Development of Methodologies for the Solution of the Forward Problem in Magnetic-Field Tomography (MFT) Based on Magnetoencephalography (MEG)

The prime topic of research presented in this report is the development and validation of methodologies for the solution of the forward problem in Magnetic field Tomography based on Magnetoencephalography. Throughout the report full aspects of the accurate solution are discussed, including the development of algorithms and methods for realistic brain model, development of realistic neuronal source, computational approaches, and validation techniques. Every delivered methodology is tested and analyzed in terms of mathematical and computational errors. Optimizations required for error minimization are performed and discussed. Presented techniques are successfully integrated together for different test problems. Results were compared to experimental data where possible for the most of calculated cases. Designed human brain model reconstruction algorithms and techniques, which are based on MRI (Magnetic Resonance Imaging) modality, are proved to be the most accurate among existing in terms of geometrical and material properties. Error estimations and algorithm structure delivers the resolution of the model to be the same as practical imaging resolution of the MRI equipment (for presented case was less than 1mm). Novel neuronal source modelling approach was also presented with partial experimental validation showing improved results in comparison to all existing methods. At the same time developed mathematical basis for practical realization of discussed approach allows computer simulations of any known neuronal formation. Also it is the most suitable method for Finite Element Method (FEM) which was proved to be the best computer solver for complex bio-electrical problems. The mathematical structure for Inverse problem solution which is based on integrated human brain modelling technique and neuronal source modelling approach is delivered and briefly discussed. In the concluding part of the report the practical application case of developed techniques is performed and discussed

Development of methodologies for the solution of the forward problem in magnetic-field tomography (MFT) based on magnetoencephalography (MEG)

The prime topic of research presented in this report is the development and validation of methodologies for the solution of the forward problem in Magnetic field Tomography based on Magnetoencephalography. Throughout the report full aspects of the accurate solution are discussed, including the development of algorithms and methods for realistic brain model, development of realistic neuronal source, computational approaches, and validation techniques. Every delivered methodology is tested and analyzed in terms of mathematical and computational errors. Optimizations required for error minimization are performed and discussed. Presented techniques are successfully integrated together for different test problems. Results were compared to experimental data where possible for the most of calculated cases. Designed human brain model reconstruction algorithms and techniques, which are based on MRI (Magnetic Resonance Imaging) modality, are proved to be the most accurate among existing in terms of geometrical and material properties. Error estimations and algorithm structure delivers the resolution of the model to be the same as practical imaging resolution of the MRI equipment (for presented case was less than 1mm). Novel neuronal source modelling approach was also presented with partial experimental validation showing improved results in comparison to all existing methods. At the same time developed mathematical basis for practical realization of discussed approach allows computer simulations of any known neuronal formation. Also it is the most suitable method for Finite Element Method (FEM) which was proved to be the best computer solver for complex bio-electrical problems. The mathematical structure for Inverse problem solution which is based on integrated human brain modelling technique and neuronal source modelling approach is delivered and briefly discussed. In the concluding part of the report the practical application case of developed techniques is performed and discussed.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Adaptive techniques for the detection and localization of event related potentials from EEGs using reference signals

In this thesis we show the methods we developed for the detection and localisation of P300 signals from the electroencephalogram. We utilised signal processing theory in order to enhance the current methodology. The work done can be applied both to EEG averages and single trial EEG data. We developed a variety of methods dealing with the extraction of the P300 and its subcomponents using independent component analysis and least squares. Moreover, we developed novel localisation methods that localise the desired P300 subcomponent from EEG data. Throughout the thesis the main idea was the use of reference signals, which describe the prior information we have about the sources of interest. The main objective of this thesis is to utilize adaptive techniques, namely blind source separation (BSS), least squares (LS) and spatial filtering, in order to extract the P300 subcomponents from the electroencephalogram (EEG) with greater accuracy than the traditional methods. The first topic of research, is the development of constrained BSS and blind signal extraction (BSE) algorithms, to enhance the estimation of the conventional BSS and BSE algorithms. In these methods we use reference signals as prior information, obtained from real EEG data, to aid BSS and BSE in the extraction of the P300 subcomponents. Although, this method exhibits very good behaviour in terms of EEG averaged data, its performance degrades when applied to single trial data, which is the response of the brain after one single stimulus. The second topic deals with single trial EEG data and is based on least squares. Again, we use reference signals to describe the prior knowledge of the P300 subcomponents. In contrast to the first method, the reference signals are Gaussian spike templates with variable latency and width. The target of this algorithm is to measure the properties of the extracted P300 subcomponents and obtain features that can be used in the classification of schizophrenic patients and healthy subjects. Finally, the idea of spatial filtering combined with the use of a reference signal for localisation is introduced for the first time. The designed algorithm localises our desired source from within a mixture of sources where the propagation model of the sources is available. It performs well in the presence of noise and correlated sources. The research presented in this thesis paves the path in introducing adaptive techniques based on reference signals into ERP estimation. The results have been very promising and provide a big step in establishing a foundation for future research

Brain signal analysis in space-time-frequency domain : an application to brain computer interfacing

In this dissertation, advanced methods for electroencephalogram (EEG) signal analysis in the space-time-frequency (STF) domain with applications to eye-blink (EB) artifact removal and brain computer interfacing (BCI) are developed. The two methods for EB artifact removal from EEGs are presented which respectively include the estimated spatial signatures of the EB artifacts into the signal extraction and the robust beamforming frameworks. In the developed signal extraction algorithm, the EB artifacts are extracted as uncorrelated signals from EEGs. The algorithm utilizes the spatial signatures of the EB artifacts as priori knowledge in the signal extraction stage. The spatial distributions are identified using the STF model of EEGs. In the robust beamforming approach, first a novel space-time-frequency/time-segment (STF-TS) model for EEGs is introduced. The estimated spatial signatures of the EBs are then taken into account in order to restore the artifact contaminated EEG measurements. Both algorithms are evaluated by using the simulated and real EEGs and shown to produce comparable results to that of conventional approaches. Finally, an effective paradigm for BCI is introduced. In this approach prior physiological knowledge of spectrally band limited steady-state movement related potentials is exploited. The results consolidate the method.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Mathematical modelling of magnetoencephalographic data

Heethaar, R.M. [Promotor]Munck, J.C. de [Copromotor

Brain signal analysis in space-time-frequency domain: an application to brain computer interfacing

In this dissertation, advanced methods for electroencephalogram (EEG) signal analysis in the space-time-frequency (STF) domain with applications to eye-blink (EB) artifact removal and brain computer interfacing (BCI) are developed. The two methods for EB artifact removal from EEGs are presented which respectively include the estimated spatial signatures of the EB artifacts into the signal extraction and the robust beamforming frameworks. In the developed signal extraction algorithm, the EB artifacts are extracted as uncorrelated signals from EEGs. The algorithm utilizes the spatial signatures of the EB artifacts as priori knowledge in the signal extraction stage. The spatial distributions are identified using the STF model of EEGs. In the robust beamforming approach, first a novel space-time-frequency/time-segment (STF-TS) model for EEGs is introduced. The estimated spatial signatures of the EBs are then taken into account in order to restore the artifact contaminated EEG measurements. Both algorithms are evaluated by using the simulated and real EEGs and shown to produce comparable results to that of conventional approaches. Finally, an effective paradigm for BCI is introduced. In this approach prior physiological knowledge of spectrally band limited steady-state movement related potentials is exploited. The results consolidate the method

Procesamiento de señales e imágenes biomédicas para el estudio de la actividad cerebral

En esta tesis se estudian distintos aspectos que influyen en la calidad de la solución de los problemas directo e inverso de la electro/magnetoencefalografía, así como problemas de estimación relacionados a las imágenes de resonancia magnética por tensor de difusión. Se analizan los efectos de variaciones en el modelo de cabeza utilizado, en el posicionamiento de los electrodos y la modelización de la actividad cerebral de fondo. Se estudia también la influencia del ruido propio del sistema de adquisición en imágenes de tensor de difusión y mediciones derivadas de éste. Tales influencias se plasman en errores en la estimación de la conductividad eléctrica, necesaria para la adecuada modelización de la cabeza, así como en la estimación de la geometría estructural intracerebral, denominada tractografía.Facultad de Ingenierí