The Acoustic Change Complex in Response to Frequency Changes and Its Correlation to Cochlear Implant Speech Outcomes

Abstract

One of the biggest challenges that face cochlear implant (CI) users is the highly variable hearing outcomes of implantation across patients. Since speech perception requires the detection of various dynamic changes in acoustic features (e.g., frequency, intensity, timing) in speech sounds, it is critical to examine the ability to detect the within-stimulus acoustic changes in CI users. The primary objective of this study was to examine the auditory event-related potential (ERP) evoked by the within-stimulus frequency changes (F-changes), one type of the acoustic change complex (ACC), in adult CI users, and its correlation to speech outcomes. Twenty-one adult CI users (29 individual CI ears) were tested with psychoacoustic frequency change detection tasks, speech tests including the Consonant-Nucleus-Consonant (CNC) word recognition, Arizona Biomedical Sentence Recognition in quiet and noise (AzBio-Q and AzBio-N), and the Digit-in-Noise (DIN) tests, and electroencephalographic (EEG) recordings. The stimuli for the psychoacoustic tests and EEG recordings were pure tones at three different base frequencies (0.25, 1, and 4 kHz) that contained a F-change at the midpoint of the tone. Results showed that the frequency change detection threshold (FCDT), ACC N1′ latency, and P2′ latency did not differ across frequencies (p > 0.05). ACC N1′-P2 amplitude was significantly larger for 0.25 kHz than for other base frequencies (p < 0.05). The mean N1′ latency across three base frequencies was negatively correlated with CNC word recognition (r = −0.40, p < 0.05) and CNC phoneme (r = −0.40, p < 0.05), and positively correlated with mean FCDT (r = 0.46, p < 0.05). The P2′ latency was positively correlated with DIN (r = 0.47, p < 0.05) and mean FCDT (r = 0.47, p < 0.05). There was no statistically significant correlation between N1′-P2′ amplitude and speech outcomes (all ps > 0.05). Results of this study indicated that variability in CI speech outcomes assessed with the CNC, AzBio-Q, and DIN tests can be partially explained (approximately 16–21%) by the variability of cortical sensory encoding of F-changes reflected by the ACC.

The Acoustic Change Complex Compared to Hearing Performance in Unilaterally and Bilaterally Deaf Cochlear Implant Users

“…The ACC can also be directly electrically evoked by changing the stimulation site from one electrode to another (i.e., spatial ACC; Brown et al 2008;He et al 2014;Hoppe et al 2010;Kim et al 2009;Mathew et al 2017;Scheperle & Abbas 2015b). Interestingly, the ACC has been reported to correlate well with psychophysical measures (He et al 2014;Mathew et al 2017) and speech perception (Han & Dimitrijevic 2020;Liang et al 2018;McGuire et al 2021;Scheperle & Abbas 2015b) in CI users. Since frequency changes are essential components in natural sounds including speech, the present study will focus on the ACC evoked by frequency changes, which we previously studied in NH and hearing-impaired subjects (Vonck et al 2019(Vonck et al , 2021; the latter paper confirming correlations with speech perception.…”

Relationship Between the Ability to Detect Frequency Changes or Temporal Gaps and Speech Perception Performance in Post-lingual Cochlear Implant Users

“…According to the FCDT results, the adult CI was divided into two groups: good and poor. The speech test results of the good CI group were significantly better than those of the poor CI group ( McGuire et al, 2021 ). In a study of changing stimulated electrodes, there was a robust correlation between electrode-discrimination capacities and speech-perception performance in CI children with auditory neuropathy spectrum disorder (ANSD) ( He et al, 2014 ).…”

“…The amplitudes of all the stimuli were normalized. Similar stimuli were used in some other studies ( Liang et al, 2018 , 2020 ; Zhang et al, 2019 ; McGuire et al, 2021 ).…”

Within- and across-frequency temporal processing and speech perception in cochlear implant users

“…For the across-frequency gap detection, the post-marker CAEP is nearly similarly strong for all gap conditions because the post-marker CAEP is mainly evoked by the acoustic changes consisting of the frequency change (from 2 kHz to 1 kHz in this study) and the gap. Therefore, this post-gap CAEP is a type of the acoustic change complex (ACC) that increases the amplitude with the increase of salience of the sound change in gap and frequency [ 42 , 59 , 63 , 69 ]. Although the participants were instructed to just pay attention to the silent gap and ignore the frequency difference between the pre- and post-gap markers, the latter may have acted as a distractor so that the participants cannot fully engage their cognitive attention to perform the across-frequency gap detection and produced a much larger value for GDT across than GDT within .…”

“…The EEG data was digitally band-pass filtered (0.1 to 30 Hz with a 6 dB/octave roll-off), segmented from 100 ms prior to the stimulus onset (-100 ms) to 200 ms beyond the offset of the post-gap marker, and baseline corrected using the pre-stimulus window (-100 to 0 ms). Subsequent analysis was completed in EEGLAB 13.6.5b ( http://sccn.ucsd.edu/eeglab ) running under Matlab R2017b (The Mathworks, Natick, MA), as in previous studies from our lab and other researchers [ 56 – 59 ]. Briefly, the data were visually checked to remove approximately 10% of the segments that contained non-stereotyped artifacts.…”