Effect of Serious Gaming on Speech-in-Noise Intelligibility in Adult Cochlear Implantees: A Randomized Controlled Study.

Listening in noise remains challenging for adults with cochlear implants (CI) even after prolonged experience. Personalized auditory training (AT) programs can be proposed to improve specific auditory skills in adults with CI. The objective of this study was to assess serious gaming as a rehabilitation tool to improve speech-in-noise intelligibility in adult CI users. Thirty subjects with bilateral profound hearing loss and at least 9 months of CI experience were randomized to participate in a 5-week serious game-based AT program (n = 15) or a control group (n = 15). All participants were tested at enrolment and at 5 weeks using the sentence recognition-in-noise matrix test to measure the signal-to-noise ratio (SNR) allowing 70% of speech-in-noise understanding (70% speech reception threshold, SRT70). Thirteen subjects completed the AT program and nine of them were re-tested 5 weeks later. The mean SRT70 improved from 15.5 dB to 11.5 dB SNR after 5 weeks of AT (p < 0.001). No significant change in SRT70 was observed in the control group. In the study group, the magnitude of SRT70 improvement was not correlated to the total number of AT hours. A large inter-patient variability was observed for speech-in-noise intelligibility measured once the AT program was completed and at re-test. The results suggest that serious game-based AT may improve speech-in-noise intelligibility in adult CI users. Potential sources of inter-patient variability are discussed. Serious gaming may be considered as a complementary training approach for improving CI outcomes in adults.

A New Method to Test the Efficiency of Cochlear Implant Artifacts Removal From Auditory Evoked Potentials.

Auditory evoked potentials are of great interest to objectively evaluate the audition in cochlear implant (CI) recipients. However, these measures are impeded by CI stimulation electrical artifacts present in the EEG. In the first part, this paper investigates the use of a hybrid model approximating CI patient data. This model gives access to both uncontaminated and denoised data, thus allowing for the evaluation of CI artifact removal methods. Here the efficiency of independent component analysis (ICA) is evaluated in the context of auditory steady-state responses (ASSRs). A dedicated experimental setup was developed to simultaneously record EEG data from a normal hearing (NH) participant and CI artifact data from a phantom equipped with a CI. Hybrid data were obtained as a linear mixture of both sources. Amplitude-modulated continuous tones were used as stimuli to elicit ASSRs. After denoising, the comparison of denoised hybrid data and original NH data showed high correlations between the two datasets, demonstrating the efficiency of ICA. In the second part, the ICA was applied to real clinical CI ASSR data. Results support the usefulness of the methodology as regards the performance evaluation of signal processing methods applied to CI patient data prior to clinical application.

Auditory steady state responses and cochlear implants: Modeling the artifact-response mixture in the perspective of denoising.

Auditory steady state responses (ASSRs) in cochlear implant (CI) patients are contaminated by the spread of a continuous CI electrical stimulation artifact. The aim of this work was to model the electrophysiological mixture of the CI artifact and the corresponding evoked potentials on scalp electrodes in order to evaluate the performance of denoising algorithms in eliminating the CI artifact in a controlled environment. The basis of the proposed computational framework is a neural mass model representing the nodes of the auditory pathways. Six main contributors to auditory evoked potentials from the cochlear level and up to the auditory cortex were taken into consideration. The simulated dynamics were then projected into a 3-layer realistic head model. 32-channel scalp recordings of the CI artifact-response were then generated by solving the electromagnetic forward problem. As an application, the framework's simulated 32-channel datasets were used to compare the performance of 4 commonly used Independent Component Analysis (ICA) algorithms: infomax, extended infomax, jade and fastICA in eliminating the CI artifact. As expected, two major components were detectable in the simulated datasets, a low frequency component at the modulation frequency and a pulsatile high frequency component related to the stimulation frequency. The first can be attributed to the phase-locked ASSR and the second to the stimulation artifact. Among the ICA algorithms tested, simulations showed that infomax was the most efficient and reliable in denoising the CI artifact-response mixture. Denoising algorithms can induce undesirable deformation of the signal of interest in real CI patient recordings. The proposed framework is a valuable tool for evaluating these algorithms in a controllable environment ahead of experimental or clinical applications.

Perceptual assimilation of lexical tone: the roles of language experience and visual information.

Using Best's (1995) perceptual assimilation model (PAM), we investigated auditory-visual (AV), auditory-only (AO), and visual-only (VO) perception of Thai tones. Mandarin and Cantonese (tone-language) speakers were asked to categorize Thai tones according to their own native tone categories, and Australian English (non-tone-language) speakers to categorize Thai tones into their native intonation categories-for instance, question or statement. As comparisons, Thai participants completed a straightforward identification task, and another Australian English group identified the Thai tones using simple symbols. All of the groups also completed an AX discrimination task. Both the Mandarin and Cantonese groups categorized AO and AV Thai falling tones as their native level tones, and Thai rising tones as their native rising tones, although the Mandarin participants found it easier to categorize Thai level tones than did the Cantonese participants. VO information led to very poor categorization for all groups, and AO and AV information also led to very poor categorizations for the English intonation categorization group. PAM's predictions regarding tone discriminability based on these category assimilation patterns were borne out for the Mandarin group's AO and AV discriminations, providing support for the applicability of the PAM to lexical tones. For the Cantonese group, however, PAM was unable to account for one specific discrimination pattern-namely, their relatively good performance on the Thai high-rising contrast in the auditory conditions-and no predictions could be derived for the English groups. A full account of tone assimilation will likely need to incorporate considerations of phonetic, and even acoustic, similarity and overlap between nonnative and native tone categories.

Cross-language perception of Cantonese vowels spoken by native and non-native speakers.

This study examined the effect of native language background on listeners' perception of native and non-native vowels spoken by native (Hong Kong Cantonese) and non-native (Mandarin and Australian English) speakers. They completed discrimination and an identification task with and without visual cues in clear and noisy conditions. Results indicated that visual cues did not facilitate perception, and performance was better in clear than in noisy conditions. More importantly, the Cantonese talker's vowels were the easiest to discriminate, and the Mandarin talker's vowels were as intelligible as the native talkers' speech. These results supported the interlanguage speech native intelligibility benefit patterns proposed by Hayes-Harb et al. (J Phonetics 36:664-679, 2008). The Mandarin and English listeners' identification patterns were similar to those of the Cantonese listeners, suggesting that they might have assimilated Cantonese vowels to their closest native vowels. In addition, listeners' perceptual patterns were consistent with the principles of Best's Perceptual Assimilation Model (Best in Speech perception and linguistic experience: issues in cross-language research. York Press, Timonium, 1995).

xDAWN algorithm to enhance evoked potentials: application to brain-computer interface.

A brain-computer interface (BCI) is a communication system that allows to control a computer or any other device thanks to the brain activity. The BCI described in this paper is based on the P300 speller BCI paradigm introduced by Farwell and Donchin . An unsupervised algorithm is proposed to enhance P300 evoked potentials by estimating spatial filters; the raw EEG signals are then projected into the estimated signal subspace. Data recorded on three subjects were used to evaluate the proposed method. The results, which are presented using a Bayesian linear discriminant analysis classifier , show that the proposed method is efficient and accurate.