Brain-Computer Interface

Brain-computer interfacing (BCI) with the use of advanced artificial intelligence identification is a rapidly growing new technology that allows a silently commanding brain to manipulate devices ranging from smartphones to advanced articulated robotic arms when physical control is not possible. BCI can be viewed as a collaboration between the brain and a device via the direct passage of electrical signals from neurons to an external system. The book provides a comprehensive summary of conventional and novel methods for processing brain signals. The chapters cover a range of topics including noninvasive and invasive signal acquisition, signal processing methods, deep learning approaches, and implementation of BCI in experimental problems

Multimodal Imaging and Characterization of Biofilms

Bacterial infection is a rampant problem faced by the medical community. The bacteria gene pool is capable of adapting itself to changing conditions building biofilms to ensure the survival of progeny. This ability reduces the efficiency of antibiotics and protects the bacteria from immune system eradication, prompting the need for a technology capable of early detection of biofilms. The ability to non-invasively image and characterize bacterial biofilms in children during nasopharyngeal (NP) colonization with potential otopathogens and during acute otitis media, (AOM) would represent a significant advance. Identifying the properties of biofilms is a crucial step towards establishing a viable imaging detection plan. In this thesis work two modalities based on different imaging principles were used to study the properties of biofilms and map their progression based on quantitative metrics as a function of time. Systematic time studies were performed on three preparations of an isolated Haemophilus influenzae (NTHi) species, Streptococcus pneumoniae (Sp) and a combination of Haemophilus influenzae and Streptococcus pneumoniae (NTHi+Sp)in an in vitro environment (N=3). A 15 MHz ultrasound acquisition system was built to study the detection of biofilms with ultrasound. Various spectral parameters - peak frequency shift, bandwidth reduction, intercept, mid-band fit, and integrated backscatter coefficient (IBC) - were recorded in a time study of biofilm growth by the bacteria and underlying trends in the progression of these metrics were attributed to the biofilms construction of specific bacteria or the combination of 2 bacteria. The frequency content of the backscattered signal was compared to a theoretical Form Factor model to estimate the effective scatter size which was also used as a characterization metric for biofilm growth. To confirm the ultrasound observations a second imaging modality was applied. Confocal laser-scanning microscopy produces 3D high-resolution time resolved data. Volumetric analyses of confocal microscopy data were used to further define structural properties of biofilms and complement ultrasound-based findings

Improved quantification of perfusion in patients with cerebrovascular disease.

In recent years measurements of cerebral perfusion using bolus-tracking MRI have become common clinical practice in the diagnosis and management of patients with stroke and cerebrovascular disease. An active area of research is the development of methods to identify brain tissue that is at risk of irreversible damage, but amenable to salvage using reperfusion treatments, such as thrombolysis. However, the specificity and sensitivity of these methods are limited by the inaccuracies in the perfusion data. Accurate measurements of perfusion are difficult to obtain, especially in patients with cerebrovascular diseases. In particular, if the bolus of MR contrast is delayed and/or dispersed due to cerebral arterial abnormalities, perfusion is likely to be underestimated using the standard analysis techniques. The potential for such underestimation is often overlooked when using the perfusion maps to assess stroke patients. Since thrombolysis can increase the risk of haemorrhage, a misidentification of 'at-risk' tissue has potentially dangerous clinical implications. This thesis presents several methodologies which aim to improve the accuracy and interpretation of the analysed bolus-tracking data. Two novel data analysis techniques are proposed, which enable the identification of brain regions where delay and dispersion of the bolus are likely to bias the perfusion measurements. In this way true hypoperfusion can be distinguished from erroneously low perfusion estimates. The size of the perfusion measurement errors are investigated in vivo, and a parameterised characterisation of the bolus delay and dispersion is obtained. Such information is valuable for the interpretation of in vivo data, and for further investigation into the effects of abnormal vasculature on perfusion estimates. Finally, methodology is presented to minimise the perfusion measurement errors prevalent in patients with cerebrovascular diseases. The in vivo application of this method highlights the dangers of interpreting perfusion values independently of the bolus delay and dispersion