Real-Time Electroencephalogram Sonification for Neurofeedback

Electroencephalography (EEG) is the measurement via the scalp of the electrical activity of the brain. The established therapeutic intervention of neurofeedback involves presenting people with their own EEG in real-time to enable them to modify their EEG for purposes of improving performance or health. The aim of this research is to develop and validate real-time sonifications of EEG for use in neurofeedback and methods for assessing such sonifications. Neurofeedback generally uses a visual display. Where auditory feedback is used, it is mostly limited to pre-recorded sounds triggered by the EEG activity crossing a threshold. However, EEG generates time-series data with meaningful detail at fine temporal resolution and with complex temporal dynamics. Human hearing has a much higher temporal resolution than human vision, and auditory displays do not require people to focus on a screen with their eyes open for extended periods of time – e.g. if they are engaged in some other task. Sonification of EEG could allow more rapid, contingent, salient and temporally detailed feedback. This could improve the efficiency of neurofeedback training and reduce the number and duration of sessions for successful neurofeedback. The same two deliberately simple sonification techniques were used in all three experiments of this research: Amplitude Modulation (AM) sonification, which maps the fluctuations in the power of the EEG to the volume of a pure tone; and Frequency Modulation (FM) sonification, which uses the changes in the EEG power to modify the frequency. Measures included, a listening task, NASA task load index; a measure of how much work it was to do the task, Pre & post measures of mood, and EEG. The first experiment used pre-recorded single channel EEG and participants were asked to listen to the sound of the sonified EEG and try and track the activity that they could hear by moving a slider on a computer screen using a computer mouse. This provided a quantitative assessment of how well people could perceive the sonified fluctuations in EEG level. The tracking accuracy scores were higher for the FM sonification but self-assessments of task load rated the AM sonification as easier to track. The second experiment used the same two sonifications, in a real neurofeedback task using participants own live EEG. Unbeknownst to the participants the neurofeedback task was designed to improve mood. A Pre-Post questionnaire showed that participants changed their self-rated mood in the intended direction with the EEG training, but there was no statistically significant change in EEG. Again the FM sonification showed a better performance but AM was rated as less effortful. The performance of sonifications in the tracking task in experiment 1 was found to predict their relative efficacy at blind self-rated mood modification in experiment 2. The third experiment used both the tracking as in experiment 1 and neurofeedback tasks as in experiment 2, but with modified versions of the AM and FM sonifications to allow two-channel EEG sonifications. This experiment introduced a physical slider as opposed to a mouse for the tracking task. Tracking accuracy increased, but this time no significant difference was found between the two sonification techniques on the tracking task. In the training task, once more the blind self-rated mood did improve in the intended direction with the EEG training, but as again there was no significant change in EEG, this cannot necessarily be attributed to the neurofeedback. There was only a slight difference between the two sonification techniques in the effort measure. In this way, a prototype method has been devised and validated for the quantitative assessment of real-time EEG sonifications. Conventional evaluations of neurofeedback techniques are expensive and time consuming. By contrast, this method potentially provides a rapid, objective and efficient method for evaluating the suitability of candidate sonifications for EEG neurofeedback

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy. This edition celebrates twenty years of uninterrupted and succesfully research in the field of voice analysis

Towards developing a reliable medical device for automated epileptic seizure detection in the ICU

Abstract. Epilepsy is a prevalent neurological disorder that affects millions of people globally, and its diagnosis typically involves laborious manual inspection of electroencephalography (EEG) data. Automated detection of epileptic seizures in EEG signals could potentially improve diagnostic accuracy and reduce diagnosis time, but there should be special attention to the number of false alarms to reduce unnecessary treatments and costs. This research presents a study on the use of machine learning techniques for EEG seizure detection with the aim of investigating the effectiveness of different algorithms in terms of high sensitivity and low false alarm rates for feature extraction, selection, pre-processing, classification, and post-processing in designing a medical device for detecting seizure activity in EEG data. The current state-of-the-art methods which are validated clinically using large amounts of data are introduced. The study focuses on finding potential machine learning methods, considering KNN, SVM, decision trees and, Random forests, and compares their performance on the task of seizure detection using features introduced in the literature. Also using ensemble methods namely, bootstrapping and majority voting techniques we achieved a sensitivity of 0.80 and FAR/h of 2.10, accuracy of 97.1% and specificity of 98.2%. Overall, the findings of this study can be useful for developing more accurate and efficient algorithms for EEG seizure detection medical device, which can contribute to the early diagnosis and treatment of epilepsy in the intensive care unit for critically ill patients

Models and Analysis of Vocal Emissions for Biomedical Applications

The Models and Analysis of Vocal Emissions with Biomedical Applications (MAVEBA) workshop came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the neonate to the adult and elderly. Over the years the initial issues have grown and spread also in other aspects of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years always in Firenze, Italy

Sonic Skills

It is common for us today to associate the practice of science primarily with the act of seeing—with staring at computer screens, analyzing graphs, and presenting images. We may notice that physicians use stethoscopes to listen for disease, that biologists tune into sound recordings to understand birds, or that engineers have created Geiger tellers warning us for radiation through sound. But in the sciences overall, we think, seeing is believing. This open access book explains why, indeed, listening for knowledge plays an ambiguous, if fascinating, role in the sciences. For what purposes have scientists, engineers and physicians listened to the objects of their interest? How did they listen exactly? And why has listening often been contested as a legitimate form of access to scientific knowledge? This concise monograph combines historical and ethnographic evidence about the practices of listening on shop floors, in laboratories, field stations, hospitals, and conference halls, between the 1920s and today. It shows how scientists have used sonic skills—skills required for making, recording, storing, retrieving, and listening to sound—in ensembles: sets of instruments and techniques for particular situations of knowledge making. Yet rather than pleading for the emancipation of hearing at the expense of seeing, this essay investigates when, how, and under which conditions the ear has contributed to science dynamics, either in tandem with or without the eye

Paraneoplastic Antibodies

Paraneoplastic neurological syndromes (PNS) are remote effects of cancer. These are not caused by invasion of the tumor or its metastasis nor by other direct effects of the tumor or its treatment. PNS are rare, affecting less than 0.1% of all cancer patients. PNS have a subacute course, leaving the patient severely disabled in weeks to months. In most cases PNS precede the diagnosis of cancer. The discovery of paraneoplastic antineuronal antibodies facilitated the diagnosis. ‘Well characterized onconeural antibodies’ are defined by recognizable patterns on rat brain immunohistochemistry and positive immunoblotting on recombinant antigen proteins. These are exclusively found in patients with cancer and include anti-Hu, Yo, CV2, Ri, Ma2 and amphiphysin. These antigens represent intracellular proteins, so in PNS damage is caused by cellular immune responses, explaining the poor response to immune modulating treatment and poor prognosis. We identified the anti-Tr antigen as the transmembrane protein Delta/ Notch-like epidermal growth factor-related receptor (DNER). More recently a still growing number of autoantibodies directed against synaptic or neuronal cell-surface antigens has been identified, including mGluR1, NMDA, AMPA and GABA receptors. These autoantibodies have direct access to their target antigen and are potentially pathogenic. The associated clinical syndromes may be paraneoplastic or may represent an autoimmune encephalitis (without underlying tumor). Patients harboring autoantibodies directed against synaptic or neuronal cell-surface antigens respond favorably to immunotherapy with a good outcome in up to 80%. This thesis focuses on paraneoplastic antineuronal antibodies and includes studies on new methods of autoantibody detection, identification of novel paraneoplastic antigen(s) and the description of clinical syndromes associated with newly detected paraneoplastic antibodies