Neural Correlates of Binaural Interaction Using Aggregate-System Stimulation in Cochlear Implantees

The importance of binaural cues in auditory stream formation and sound source differentiation is widely accepted. When treating one ear with a cochlear implant (CI) the peripheral auditory system gets partially replaced and processing delays get added potentially, thus important interaural time encoding gets altered. This is a crucial problem because factors like the interaural time delay between the receiving ears are known to be responsible for facilitating such cues, e.g., sound source localization and separation. However, these effects are not fully understood, leaving a lack of systematic binaural fitting strategies with respect to an optimal binaural fusion. To gain new insights into such alterations, we suggest a novel method of free-field evoked auditory brainstem response (ABR) analysis in CI users. As a result, this method does not bypass the technically induced intrinsic delays of the hearing device while leaving the complete electrode array active, thus the most natural way of stimulation is provided and the comparable testing of real world stimuli gets facilitated. Unfortunately, ABRs acquired in CI users are additionally affected by the prominent artifact caused by their electrical stimulation, which severely distorts the desired neural response, thus challenging their analysis. To circumvent this problem, we further introduce a novel narrowband filtering CI artifact removal technique capable of obtaining neural correlates of ABRs in CI users. Consequently, we were able to compare brainstem-level responses collected of 12 CI users and 12 normal hearing listeners using two different stimuli (i.e., chirp and click) at four different intensities each, what comprises an adaption of the prominent brainstem evoked response audiometry serving as an additional evaluation criterion. We analyzed the responses using the average of 2,000 trials in combination with synchronized regularizations across them and found consistent results in their deflections and latencies, as well as in single trial relationships between both groups. This method provides a novel and unique perspective into the natural CI users’ brainstem-level responses and can be practical in future research regarding binaural interaction and fusion. Furthermore, the binaural interaction component (BIC), i.e., the arithmetical difference between the sum of both monaurally evoked ABRs and the binaurally evoked ABR, has been previously shown to be an objective indicator for binaural interaction. This component is unfortunately known to be rather fragile and as a result, a reliable, objective measure of binaural interaction in CI users does not exist to the present date. It is most likely that implantees would benefit from a reliable analysis of brainstem-level and subsequent higher-level binaural interaction, since this could objectively support fitting strategies with respect to a maximization of interaural integration. Therefore, we introduce a novel method capable of obtaining neural correlates of binaural interaction in bimodal CI users by combining recent advances in the field of fast, deconvolution-based ABR acquisitions with the introduced narrowband filtering technique. The proposed method shows a significant improvement in the magnitude of resulting BICs in 10 bimodal CI users and a control-group of 10 normal hearing subjects when compensating the interaural latency difference caused by the technical devices. In total, both proposed studies objectively demonstrate technical-driven interaural latency mismatches. Thus, they strongly emphasize potential benefits when balancing these interaural delays to improve binaural processing by significant increases in associated neural correlates of successful binaural interaction. These results and also the estimated latency differences should be investigated in larger group sizes to further consolidate the results, but confirm the demand for rather binaural solutions than treating hearing losses in an isolated monaural manner.Zusammenfassung Die Notwendigkeit binauraler Verarbeitungsprozesse in der auditorischen Wahrnehmung ist weitestgehend akzeptiert. Bei der Therapie eines Ohres mit einem Cochlea-Implantat (engl. cochlear implant (CI)) wird das periphere auditorische System teilweise ersetzt und verändert, sodass natürliche, interaurale Zeitauflösungen beeinflusst werden. Dieses Problem ist entscheidend, denn Faktoren wie interaurale Laufzeitunterschiede zwischen den aufnehmenden Ohren sind verantwortlich für die Umsetzung der erwähnten binauralen Verarbeitungsprozesse, z.B. Schallquellenlokalisation und -separation. Allerdings sind diese Effekte nicht ausreichend verstanden, weshalb bis heute binaurale Anpassstrategien mit Rücksicht auf eine optimale Fusionierung fehlen. Um neue Einsichten in solche zeitlichen Verzerrungen zu erhalten, schlagen wir ein neues Verfahren der Freifeld evozierten auditorischen Hirnstammpotentiale (engl. auditory brainstem response (ABR)) in CI-Nutzern vor. Diese Methode beinhaltet explizit technisch-induzierte Laufzeiten verwendeter Hörhilfen, sodass eine natürliche Stimulation unter Verwendung von realitätsnahen Stimuli ermöglicht wird. Unglücklicherweise sind ABRs von CI-Nutzern zusätzlich mit Stimulationsartefakten belastet, wodurch benötigte neurale Antworten weiter verzerrt werden und eine entsprechende Analyse der Signale deutlich erschwert wird. Um dieses Problem zu umgehen, schlagen wir eine neue Artefakt- Reduktionstechnik vor, welche auf spektraler Schmalbandfilterung basiert und somit den Erhalt entsprechender, neuraler ABR Korrelate ermöglicht. Diese Methoden erlaubten die Interpretation neuraler Korrelate auf Hirnstammebene unter Verwendung von zwei verschiedenen Stimuli (Chirps und Klicks) unter vier verschiedenen Lautstärken in 12 CI-Nutzern und 12 normalhörenden Probanden. Die beschriebene Prozedur adaptiert somit die weitläufig bekannte Hirnstammaudiometrie (engl. brainstem evoked response audiometry (BERA)), deren Ergebnisse zur zusätzlichen Evaluation des vorgestellten Verfahrens dienten. Die Untersuchung der aus 2000 Einzelantworten erhaltenen Mittelwerte in Kombination mit der Analyse synchronisierter Regularitäten über den Verlauf der Einzelantworten ergab dabei konsistente Beobachtungen in gefundenen Amplituden, Latenzen sowie in Abhängigkeiten zwischen Einzelantworten in beiden Gruppen. Das vorgestellte Verfahren erlaubt somit auf einzigartige Weise neue und ungesehene Einsichten in natürliche, neurale Antworten auf Hirnstammebene von CI-Nutzern, welche in zukünftigen Studien verwendet werden können, um binaurale Interaktionen und Fusionen weiter untersuchen zu können. Interessanterweise hat sich, die auf ABRs basierende, binaurale Interaktionskomponente (engl. binaural interaction component (BIC)) als objektiver Indikator binauraler Integration etabliert. Diese Komponente (d.h. die arithmetische Differenz zwischen der Summe der monauralen Antworten und der binauralen Antwort) ist leider sehr fragil, wodurch ein sicherer und objektiver Nachweis in CI-Nutzern bis heute nicht existiert. Dabei ist es sehr wahrscheinlich, dass gerade Implantatsträger von einer entsprechenden Analyse auf Hirnstammebene und höherrangigen Ebenen deutlich profitieren würden, da dies objektiv Anpassstrategien mit Rücksicht auf eine bestmögliche binaurale Integration ermöglichen könnte. Deshalb stellen wir ein weiteres, neuartiges Verfahren zum Erhalt von neuralen Korrelaten binauraler Interaktion in bimodal versorgten CI-Trägern vor, welches jüngste Erfolge im Bereich der schnellen, entfalltungsbasierten ABR Akquisition und der bereits vorgestellten Schmalband- filterung zur Reduktion von Stimulationsartefakten kombiniert. Basierend auf diesem Verfahren konnten signifikante Verbesserungen in der BIC-Amplitude in 10 bimodal versorgten Patienten sowie 10 normalhörenden Probanden, basierend auf umgesetzte, interaurale Laufzeitkompensationen technischer Hörhilfen, aufgezeigt werden. Insgesamt demonstrieren beide vorgestellten Studien technisch-induzierte, interaurale Laufzeitunterschiede und betonen demnach sehr deutlich potenzielle Vorteile in assoziierten neuralen Korrelaten binauraler Interaktionen, wenn solche Missverhältnisse zeitlich ausgeglichen werden. Die aufgezeigten Ergebnisse sowie die getätigte Abschätzungen technischer Laufzeiten sollte in größeren Gruppen weiter untersucht werden, um die Aussagekraft weiter zu steigern. Dennoch unterstreichen diese Einsichten das Verlangen nach binauralen Lösungsansätzen in der zukünftigen Hörrehabilitation, statt bisheriger isolierter und monauraler Therapien

Models and analysis of vocal emissions for biomedical applications

This book of Proceedings collects the papers presented at the 3rd International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications, MAVEBA 2003, held 10-12 December 2003, Firenze, Italy. The workshop is organised every two years, and aims to stimulate contacts between specialists active in research and industrial developments, in the area of voice analysis for biomedical applications. The scope of the Workshop includes all aspects of voice modelling and analysis, ranging from fundamental research to all kinds of biomedical applications and related established and advanced technologies

Brain-Computer Interface Based on Unsupervised Methods to Recognize Motor Intention for Command of a Robotic Exoskeleton

Stroke and road traffic injuries may severely affect movements of lower-limbs in humans, and consequently the locomotion, which plays an important role in daily activities, and the quality of life. Robotic exoskeleton is an emerging alternative, which may be used on patients with motor deficits in the lower extremities to provide motor rehabilitation and gait assistance. However, the effectiveness of robotic exoskeletons may be reduced by the autonomous ability of the robot to complete the movement without the patient involvement. Then, electroencephalography signals (EEG) have been addressed to design brain-computer interfaces (BCIs), in order to provide a communication pathway for patients perform a direct control on the exoskeleton using the motor intention, and thus increase their participation during the rehabilitation. Specially, activations related to motor planning may help to improve the close loop between user and exoskeleton, enhancing the cortical neuroplasticity. Motor planning begins before movement onset, thus, the training stage of BCIs may be affected by the intuitive labeling process, as it is not possible to use reference signals, such as goniometer or footswitch, to select those time periods really related to motor planning. Therefore, the gait planning recognition is a challenge, due to the high uncertainty of selected patterns, However, few BCIs based on unsupervised methods to recognize gait planning/stopping have been explored. This Doctoral Thesis presents unsupervised methods to improve the performance of BCIs during gait planning/ stopping recognition. At this context, an adaptive spatial filter for on-line processing based on the Concordance Correlation Coefficient (CCC) was addressed to preserve the useful information on EEG signals, while rejecting neighbor electrodes around the electrode of interest. Here, two methods for electrode selection were proposed. First, both standard deviation and CCC between target electrodes and their correspondent neighbor electrodes are analyzed on sliding windows to select those neighbors that are highly correlated. Second, Zscore analysis is performed to reject those neighbor electrodes whose amplitude values presented significant difference in relation to other neighbors. Furthermore, another method that uses the representation entropy and the maximal information compression index (MICI) was proposed for feature selection, which may be robust to select patterns, as only it depends on cluster distribution. In addition, a statistical analysis was introduced here to adjust, in the training stage of BCIs, regularized classifiers, such as support vector machine (SVM) and regularized discriminant analysis (RDA). Six subjects were adopted to evaluate the performance of different BCIs based on the proposed viii methods, during gait planning/stopping recognition. The unsupervised approach for feature selection showed similar performance to other methods based on linear discriminant analysis (LDA), when it was applied in a BCI based on the traditional Weighted Average to recognize gait planning. Additionally, the proposed adaptive filter improved the performance of BCIs based on traditional spatial filters, such as Local Average Reference (LAR) and WAR, as well as others BCIs based on powerful methods, such as Common Spatial Pattern (CSP), Filter Bank Common Spatial Pattern (FBCSP) and Riemannian kernel (RK). RK presented the best performance in comparison to CSP and FBCSP, which agrees with the hypothesis that unsupervised methods may be more appropriate to analyze clusters of high uncertainty, as those formed by motor planning. BCIs using adaptive filter based on Zscore analysis, with an unsupervised approach for feature selection and RDA showed promising results to recognize both gait planning and gait stopping, achieving for three subjects, good values of true positive rate (>70%) and false positive (<16%). Thus, the proposed methods may be used to obtain an optimized BCI that preserves the useful information, enhancing the gait planning/stopping recognition. In addition, the method for feature selection has low computational cost, which may be suitable for applications that demand short time of training, such as clinical application time

Advancing Pattern Recognition Techniques for Brain-Computer Interfaces: Optimizing Discriminability, Compactness, and Robustness

In dieser Dissertation formulieren wir drei zentrale Zielkriterien zur systematischen Weiterentwicklung der Mustererkennung moderner Brain-Computer Interfaces (BCIs). Darauf aufbauend wird ein Rahmenwerk zur Mustererkennung von BCIs entwickelt, das die drei Zielkriterien durch einen neuen Optimierungsalgorithmus vereint. Darüber hinaus zeigen wir die erfolgreiche Umsetzung unseres Ansatzes für zwei innovative BCI Paradigmen, für die es bisher keine etablierte Mustererkennungsmethodik gibt

New approaches for EEG signal processing: artifact EOG removal by ICA-RLS scheme and tracks extraction method

Localizing the bioelectric phenomena originating from the cerebral cortex and evoked by auditory and somatosensory stimuli are clear objectives to both understand how the brain works and to recognize different pathologies. Diseases such as Parkinson’s, Alzheimer’s, schizophrenia and epilepsy are intensively studied to find a cure or accurate diagnosis. Epilepsy is considered the disease with major prevalence within disorders with neurological origin. The recurrent and sudden incidence of seizures can lead to dangerous and possibly life-threatening situations. Since disturbance of consciousness and sudden loss of motor control often occur without any warning, the ability to predict epileptic seizures would reduce patients’ anxiety, thus considerably improving quality of life and safety. The common procedure for epilepsy seizure detection is based on brain activity monitorization via electroencephalogram (EEG) data. This process consumes a lot of time, especially in the case of long recordings, but the major problem is the subjective nature of the analysis among specialists when analyzing the same record. From this perspective, the identification of hidden dynamical patterns is necessary because they could provide insight into the underlying physiological mechanisms that occur in the brain. Time-frequency distributions (TFDs) and adaptive methods have demonstrated to be good alternatives in designing systems for detecting neurodegenerative diseases. TFDs are appropriate transformations because they offer the possibility of analyzing relatively long continuous segments of EEG data even when the dynamics of the signal are rapidly changing. On the other hand, most of the detection methods proposed in the literature assume a clean EEG signal free of artifacts or noise, leaving the preprocessing problem opened to any denoising algorithm. In this thesis we have developed two proposals for EEG signal processing: the first approach consists in electrooculogram (EOG) removal method based on a combination of ICA and RLS algorithms which automatically cancels the artifacts produced by eyes movement without the use of external “ad hoc” electrode. This method, called ICA-RLS has been compared with other techniques that are in the state of the art and has shown to be a good alternative for artifacts rejection. The second approach is a novel method in EEG features extraction called tracks extraction (LFE features). This method is based on the TFDs and partial tracking. Our results in pattern extractions related to epileptic seizures have shown that tracks extraction is appropriate in EEG detection and classification tasks, being practical, easily applicable in medical environment and has acceptable computational cost