Corporeal prisons: dynamics of body and mise-en-scène in three films by Paul Schrader

This thesis focuses on the complex relationship between representations of the human body and the formal processes of mise-en-scène in three consecutive films by the writer-director Paul Schrader: American Gigolo (1980), Cat People (1982) and Mishima: A Life in Four Chapters (1985). While Schrader’s work has typically been critiqued under the broad category of masculinity in crisis (and often as a subset of the films of his more famous long-time collaborator, Martin Scorsese), I focus on a fiveyear early period of his filmography when he sought to explore his key themes of bodily crisis, fragmentation and alienation through an unusually intense focus upon the expressive potential of film form, specifically via the combined elements of colour, lighting, camerawork and production design. By approaching these three films as corporeal character studies of troubled figures whose emotional and psychosexual neurosis is experienced in and through the body, I will locate Schrader’s filmmaking process and style within the thematic and aesthetic contexts of both his own early film criticism and the European and Japanese art cinemas that he claims as his primary influence. In doing so, I will establish Schrader’s position as a director whose literary and theological background differentiated him from his peers of the postclassical Hollywood generation, and who thus continually sought to develop his own visual literacy through his relationship with the camera and his collaborations with more overtly style-oriented film artists. But instead of merely focusing on mise-en-scène to gain a formalist appreciation of these films, I mobilise stylistic analysis as a new critical approach towards the problematic discourses of identity and embodiment that have haunted Schrader’s career from the beginning. In particular, I argue that paying closer attention to Schrader’s formal choices sheds new light on how these films – which he approached as exercises in style – repeatedly deal with the volatile and unavoidably body-oriented categories of race, gender and sexuality. In the process, I argue that a formalist attentiveness to mise-en-scène can also provide valuable cultural insights into Schrader’s oeuvre.

Artefact detection and removal algorithms for EEG diagnostic systems

The electroencephalogram (EEG) is a medical technology that is used in the monitoring of the brain and in the diagnosis of many neurological illnesses. Although coarse in its precision, the EEG is a non-invasive tool that requires minimal set-up times, and is suitably unobtrusive and mobile to allow continuous monitoring of the patient, either in clinical or domestic environments. Consequently, the EEG is the current tool-of-choice with which to continuously monitor the brain where temporal resolution, ease-of- use and mobility are important. Traditionally, EEG data are examined by a trained clinician who identifies neurological events of interest. However, recent advances in signal processing and machine learning techniques have allowed the automated detection of neurological events for many medical applications. In doing so, the burden of work on the clinician has been significantly reduced, improving the response time to illness, and allowing the relevant medical treatment to be administered within minutes rather than hours. However, as typical EEG signals are of the order of microvolts (μV ), contamination by signals arising from sources other than the brain is frequent. These extra-cerebral sources, known as artefacts, can significantly distort the EEG signal, making its interpretation difficult, and can dramatically disimprove automatic neurological event detection classification performance. This thesis therefore, contributes to the further improvement of auto- mated neurological event detection systems, by identifying some of the major obstacles in deploying these EEG systems in ambulatory and clinical environments so that the EEG technologies can emerge from the laboratory towards real-world settings, where they can have a real-impact on the lives of patients. In this context, the thesis tackles three major problems in EEG monitoring, namely: (i) the problem of head-movement artefacts in ambulatory EEG, (ii) the high numbers of false detections in state-of-the-art, automated, epileptiform activity detection systems and (iii) false detections in state-of-the-art, automated neonatal seizure detection systems. To accomplish this, the thesis employs a wide range of statistical, signal processing and machine learning techniques drawn from mathematics, engineering and computer science. The first body of work outlined in this thesis proposes a system to automatically detect head-movement artefacts in ambulatory EEG and utilises supervised machine learning classifiers to do so. The resulting head-movement artefact detection system is the first of its kind and offers accurate detection of head-movement artefacts in ambulatory EEG. Subsequently, addtional physiological signals, in the form of gyroscopes, are used to detect head-movements and in doing so, bring additional information to the head- movement artefact detection task. A framework for combining EEG and gyroscope signals is then developed, offering improved head-movement arte- fact detection. The artefact detection methods developed for ambulatory EEG are subsequently adapted for use in an automated epileptiform activity detection system. Information from support vector machines classifiers used to detect epileptiform activity is fused with information from artefact-specific detection classifiers in order to significantly reduce the number of false detections in the epileptiform activity detection system. By this means, epileptiform activity detection which compares favourably with other state-of-the-art systems is achieved. Finally, the problem of false detections in automated neonatal seizure detection is approached in an alternative manner; blind source separation techniques, complimented with information from additional physiological signals are used to remove respiration artefact from the EEG. In utilising these methods, some encouraging advances have been made in detecting and removing respiration artefacts from the neonatal EEG, and in doing so, the performance of the underlying diagnostic technology is improved, bringing its deployment in the real-world, clinical domain one step closer.