Using Interleaved Stimulation to Measure the Size and Selectivity of the Sustained Phase-Locked Neural Response to Cochlear Implant Stimulation

Objective discrimination of bimodal speech using frequency following responses

Bimodal hearing, in which a contralateral hearing aid is combined with a cochlear implant (CI), provides greater speech recognition benefits than using a CI alone. Factors predicting individual bimodal patient success are not fully understood. Previous studies have shown that bimodal benefits may be driven by a patient's ability to extract fundamental frequency (f0) and/or temporal fine structure cues (e.g., F1). Both of these features may be represented in frequency following responses (FFR) to bimodal speech. Thus, the goals of this study were to: 1) parametrically examine neural encoding of f0 and F1 in simulated bimodal speech conditions; 2) examine objective discrimination of FFRs to bimodal speech conditions using machine learning; 3) explore whether FFRs are predictive of perceptual bimodal benefit. Three vowels (/ε/, /i/, and /ʊ/) with identical f0 were manipulated by a vocoder (right ear) and low-pass filters (left ear) to create five bimodal simulations for evoking FFRs: Vocoder-only, Vocoder +125 Hz, Vocoder +250 Hz, Vocoder +500 Hz, and Vocoder +750 Hz. Perceptual performance on the BKB-SIN test was also measured using the same five configurations. Results suggested that neural representation of f0 and F1 FFR components were enhanced with increasing acoustic bandwidth in the simulated "non-implanted" ear. As spectral differences between vowels emerged in the FFRs with increased acoustic bandwidth, FFRs were more accurately classified and discriminated using a machine learning algorithm. Enhancement of f0 and F1 neural encoding with increasing bandwidth were collectively predictive of perceptual bimodal benefit on a speech-in-noise task. Given these results, FFR may be a useful tool to objectively assess individual variability in bimodal hearing.

Towards personalized and optimized fitting of cochlear implants

A cochlear implant (CI) is a neurotechnological device that restores total sensorineural hearing loss. It contains a sophisticated speech processor that analyzes and transforms the acoustic input. It distributes its time-enveloped spectral content to the auditory nerve as electrical pulsed stimulation trains of selected frequency channels on a multi-contact electrode that is surgically inserted in the cochlear duct. This remarkable brain interface enables the deaf to regain hearing and understand speech. However, tuning of the large (>50) number of parameters of the speech processor, so-called "device fitting," is a tedious and complex process, which is mainly carried out in the clinic through 'one-size-fits-all' procedures. Current fitting typically relies on limited and often subjective data that must be collected in limited time. Despite the success of the CI as a hearing-restoration device, variability in speech-recognition scores among users is still very large, and mostly unexplained. The major factors that underly this variability incorporate three levels: (i) variability in auditory-system malfunction of CI-users, (ii) variability in the selectivity of electrode-to-auditory nerve (EL-AN) activation, and (iii) lack of objective perceptual measures to optimize the fitting. We argue that variability in speech recognition can only be alleviated by using objective patient-specific data for an individualized fitting procedure, which incorporates knowledge from all three levels. In this paper, we propose a series of experiments, aimed at collecting a large amount of objective (i.e., quantitative, reproducible, and reliable) data that characterize the three processing levels of the user's auditory system. Machine-learning algorithms that process these data will eventually enable the clinician to derive reliable and personalized characteristics of the user's auditory system, the quality of EL-AN signal transfer, and predictions of the perceptual effects of changes in the current fitting.

Electrophysiological and Psychophysical Measures of Temporal Pitch Sensitivity in Normal-hearing Listeners

To obtain combined behavioural and electrophysiological measures of pitch perception, we presented harmonic complexes, bandpass filtered to contain only high-numbered harmonics, to normal-hearing listeners. These stimuli resemble bandlimited pulse trains and convey pitch using a purely temporal code. A core set of conditions consisted of six stimuli with baseline pulse rates of 94, 188 and 280 pps, filtered into a HIGH (3365-4755 Hz) or VHIGH (7800-10,800 Hz) region, alternating with a 36% higher pulse rate. Brainstem and cortical processing were measured using the frequency following response (FFR) and auditory change complex (ACC), respectively. Behavioural rate change difference limens (DLs) were measured by requiring participants to discriminate between a stimulus that changed rate twice (up-down or down-up) during its 750-ms presentation from a constant-rate pulse train. FFRs revealed robust brainstem phase locking whose amplitude decreased with increasing rate. Moderate-sized but reliable ACCs were obtained in response to changes in purely temporal pitch and, like the psychophysical DLs, did not depend consistently on the direction of rate change or on the pulse rate for baseline rates between 94 and 280 pps. ACCs were larger and DLs lower for stimuli in the HIGH than in the VHGH region. We argue that the ACC may be a useful surrogate for behavioural measures of rate discrimination, both for normal-hearing listeners and for cochlear-implant users. We also showed that rate DLs increased markedly when the baseline rate was reduced to 48 pps, and compared the behavioural and electrophysiological findings to recent cat data obtained with similar stimuli and methods.

Recording EEG in Cochlear Implant Users: Guidelines for Experimental Design and Data Analysis for Optimizing Signal Quality and Minimizing Artifacts

Cochlear implants (CI) are neural prostheses that can restore hearing in individuals with severe to profound hearing loss. Although CIs significantly improve quality of life, clinical outcomes are still highly variable. An important part of this variability is explained by the brain reorganization following cochlear implantation. Therefore, clinicians and researchers are seeking objective measurements to investigate post-implantation brain plasticity. Electroencephalography (EEG) is a promising technique because it is objective, non-invasive, and implant-compatible, but is nonetheless susceptible to massive artifacts generated by the prosthesis's electrical activity. CI artifacts can blur and distort brain responses; thus, it is crucial to develop reliable techniques to remove them from EEG recordings. Despite numerous artifact removal techniques used in previous studies, there is a paucity of documentation and consensus on the optimal EEG procedures to reduce these artifacts. Herein, and through a comprehensive review process, we provide a guideline for designing an EEG-CI experiment minimizing the effect of the artifact. We provide some technical guidance for recording an accurate neural response from CI users and discuss the current challenges in detecting and removing CI-induced artifacts from a recorded signal. The aim of this paper is also to provide recommendations to better appraise and report EEG-CI findings.

Cochlear Implant Research and Development in the Twenty-first Century: A Critical Update

Cochlear implants (CIs) are the world's most successful sensory prosthesis and have been the subject of intense research and development in recent decades. We critically review the progress in CI research, and its success in improving patient outcomes, from the turn of the century to the present day. The review focuses on the processing, stimulation, and audiological methods that have been used to try to improve speech perception by human CI listeners, and on fundamental new insights in the response of the auditory system to electrical stimulation. The introduction of directional microphones and of new noise reduction and pre-processing algorithms has produced robust and sometimes substantial improvements. Novel speech-processing algorithms, the use of current-focusing methods, and individualised (patient-by-patient) deactivation of subsets of electrodes have produced more modest improvements. We argue that incremental advances have and will continue to be made, that collectively these may substantially improve patient outcomes, but that the modest size of each individual advance will require greater attention to experimental design and power. We also briefly discuss the potential and limitations of promising technologies that are currently being developed in animal models, and suggest strategies for researchers to collectively maximise the potential of CIs to improve hearing in a wide range of listening situations.