A Model for Electrical Communication Between Cochlear Implants and the Brain

In the last thirty years, cochlear implants have become an invaluable instrument in the treatment of severe-to-profound hearing impairment. An important aspect of research in the continued development of cochlear implants is the in vivo assessment of signal processing algorithms intended to improve perception of speech and other auditory signals. In trying to determine how closely cochlear implant recipients process sound relative to the processing done by a normal auditory system, various assessment techniques have been applied. The most common technique has been measurement of auditory evoked potentials (AEPs), which involves the recording of neural responses to auditory stimulation. Depending on the latency of the observed response, the evoked potential indicates neural activity at various ascending neurological structures of the auditory system. Although there have been a number of publications on the topic of AEPs in cochlear implant subjects, there is a need for better measurement and research techniques to obtain more in-depth information to facilitate research on effectiveness of signal processing approaches in cochlear implants. The research presented herein explored the use of MatLab for the purpose of developing a model for electrically evoked auditory brainstem responses (EABRs). The EABR is commonly measured in hearing-impaired patients who have cochlear implants, via electrical stimulation delivered from electrodes in the implanted array. The simulation model developed in this study took as its input the stimulus current intensity level, and used function vectors and equations derived from measured EABRs, to generate an approximation of the evoked surface potentials. A function vector was used to represent the combined firing of the neurons of the auditory nervous system that are needed to elicit a measurable response. Equations were derived to represent the latency and stimulus amplitude scaling functions. The simulation also accounted for other neural activity that can be present in and contaminate an ABR recording, and reduced it through time-locked averaging of the simulated response. Predicted waveforms from the MatLab model were compared both to published waveforms from a cochlear implant recipient, and a series of EABR waveforms measured by the author in other cochlear implant recipients. Measurement of the EABRs required specialized interfacing of a commercial recording system with the signal processors of the patients\u27 cochlear implants. A novel measurement technique was also used to obtain more frequency-specific information than usually obtained. Although the nonlinearities normally present in the auditory system were not considered in this MatLab simulation, the model nevertheless performed well and delivered results comparing favorably with the results measured from the research subjects

Authentication using c-VEP evoked in a mild-burdened cognitive task

In recent years, more and more researchers are devoting themselves to the studies about authentication based on biomarkers. Among a wide variety of biomarkers, code-modulated visual evoked potential (c-VEP) has attracted increasing attention due to its significant role in the field of brain-computer interface. In this study, we designed a mild-burdened cognitive task (MBCT), which can check whether participants focus their attention on the visual stimuli that evoke c-VEP. Furthermore, we investigated the authentication based on the c-VEP evoked in the cognitive task by introducing a deep learning method. Seventeen participants were recruited to take part in the MBCT experiments including two sessions, which were carried out on two different days. The c-VEP signals from the first session were extracted to train the authentication deep models. The c-VEP data of the second session were used to verify the models. It achieved a desirable performance, with the average accuracy and F1 score, respectively, of 0.92 and 0.89. These results show that c-VEP carries individual discriminative characteristics and it is feasible to develop a practical authentication system based on c-VEP

The Impact of Childhood Music Experience on Speech Perception and Processing: A Systematic Review

Objective: The purpose of this investigation was to conduct a systematic review of the literature that addresses the impact of childhood musical experience on speech perception and processing abilities. Specifically, this review assessed how musical training impacted scores on both objective and behavioral tests of speech perception/processing in children. This analysis contributes to a better understanding of the effects of individual musical experience in childhood on our ability to perceive and process speech in a variety of listening conditions. This analysis also determined the clinical implications of such findings. Methods: A comprehensive search utilizing the Web of Science database accessible through the City University of New York (CUNY) Graduate Center Library was conducted to identify relevant studies published after 2000. Inclusion criteria included the evaluation speech perception and/or processing in children utilizing objective and/or behavioral outcome measures. Results: Sixteen studies met the inclusion criteria for this systematic review. The studies utilized a variety of outcome measures, which were categorized as objective or behavioral. All included studies found a significant positive relationship between musical experience and speech perception and/or processing abilities in children for both behavioral and objective outcome measures. Discussion: Significant effects of musical training in childhood were noted across outcome measures suggesting a positive effect on speech perception and processing. Effects on speech perception and processing were noted when both behavioral and objective measures were utilized. Furthermore, studies comparing behavioral and objective outcome measures reported similar findings between the two methods. Conclusion: The positive effect of childhood musical experience on speech perception and processing abilities is present throughout the literature reviewed when both objective and behavioral outcome measures are utilized. As a result, formal musical training in childhood should be considered as a viable option for auditory training when the goal is improved speech perception and/or processing. The results of these studies should also support the benefit of music classes in school curriculums to help children overcome communication challenges (such as listening in the presence of noise, distance, and poor acoustics) that are frequently found inside and outside of the classroom. Future research should address the limitations of the included studies, such as utilizing a standard musical training program, replicating the large proportion of research on this topic that originated from the Northwestern University Auditory Neuroscience Laboratory, and the utilization of a quasi-experimental or randomized clinical trial design

Hearing the Moment: Measures and Models of the Perceptual Centre

The perceptual centre (P-centre) is the hypothetical specific moment at which a brief event is perceived to occur. Several P-centre models are described in the literature and the first collective implementation and rigorous evaluation of these models using a common corpus is described in this thesis, thus addressing a significant open question: which model should one use? The results indicate that none of the models reliably handles all sound types. Possibly this is because the data for model development are too sparse, because inconsistent measurement methods have been used, or because the assumptions underlying the measurement methods are untested. To address this, measurement methods are reviewed and two of them, rhythm adjustment and tap asynchrony, are evaluated alongside a new method based on the phase correction response (PCR) in a synchronized tapping task. Rhythm adjustment and the PCR method yielded consistent P-centre estimates and showed no evidence of P-centre context dependence. Moreover, the PCR method appears most time efficient for generating accurate P-centre estimates. Additionally, the magnitude of the PCR is shown to vary systematically with the onset complexity of speech sounds, which presumably reflects the perceived clarity of a sound’s P-centre. The ideal outcome of any P-centre measurement technique is to detect the true moment of perceived event occurrence. To this end a novel P-centre measurement method, based on auditory evoked potentials, is explored as a possible objective alternative to the conventional approaches examined earlier. The results are encouraging and suggest that a neuroelectric correlate of the P-centre does exist, thus opening up a new avenue of P-centre research. Finally, an up to date and comprehensive review of the P-centre is included, integrating recent findings and reappraising previous research. The main open questions are identified, particularly those most relevant to P-centre modelling

Identification of audio evoked response potentials in ambulatory EEG data

Electroencephalography (EEG) is commonly used for observing brain function over a period of time. It employs a set of invasive electrodes on the scalp to measure the electrical activity of the brain. EEG is mainly used by researchers and clinicians to study the brain’s responses to a specific stimulus - the event-related potentials (ERPs). Different types of undesirable signals, which are known as artefacts, contaminate the EEG signal. EEG and ERP signals are very small (in the order of microvolts); they are often obscured by artefacts with much larger amplitudes in the order of millivolts. This greatly increases the difficulty of interpreting EEG and ERP signals.Typically, ERPs are observed by averaging EEG measurements made with many repetitions of the stimulus. The average may require many tens of repetitions before the ERP signal can be observed with any confidence. This greatly limits the study and useof ERPs. This project explores more sophisticated methods of ERP estimation from measured EEGs. An Optimal Weighted Mean (OWM) method is developed that forms a weighted average to maximise the signal to noise ratio in the mean. This is developedfurther into a Bayesian Optimal Combining (BOC) method where the information in repetitions of ERP measures is combined to provide a sequence of ERP estimations with monotonically decreasing uncertainty. A Principal Component Analysis (PCA) isperformed to identify the basis of signals that explains the greatest amount of ERP variation. Projecting measured EEG signals onto this basis greatly reduces the noise in measured ERPs. The PCA filtering can be followed by OWM or BOC. Finally, crosschannel information can be used. The ERP signal is measured on many electrodes simultaneously and an improved estimate can be formed by combining electrode measurements. A MAP estimate, phrased in terms of Kalman Filtering, is developed using all electrode measurements.The methods developed in this project have been evaluated using both synthetic and measured EEG data. A synthetic, multi-channel ERP simulator has been developed specifically for this project.Numerical experiments on synthetic ERP data showed that Bayesian Optimal Combining of trial data filtered using a combination of PCA projection and Kalman Filtering, yielded the best estimates of the underlying ERP signal. This method has been applied to subsets of real Ambulatory Electroencephalography (AEEG) data, recorded while participants performed a range of activities in different environments. From this analysis, the number of trials that need to be collected to observe the P300 amplitude and delay has been calculated for a range of scenarios

IFCN-endorsed practical guidelines for clinical magnetoencephalography (MEG)

Magnetoencephalography (MEG) records weak magnetic fields outside the human head and thereby provides millisecond-accurate information about neuronal currents supporting human brain function. MEG and electroencephalography (EEG) are closely related complementary methods and should be interpreted together whenever possible. This manuscript covers the basic physical and physiological principles of MEG and discusses the main aspects of state-of-the-art MEG data analysis. We provide guidelines for best practices of patient preparation, stimulus presentation, MEG data collection and analysis, as well as for MEG interpretation in routine clinical examinations. In 2017, about 200 whole-scalp MEG devices were in operation worldwide, many of them located in clinical environments. Yet, the established clinical indications for MEG examinations remain few, mainly restricted to the diagnostics of epilepsy and to preoperative functional evaluation of neurosurgical patients. We are confident that the extensive ongoing basic MEG research indicates potential for the evaluation of neurological and psychiatric syndromes, developmental disorders, and the integrity of cortical brain networks after stroke. Basic and clinical research is, thus, paving way for new clinical applications to be identified by an increasing number of practitioners of MEG. (C) 2018 International Federation of Clinical Neurophysiology. Published by Elsevier B.V.Peer reviewe

Joint time-frequency analysis and filtering of single trial event-related potentials.

The ongoing electrical activity of the brain is known as the electroencephalograph (EEG). Event related potentials (ERPs) are voltage deviations in the EEG elicited in association with stimuli. Their elicitation require cognitive processes such as response to a recognised stimulus. ERPs therefore provide clinical information by allowing an insight into neurological processes. The amplitude of an event-related potential is typically several times less than the background EEG. The background EEG has the effect of obscuring the ERP and therefore appropriate signal processing is required for its recovery. Traditionally ERPs are estimated using the synchronised averaging of several single trials or sweeps. This inhibits investigation of any trial-to-trial variation, which can prove valuable in understanding cognitive processes. An aim of this study was to develop wavelet-based techniques for the recovery of single trial ERPs from background EEG. A novel wavelet-based adaptive digital filtering method for ERPs has been developed. The method provides the ability to effectively estimate or recover single ERPs. The effectiveness of the method has been quantitatively evaluated and compared with other methods of ERP estimation.The ability to recover single sweep ERPs allowed the investigation of characteristics that are not possible using the conventional averaged estimation. The development of features of a cognitive ERP known as the contingent negative variation over a number of trials was investigated. The trend in variation enabled the identification of schizophrenic subjects using artificial intelligence methods.A new technique to investigate the phase dynamics of ERPs was developed. This was successfully applied, along with other techniques, to the investigation of independent component analysis (ICA) component activations in a visual spatial attention task. Two components with scalp projections that suggested that they may be sources within the visual cortex were investigated. The study showed that the two components were visual field selective and that their activation was both amplitude and phase modulated

Cognitive Auditory Evoked Potentials in Investigation of Hearing Discrimination

Preattentive perception of occasional deviating stimuli in the stream of standard stimuli can be recorded with cognitive event-related potential (ERP) mismatch negativity (MMN). The earlier detection of stimuli at the auditory cortex can be examined with N1 and P2 ERPs. The MMN recording does not require co-operation, it correlates with perceptual threshold, and even complex sounds can be used as stimuli. The aim of this study was to examine different aspects that should be considered when measuring discrimination of hearing with ERPs. The MMN was found to be stimulusintensity- dependent. As the intensity of sine wave stimuli was increased from 40 to 80 dB HL, MMN mean amplitudes increased. The effect of stimulus frequency on the MMN was studied so that the pitch difference would be equal in each stimulus block according to the psychophysiological mel scale or the difference limen of frequency (DLF). However, the blocks differed from each other. The contralateral white noise masking (50 dB EML) was found to attenuate the MMN amplitude when the right ear was stimulated. The N1 amplitude was attenuated and, in contrast, P2 amplitude was not affected by contralateral white noise masking. The perception and production of vowels by four postlingually deafened patients with a cochlear implant were studied. The MMN response could be elicited in the patient with the best vowel perception abilities. The results of the studies show that concerning the MMN recordings, the stimulus parameters and recording procedure design have a great influence on the results.Siirretty Doriast

The utility of latency and spectral analysis methods in evoked potential recordings from patients with hepatic encephalopathy

Evoked potentials (EPs) are small phasic potentials that are elicited in conjunction with sensory, motor and cognitive events. EP variables have been assessed in patients with cirrhosis but in general, methods were inadequately standardized and study populations incompletely characterized, leading to some studies questioning the validity of EP’s in diagnosing and monitoring hepatic encephalopathy, while other studies indicated that there is only a low positive yield with these investigations. Few studies have attempted tri-modal sensory and cognitive recordings. Recorded waveforms may demonstrate altered morphology while possessing broadly normal latencies. Since EP analysis is usually performed solely in the time domain, latency measurements do not therefore highlight morphological changes to the waveform and so abnormalities may go unreported. The aim of this study was twofold (i) to measure sensory and cognitive EPs in patients with cirrhosis in relation to their neuropsychiatric status and (ii) to address frequency content in relation to neuropsychiatric status by examining EPs with two spectral techniques, the Fourier Transform (FT) and the Power Spectral Density Estimate (PSD). Seventy patients with biopsy–proven cirrhosis were classified using clinical, psychometric and EEG criteria as unimpaired or as having minimal or overt hepatic encephalopathy (HE). Forty-eight healthy individuals served as controls. Visual (VEPs), brainstem auditory (BAEPs) somatosensory (SSEPs) and cognitive auditory (P300) EPs were recorded under standardized conditions. Significant latency differences were observed in sensory EPs between patients and controls with patient subgroups differences being less significant. The cognitive auditory P300 however, distinguished the patient subpopulations from one another. Frequency shifts are observed in all EP modalities with significant differences also occurring between patient groups. The sensitivity and specificity of the frequency-domain is comparable to that of the time-domain. Paired EP investigations analysed by latency indicate BAEP and P300 best discriminate any degree of encephalopathy; in the frequency domain it is the VEP combined with SEP and in the time-frequency domain it is the SEP. These findings suggest that EPs, when performed as a bank of multimodal tests and with spectral analysis, could provide a sensitive and specific method for the diagnosis and monitoring of hepatic encephalopathy

A Comparison of Movement-Related Cortical Potentials and Their Application in Brain-Computer Interfaces for Autism Spectrum Disorder

Brain-computer interfaces have the potential to improve the lives of many populations who benefit from neurofeedback. Autism Spectrum Disorder is a condition experienced by many and its deficits are potentially improved for some using brain-computer interface technology. Various techniques have already been used to illustrate improvements in ASD across different brain signals and interactive interfaces. In particular, movement-related cortical potentials are related to executive functioning of movement and have been shown to be successful in other systems. This thesis investigates the effect of Autism Spectrum Disorder in adults on how movement-related cortical potentials are elicited in the brain compared to neurotypical populations to determine whether the motor systems that elicit such signals are abnormally functioning, and as a result whether they may be improved with neurofeedback. In addition to understanding the EEG response for people with ASD to brain-computer interfaces, it is important to gain insights into their perception of such technologies. This thesis also examines how people with ASD perceive different potential brain-computer interfaces. Quantitative and qualitative data was collected and analysed across three different interfaces (auditory, visual, and haptic) and two different tasks (real movement and imagined movement execution). The EEG results show statistically significant differences in the elicitation of movement-related cortical potentials (MRCPs) between the autistic and neurotypical group, thus indicating possible underlying abnormalities in the motor systems being activated. The features of MRCP were much smaller in amplitude in the ASD group, suggesting that fewer neurons are being recruited for movement-based actions. Since other studies have demonstrated success when improving MRCPs in populations suffering from Parkinson’s and stroke, it is thus inferred that such neurofeedback may also benefit those with Autism Spectrum Disorder. While there were no statistical differences regarding EEG-related performance for different modalities, qualitative results suggest common themes regarding people with ASD’s subjective perceptions, including the need for feedback on performance and strong preferences for different types of modalities. These results emphasize the importance of considering both quantitative and qualitative data when designing brain-computer interfaces for these populations. This research demonstrates an opportunity to use MRCP-based neurofeedback to help populations with ASD, as well as emphasizes the importance and insights of capturing qualitative data in the process