Here, we attempted to characterize cortical EEG potentials in healthy subjects performing six tasks that are difficult for those with movement disorders. Using a 32-channel EEG cap with an amplifier sampling at 500 Hz, we performed our protocol on 11 college-aged volunteers lacking any known movement disorder. For each task, we analyzed task-related power (TRP) changes, spectrograms, and topographical maps. In a finger movement exercise, we found task-related depression (TRD) in the delta band at the F4 electrode, as well as TRD at the C3 electrode in the alpha band during a pencil-pickup task, and TRD at the F3 electrode in the beta band during voluntary swallowing. While delta-ERD in the finger movement exercise was likely due to ocular artifact, the other significant results were in line with what relevant literature would predict. The findings from the work, in conjunction with a future study involving movement disorder patients, can provide insight into the use of EEG as a feedback source for aDBS. Keywords: EEG, electroencephalography, neurostimulation, deep brain stimulation, movement disorders, closed-loop DBS, adaptive DBS, aDB

Sensor Approach for Brain Pathophysiology of Freezing of Gait in Parkinson\u27s Disease Patients

Parkinson\u27s Disease (PD) affects over 1% of the population over 60 years of age and is expected to reach 1 million in the USA by the year 2020, growing by 60 thousand each year. It is well understood that PD is characterized by dopaminergic loss, leading to decreased executive function causing motor symptoms such as tremors, bradykinesia, dyskinesia, and freezing of gait (FoG) as well as non-motor symptoms such as loss of smell, depression, and sleep abnormalities. A PD diagnosis is difficult to make since there is no worldwide approved test and difficult to manage since its manifestations are widely heterogeneous among subjects. Thus, understanding the patient subsets and the neural biomarkers that set them apart will lead to improved personalized care. To explore the physiological alternations caused by PD on neurological pathways and their effect on motor control, it is necessary to detect the neural activity and its dissociation with healthy physiological function. To this effect, this study presents a custom ultra-wearable sensor solution, consisting of electroencephalograph, electromyograph, ground reaction force, and symptom measurement sensors for the exploration of neural biomarkers during active gait paradigms. Additionally, this study employed novel de-noising techniques for dealing with the motion artifacts associated with active gait EEG recordings and compared time-frequency features between a group of PD with FoG and a group of age-matched controls and found significant differences between several EEG frequency bands during start and end of normal walking (with a p\u3c0.05)