A filtering approach to underdetermined blind source separation with application to temporomandibular disorders

This paper addresses the underdetermined blind source separation problem, using a filtering approach. We have developed an extension of the FastICA algorithm which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter the recovery of the sources is achieved by employing the l1-norm algorithm. Also, we demonstrate how promising FastICA can be to extract the sources, without utilizing the l1-norm algorithm. Furthermore, we illustrate how this scenario is particularly suitable to the separation of the temporomandibular joint (TMJ) sounds, crucial in the diagnosis of temporomandibular disorders (TMDs

Blind source separation via independent and sparse component analysis with application to temporomandibular disorder

Blind source separation (BSS) addresses the problem of separating multi channel signals observed by generally spatially separated sensors into their constituent underlying sources. The passage of these sources through an unknown mixing medium results in these observed multichannel signals. This study focuses on BSS, with special emphasis on its application to the temporomandibular joint disorder (TMD). TMD refers to all medical problems related to the temporomandibular joint (TMJ), which holds the lower jaw (mandible) and the temporal bone (skull). The overall objective of the work is to extract the two TMJ sound sources generated by the two TMJs, from the bilateral recordings obtained from the auditory canals, so as to aid the clinician in diagnosis and planning treatment policies. Firstly, the concept of 'variable tap length' is adopted in convolutive blind source separation. This relatively new concept has attracted attention in the field of adaptive signal processing, notably the least mean square (LMS) algorithm, but has not yet been introduced in the context of blind signal separation. The flexibility of the tap length of the proposed approach allows for the optimum tap length to be found, thereby mitigating computational complexity or catering for fractional delays arising in source separation. Secondly, a novel fixed point BSS algorithm based on Ferrante's affine transformation is proposed. Ferrante's affine transformation provides the freedom to select the eigenvalues of the Jacobian matrix of the fixed point function and thereby improves the convergence properties of the fixed point iteration. Simulation studies demonstrate the improved convergence of the proposed approach compared to the well-known fixed point FastICA algorithm. Thirdly, the underdetermined blind source separation problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter achieves source recovery by employing the i-norm algorithm. Additionally, it will be shown that FastICA can also be utilised to extract the sources. Furthermore, it is illustrated how this scenario is particularly suitable for the separation of TMJ sounds. Finally, estimation of fractional delays between the mixtures of the TMJ sources is proposed as a means for TMJ separation. The estimation of fractional delays is shown to simplify the source separation to a case of in stantaneous BSS. Then, the estimated delay allows for an alignment of the TMJ mixtures, thereby overcoming a spacing constraint imposed by a well- known BSS technique, notably the DUET algorithm. The delay found from the TMJ bilateral recordings corroborates with the range reported in the literature. Furthermore, TMJ source localisation is also addressed as an aid to the dental specialist.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Blind source separation via independent and sparse component analysis with application to temporomandibular disorder

Blind source separation (BSS) addresses the problem of separating multi channel signals observed by generally spatially separated sensors into their constituent underlying sources. The passage of these sources through an unknown mixing medium results in these observed multichannel signals. This study focuses on BSS, with special emphasis on its application to the temporomandibular joint disorder (TMD). TMD refers to all medical problems related to the temporomandibular joint (TMJ), which holds the lower jaw (mandible) and the temporal bone (skull). The overall objective of the work is to extract the two TMJ sound sources generated by the two TMJs, from the bilateral recordings obtained from the auditory canals, so as to aid the clinician in diagnosis and planning treatment policies. Firstly, the concept of 'variable tap length' is adopted in convolutive blind source separation. This relatively new concept has attracted attention in the field of adaptive signal processing, notably the least mean square (LMS) algorithm, but has not yet been introduced in the context of blind signal separation. The flexibility of the tap length of the proposed approach allows for the optimum tap length to be found, thereby mitigating computational complexity or catering for fractional delays arising in source separation. Secondly, a novel fixed point BSS algorithm based on Ferrante's affine transformation is proposed. Ferrante's affine transformation provides the freedom to select the eigenvalues of the Jacobian matrix of the fixed point function and thereby improves the convergence properties of the fixed point iteration. Simulation studies demonstrate the improved convergence of the proposed approach compared to the well-known fixed point FastICA algorithm. Thirdly, the underdetermined blind source separation problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter achieves source recovery by employing the i-norm algorithm. Additionally, it will be shown that FastICA can also be utilised to extract the sources. Furthermore, it is illustrated how this scenario is particularly suitable for the separation of TMJ sounds. Finally, estimation of fractional delays between the mixtures of the TMJ sources is proposed as a means for TMJ separation. The estimation of fractional delays is shown to simplify the source separation to a case of in stantaneous BSS. Then, the estimated delay allows for an alignment of the TMJ mixtures, thereby overcoming a spacing constraint imposed by a well- known BSS technique, notably the DUET algorithm. The delay found from the TMJ bilateral recordings corroborates with the range reported in the literature. Furthermore, TMJ source localisation is also addressed as an aid to the dental specialist.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Blind source separation via independent and sparse component analysis with application to temporomandibular disorder

Blind source separation (BSS) addresses the problem of separating multi channel signals observed by generally spatially separated sensors into their constituent underlying sources. The passage of these sources through an unknown mixing medium results in these observed multichannel signals. This study focuses on BSS, with special emphasis on its application to the temporomandibular joint disorder (TMD). TMD refers to all medical problems related to the temporomandibular joint (TMJ), which holds the lower jaw (mandible) and the temporal bone (skull). The overall objective of the work is to extract the two TMJ sound sources generated by the two TMJs, from the bilateral recordings obtained from the auditory canals, so as to aid the clinician in diagnosis and planning treatment policies. Firstly, the concept of 'variable tap length' is adopted in convolutive blind source separation. This relatively new concept has attracted attention in the field of adaptive signal processing, notably the least mean square (LMS) algorithm, but has not yet been introduced in the context of blind signal separation. The flexibility of the tap length of the proposed approach allows for the optimum tap length to be found, thereby mitigating computational complexity or catering for fractional delays arising in source separation. Secondly, a novel fixed point BSS algorithm based on Ferrante's affine transformation is proposed. Ferrante's affine transformation provides the freedom to select the eigenvalues of the Jacobian matrix of the fixed point function and thereby improves the convergence properties of the fixed point iteration. Simulation studies demonstrate the improved convergence of the proposed approach compared to the well-known fixed point FastICA algorithm. Thirdly, the underdetermined blind source separation problem using a filtering approach is addressed. An extension of the FastICA algorithm is devised which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter achieves source recovery by employing the i-norm algorithm. Additionally, it will be shown that FastICA can also be utilised to extract the sources. Furthermore, it is illustrated how this scenario is particularly suitable for the separation of TMJ sounds. Finally, estimation of fractional delays between the mixtures of the TMJ sources is proposed as a means for TMJ separation. The estimation of fractional delays is shown to simplify the source separation to a case of in stantaneous BSS. Then, the estimated delay allows for an alignment of the TMJ mixtures, thereby overcoming a spacing constraint imposed by a well- known BSS technique, notably the DUET algorithm. The delay found from the TMJ bilateral recordings corroborates with the range reported in the literature. Furthermore, TMJ source localisation is also addressed as an aid to the dental specialist

Effects of pain catastrophising on behavioural and cortical responses to pain-related stimuli

Pain catastrophising is an exaggerated negative mental set brought to bear during actual or anticipated pain experience (Sullivan et al., 2001b). People with high pain catastrophising were reported to perceive stronger pain intensity, attribute more pain to others, and solicit higher levels of social support from others when exposed to pain, relative to low pain catastrophisers (Sullivan et al., 2001b, Quartana et al., 2009). Three important models of pain catastrophising, the appraisal model, the attentional model, and the communal coping model, have been proposed to investigate the influence of pain catastrophising on pain-related outcomes. However, the neural basis of pain catastrophising in the social-emotional context among healthy people is poorly understood. This thesis utilised neuroimaging methods and novel experimental paradigms to explore effects of pain catastrophising on behavioural and cortical responses to pain-related stimuli in healthy people. It also investigates the associations between pain catastrophising and structural brain features. A comprehensive review of previous experimental findings was performed to identify novel research questions. Behavioural, eye movement, EEG and MRI data for 6 unique studies were collected. Chapter One features a review of relevant theories, studies, and findings pertaining to pain catastrophising. The specific research problems and hypotheses investigated in the thesis are explicitly described. Chapter Two describes the theory of the EEG, MRI and eye tracking methods used in the experimental chapters of the thesis. Chapter Three outlines the methods and materials used for each individual study. Chapter Four describes the experimental findings of the thesis. In the first study, a paradigm using a varying level of background noise was applied to evaluate the sensitivity to pain cues in high and low pain catastrophisers. No significant differences were found. In the second and third study, the eye tracking method and a dot-probe paradigm were used to measure the attentional processing to pain-related stimuli. High pain catastrophisers responded to probes after pain scenes slower compared to low pain catastrophisers. In the fourth study, ERP data revealed that high pain catastrophisers exhibited differences in ERP components and source activation patterns during the observation of pain pictures. The first four studies of this thesis reported that high pain catastrophisers attributed stronger pain to pain in others. In the fifth study, LEP data showed that high pain catastrophisers reduced perceived pain during viewing of comforting hand postures, and displayed enhanced ipsilateral operculo-insular activation to pictures not showing comforting gestures. In the final study of the thesis, a morphological analysis of cortical and subcortical structures was performed using high-resolution T1-weighted MR images. It demonstrated that alterations to the morphology of selected cortical regions and the dorsal striatum were associated with pain catastrophising. Chapter Five discusses the findings of each individual study in light of previous research and the implications and inferences that can be drawn from the data. Chapter Six represents a general discussion of the main findings of the thesis. This chapter examines how the findings of each individual study relate to the theories of pain catastrophising. The limitations of the thesis and the implications of the findings for future research are also discussed

A filtering approach to underdetermined blind source separation with application to temporomandibular disorders

This paper addresses the underdetermined blind source separation problem, using a filtering approach. We have developed an extension of the FastICA algorithm which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter the recovery of the sources is achieved by employing the ℓ1-norm algorithm. Also, we demonstrate how promising FastICA can be to extract the sources, without utilizing the ℓ1-norm algorithm. Furthermore, we illustrate how this scenario is particularly suitable to the separation of the temporomandibular joint (TMJ) sounds, crucial in the diagnosis of temporomandibular disorders (TMDs). © 2006 British Crown Copyright

A filtering approach to underdetermined blind source separation with application to temporomandibular disorders

This paper addresses the underdetermined blind source separation problem, using a filtering approach. We have developed an extension of the FastICA algorithm which exploits the disparity in the kurtoses of the underlying sources to estimate the mixing matrix and thereafter the recovery of the sources is achieved by employing the ℓ1-norm algorithm. Also, we demonstrate how promising FastICA can be to extract the sources, without utilizing the ℓ1-norm algorithm. Furthermore, we illustrate how this scenario is particularly suitable to the separation of the temporomandibular joint (TMJ) sounds, crucial in the diagnosis of temporomandibular disorders (TMDs). © 2006 British Crown Copyright