19 research outputs found
ARTIFACT CHARACTERIZATION, DETECTION AND REMOVAL FROM NEURAL SIGNALS
Ph.DDOCTOR OF PHILOSOPH
Lie Detection Based EEG-P300 Signal Classified by ANFIS Method
In this paper, the differences in brain signal activity (EEG-P300 component) which detects whether a person is telling the truth or lying is explored. Brain signal activity is monitored when they are first respond to a given experiment stimulus. In the experiment, twelve subjects whose age are around 19 ± 1 years old were involved. In the signal processing, the recorded brain signals were filtered and extracted using bandpass filter and independent component analysis, respectively. Furthermore, the extracted signals were classified with adaptive neuro-fuzzy inference system method. The results show that a huge spike of the EEG-P300 amplitude on a lying subject correspond to the appeared stimuli is achieved. The findings of these experiments have been promising in testing the validity of using an EEG-P300 as a lie detector
Automated and Reliable Low-Complexity SoC Design Methodology for EEG Artefacts Removal
EEG is a non-invasive tool for neurodevelopmental disorder diagnosis (NDD) and treatment. However,
EEG signal is mixed with other biological signals including Ocular and Muscular artefacts making
it difficult to extract the diagnostic features. Therefore, the contaminated EEG channels are often discarded
by the medical practitioners which may result in less accurate diagnosis. Independent Component
Analysis (ICA) and wavelet-based algorithms require reference electrodes, which will create discomfort
to the patient/children and cause hindrance to the diagnosis of the NDD and Brain Computer Interface
(BCI). Therefore, it would be ideal if these artefacts can be removed real time and on hardware platform
in an automated fashion and denoised EEG can be used for online diagnosis in a pervasive personalised
healthcare environment without the need of any reference electrode. In this thesis we propose a reliable,
robust and automated methodology to solve the aforementioned problem and its subsequent hardware
implementation results are also presented. 100 EEG data from Physionet, Klinik fur Epileptologie, Universitat
Bonn, Germany, Caltech EEG databases and 3 EEG data from 3 subjects from University of
Southampton, UK have been studied and nine exhaustive case studies comprising of real and simulated
data have been formulated and tested. The performance of the proposed methodology is measured in
terms of correlation, regression and R-square statistics and the respective values lie above 80%, 79% and
65% with the gain in hardware complexity of 64.28% and hardware delay 53.58% compared to state-ofthe
art approach. We believe the proposed methodology would be useful in next generation of pervasive
healthcare for BCI and NDD diagnosis and treatment
Motion Artifact Processing Techniques for Physiological Signals
The combination of reducing birth rate and increasing life expectancy continues to drive
the demographic shift toward an ageing population and this is placing an ever-increasing
burden on our healthcare systems. The urgent need to address this so called healthcare
\time bomb" has led to a rapid growth in research into ubiquitous, pervasive and
distributed healthcare technologies where recent advances in signal acquisition, data
storage and communication are helping such systems become a reality. However, similar
to recordings performed in the hospital environment, artifacts continue to be a major
issue for these systems. The magnitude and frequency of artifacts can vary signicantly
depending on the recording environment with one of the major contributions due to
the motion of the subject or the recording transducer. As such, this thesis addresses
the challenges of the removal of this motion artifact removal from various physiological
signals.
The preliminary investigations focus on artifact identication and the tagging of physiological
signals streams with measures of signal quality. A new method for quantifying
signal quality is developed based on the use of inexpensive accelerometers which facilitates
the appropriate use of artifact processing methods as needed. These artifact
processing methods are thoroughly examined as part of a comprehensive review of the
most commonly applicable methods. This review forms the basis for the comparative
studies subsequently presented. Then, a simple but novel experimental methodology
for the comparison of artifact processing techniques is proposed, designed and tested
for algorithm evaluation. The method is demonstrated to be highly eective for the
type of artifact challenges common in a connected health setting, particularly those concerned
with brain activity monitoring. This research primarily focuses on applying the
techniques to functional near infrared spectroscopy (fNIRS) and electroencephalography
(EEG) data due to their high susceptibility to contamination by subject motion related
artifact.
Using the novel experimental methodology, complemented with simulated data, a comprehensive
comparison of a range of artifact processing methods is conducted, allowing
the identication of the set of the best performing methods. A novel artifact removal
technique is also developed, namely ensemble empirical mode decomposition with canonical
correlation analysis (EEMD-CCA), which provides the best results when applied on
fNIRS data under particular conditions. Four of the best performing techniques were
then tested on real ambulatory EEG data contaminated with movement artifacts comparable
to those observed during in-home monitoring.
It was determined that when analysing EEG data, the Wiener lter is consistently
the best performing artifact removal technique. However, when employing the fNIRS
data, the best technique depends on a number of factors including: 1) the availability
of a reference signal and 2) whether or not the form of the artifact is known. It is
envisaged that the use of physiological signal monitoring for patient healthcare will grow
signicantly over the next number of decades and it is hoped that this thesis will aid in
the progression and development of artifact removal techniques capable of supporting
this growth
IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)
Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.Peer reviewe
Intelligent Biosignal Analysis Methods
This book describes recent efforts in improving intelligent systems for automatic biosignal analysis. It focuses on machine learning and deep learning methods used for classification of different organism states and disorders based on biomedical signals such as EEG, ECG, HRV, and others
Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration
One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy
Optimizing Common Spatial Pattern for a Motor Imagerybased BCI by Eigenvector Filteration
One of the fundamental criterion for the successful application of a brain-computer interface (BCI) system is to extract significant features that confine invariant characteristics specific to each brain state. Distinct features play an important role in enabling a computer to associate different electroencephalogram (EEG) signals to different brain states. To ease the workload on the feature extractor and enhance separability between different brain states, the data is often transformed or filtered to maximize separability before feature extraction. The common spatial patterns (CSP) approach can achieve this by linearly projecting the multichannel EEG data into a surrogate data space by the weighted summation of the appropriate channels. However, choosing the optimal spatial filters is very significant in the projection of the data and this has a direct impact on classification. This paper presents an optimized pattern selection method from the CSP filter for improved classification accuracy. Based on the hypothesis that values closer to zero in the CSP filter introduce noise rather than useful information, the CSP filter is modified by analyzing the CSP filter and removing/filtering the degradative or insignificant values from the filter. This hypothesis is tested by comparing the BCI results of eight subjects using the conventional CSP filters and the optimized CSP filter. In majority of the cases the latter produces better performance in terms of the overall classification accuracy
INVESTIGATION, DEVELOPMENT AND APPLICATION OF KNOWLEDGE BASED DIGITAL SIGNAL PROCESSING METHODS FOR ENHANCING HUMAN EEGsJ
This thesis details the development of new and reliable techniques
for enhancing the human Electroencephalogram {EEGI. This development has
involved the incorporation of adaptive signal processing (ASP) techniques,
within an artificial intelligence (Al) paradigm, more closely matching the
implicit signal analysis capabilities of the EEG expert.
The need for EEG enhancement, by removal of ocular artefact (OA) , is
widely recognised. However, conventional ASP techniques for OA removal
fail to differentiate between OAs and some abnormal cerebral waveforms,
such as frontal slow waves. OA removal often results in the corruption of
these diagnostically important cerebral waveforms. However, the
experienced EEG expert is often able to differentiate between OA and
abnormal slow waveforms, and between different types of OA. This EEG
expert knowledge is integrated with selectable adaptive filters in an
intelligent OA removal system (tOARS). The EEG is enhanced by only
removing OA when OA is identified, and by applying the OA removal
algorithm pre-set for the specific OA type.
Extensive EEG data acquisition has provided a database of abnormal EEG
recordings from over 50 patients, exhibiting a variety of cerebral
abnormalities. Structured knowledge elicitation has provided over 60
production rules for OA identification in the presence of abnormal frontal
slow waveforms, and for distinguishing between OA types.
The lOARS was implemented on personal computer (PCI based hardware in
PROLOG and C software languages. 2-second, 18-channel, EEG signal segments
are subjected to digital signal processing, to extract salient features
from time, frequency, and contextual domains. OA is identified using a
forward/backward hybrid inference engine, with uncertainty management,
using the elicited expert rules and extracted signal features.
Evaluation of the system has been carried out using both normal and
abnormal patient EEGs, and this shows a high agreement (82.7%) in OA
identification between the lOARS and an EEG expert. This novel development
provides a significant improvement in OA removal, and EEG signal
enhancement, and will allow more reliable automated EEG analysis.
The investigation detailed in this thesis has led to 4 papers,
including one in a special proceedings of the lEE, and been subject to
several review articles.Department of Neurophysiology,
Derriford Hospital, Plymouth, Devo