Objective Binaural Loudness Balancing Based on 40-Hz Auditory Steady-State Responses. Part I: Normal Hearing

Eeckhoutte, Maaike Van; Wouters, Jan; Francart, Tom

doi:10.1177/2331216518805352

Cited by 6 publications

(3 citation statements)

References 44 publications

(57 reference statements)

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“…It is critical that clinicians ensure the programming of the device is optimized to facilitate the greatest possible improvement in hearing. CI programming involves selecting a current level that corresponds to the threshold level (T) and the most or maximum comfortable level (C or MCL) of hearing, thereby creating a dynamic range for each CI channel (Van Eeckhoutte et al 2018). Vaerenberg et al (2014) conducted a worldwide survey of CI programming procedures and found that 31% of clinics determined thresholds (Ts) only and set MCLs at intervals that were too high; 24% determined MCLs only and had Ts at 0 or 10%; while 45% determined both Ts and MCLs.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Cortical Auditory Evoked Potential in Cochlear Implant Users

et al. 2021

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Objectives:The primary goal of the study was to investigate electrical cortical auditory evoked potentials (eCAEPs) at maximum comfortable level (MCL) and 50% MCL on three cochlear implant (CI) electrodes and compare them with the acoustic CAEP (aCAEPs), in terms of the amplitude and latency of the P1-N1-P2 complex. This was achieved by comparing the eCAEP obtained with the method described and stimulating single electrodes, via the fitting software spanning the cochlear array and the aCAEP obtained using the HEARLab system at four speech tokens.Design: Twenty MED-EL (MED-EL Medical Electronics, Innsbruck, Austria) CI adult users were tested. CAEP recording with HEARLab System was performed with speech tokens /m/, /g/, /t/, and /s/ in free field, presented at 55 dB SPL. eCAEPs were recorded with an Evoked Potential device triggered from the MAX Programming Interface (MED-EL Medical Devices) with 70 msec electrical burst at 0.9 Hz at the apical (1), middle (6), and basal (10 or 11) CI electrode at their MCL and 50% MCL.Results: CAEP responses were recorded in 100% of the test subjects for the speech token /t/, 95% for the speech tokens /g/ and /s/, and 90% for the speech token /m/. For eCAEP recordings, in all subjects, it was possible to identify N1 and P2 peaks when stimulating the apical and middle electrodes. This incidence of detection decreased to an 85% chance of stimulation at 50% MCL on the same electrodes. A P1 peak was less evident for all electrodes. There was an overall increase in latency for stimulation at 50% MCL compared with MCL. There was a significant difference in the amplitude of adjacent peaks (P1-N1 and N1-P2) for 50% MCL compared with MCL. The mean of the maximum cross-correlation values were in the range of 0.63 to 0.68 for the four speech tokens. The distribution of the calculated time shift, where the maximum of the cross-correlation was found, was distributed between the speech tokens. The speech token /g/ had the highest number of valid cross-correlations, while the speech token /s/ had the lowest number. Conclusions:This study successfully compared aCAEP and eCAEP in CI users. Both acoustic and electrical P1-N1-P2 recordings obtained were clear and reliable, with good correlation. Latency increased with decreasing stimulation level, while amplitude decreased. eCAEP is potentially a better option to verify speech detection at the cortical level because it (1) uses direct stimulation and therefore creates less interference and delay of the sound processor and (2) creates more flexibility with the recording setup and stimulation setting. As such, eCAEP is an alternative method for CI optimization.

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Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Cortical Auditory Evoked Potential in Cochlear Implant Users

et al. 2021

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“…Similarly, symmetrical electrode positions were chosen on both the left and the right hemispheres, P4 and P8 in the RP region and P3 and P7 in the LP region [76]. This will remove the bias for a single hemisphere, which was indicated as a serious problem in several studies [77,78]. Locations P3, P4, Pz, P7 and P8 relate to perception and differentiation functions.…”

Section: E Electrode Placementmentioning

confidence: 99%

Study of Correlation Between EEG Electrodes for the Analysis of Cortical Responses Related to Binaural Hearing

et al. 2021

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Binaural hearing is the ability of the human auditory system to integrate information received from both ears simultaneously. Binaural hearing is fundamental in understanding speech in noisy backgrounds. Any disfunction in one or both ears could cause a disruption in the processing mechanism. Auditory evoked potentials (AEPs) are electrical potentials evoked by externally presented auditory stimuli from any part of the auditory system. A non-invasive technology, electroencephalography (EEG) is used for the monitoring of AEPs. The research aims to identify the best suited electrode positions through correlation analysis and analyse the AEP signals from the selected electrodes in order to detect binaural sensitivity of the human brain. The study evaluates the time-averaged EEG responses of normal hearing subjects to auditory stimuli. The stimuli used for the study are 500 Hz Blackman windowed pure tones, presented in either homophasic (the same phase in both ears) or antiphasic (180-degree phase difference between the two ears) conditions. The study focuses on understanding the effect of phase reversal of auditory stimuli, an under interaural time difference (ITD) cue, on the middle latency response (MLR) region of the AEPs. A correlation analysis was carried out between the eight different locations and as a result, Cz and Pz electrode positions were selected as the best suited positions for further analysis. The selected electrode signals were further processed in the time domain and frequency domain analysis. In the time domain analysis, it was found that Cz electrode for eight subjects out of nine and Pz electrode for seven subjects out of nine, had the larger area under signal curve obtained in the antiphasic condition than in the homophasic signals. Frequency domain analysis showed that the frequency bands 20 to 25Hz and 25 to 30Hz had the most energy when elicited by antiphasic stimuli than by homophasic stimuli. The findings of this study can be further utilised for the detection of binaural processing in a human brain.

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“…The rationale behind this channel selection strategy is to maintain symmetry. The symmetry criterion avoids bias to one hemisphere, which could be problematic as hemispheric differences are often found between subjects (e.g., Goossens et al, 2019; Van Eeckhoutte et al, 2018; Poelmans et al, 2012; Vanvooren et al, 2015). A similar channel selection strategy, also based on the utility metric, was proposed by Narayanan and Bertrand (2019) on an auditory attention decoding task, where the main goal was to optimize the topology of a wireless EEG sensor network (WESN), without imposing a symmetry constraint on the selected channels.…”

Section: Introductionmentioning

confidence: 99%

Effect of number and placement of EEG electrodes on measurement of neural tracking of speech

Montoya-Martínez

Vanthornhout

Bertrand

et al. 2019

Preprint

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Measurement of neural tracking of natural running speech from the 15 electroencephalogram (EEG) is an increasingly popular method in auditory neuroscience 16 and has applications in audiology. The method involves decoding the envelope of the 17 speech signal from the EEG signal, and calculating the correlation with the envelope 18 that was presented to the subject. Typically EEG systems with 64 or more electrodes 19 are used. However, in practical applications, set-ups with fewer electrodes are required. 20 Here, we determine the optimal number of electrodes, and the best position to place 21 a limited number of electrodes on the scalp. We propose a channel selection strategy, 22 aiming to induce the selection of symmetric EEG channel groups in order to avoid 23 hemispheric bias. The proposed method is based on a utility metric, which allows 24 a quick quantitative assessment of the influence of each group of EEG channels on 25 the reconstruction error. We consider two use cases: a subject-specific case, where 26 the optimal number and positions of the electrodes is determined for each subject 27 individually, and a subject-independent case, where the electrodes are placed at the same 28 positions (in the 10-20 system) for all the subjects. We evaluated our approach using 64-29 channel EEG data from 90 subjects. Surprisingly, in the subject-specific case we found 30 that the correlation between actual and reconstructed envelope first increased with 31 decreasing number of electrodes, with an optimum at around 20 electrodes, yielding 38% 32 higher correlations using the optimal number of electrodes. In the subject-independent 33 case, we obtained a stable decoding performance when decreasing from 64 to 32 channels. 34When the number of channels was further decreased, the correlation decreased. For 35 a maximal decrease in correlation of 10%, 32 well-placed electrodes were sufficient in 36 87% of the subjects. Practical electrode placement recommendations are given for 8, 37 16, 24 and 32 electrode systems. 38 42 also has applications in domains such as audiology, as part of an objective measure 43 of speech intelligibility (Vanthornhout et al., 2018; Lesenfants et al., 2019), and coma 44 science (Braiman et al., 2018). 45The relationship between the stimulus and the brain response can be studied using 46 two different models (e.g., Crosse et al., 2016; Lalor and Foxe, 2010; Ding and Simon, 47 2012; Verschueren et al., 2019; Vanthornhout et al., 2018): in the forward model (also 48 know as encoding model), we determine a linear mapping from the stimulus to the 49 brain response. On the other hand, in the backward model (also known as stimulus 50 reconstruction), we determine the linear mapping from the brain response to the stimulus. 51Backward models are referred to as decoding models, because they attempt to reverse 52 the data generation process. Both the forward and backward models involve the solution 53 of a linear least squares (LS) regression problem. The quality of the reconstruction is 54 ...

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Objective Binaural Loudness Balancing Based on 40-Hz Auditory Steady-State Responses. Part I: Normal Hearing

Cited by 6 publications

References 44 publications

Comparative Analysis of Cortical Auditory Evoked Potential in Cochlear Implant Users

Comparative Analysis of Cortical Auditory Evoked Potential in Cochlear Implant Users

Study of Correlation Between EEG Electrodes for the Analysis of Cortical Responses Related to Binaural Hearing

Effect of number and placement of EEG electrodes on measurement of neural tracking of speech

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