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268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 from the 12 CI users that participated in both studies. For all tests differences were considered significant when p < .05. 2.5. Test-retest reliability The test-retest reliability was also assessed by computing the coefficient of determination (R 2 ) for the correlation between N1 peak amplitudes and latencies in the TNS (first test) and in the ESS (retest). It is worth noting that the studies were not planned as retests and therefore different stimuli and paradigms were used. In addition, since the participants had at least 6 months experience with the CI device prior to taking part in either of the studies reported here, we assumed that the auditory system would have re-organized to a substantial extent and AEPs would have been established (Pantev et al., 2006). Thus, we predicted that, given careful artifact attenuation, the AEP N1 response should be characterized by a reasonable temporal stability. 3. Results 3.1. Environmental Sounds Study (ESS) After running CIAC, the reconstructed AEPs were similar to the ones obtained after manual selection of CI artifacts. Figure 3 shows the individual AEPs for a fronto-central electrode, as well as the grand average AEP and the N1 and P2 peak topographies. No significant differences were found for the N1 peak latency obtained after manual (MAN) and semi-automatic (S-AUTO) selection (t(17) = 1.27, n.s.). The P2 peak latency was also not significantly different (t(17) = 0.81, n.s.). When comparing the N1-P2 peak-to-peak amplitude, no significant differences were found (t(17) = -1.42, n.s.). Table 2 shows a summary of these comparisons. Interestingly, CIAC revealed a mean sensitivity of 91.7 % ± 0.12 and a mean specificity of 92.3 % ± 0.07, when its performance was compared to the manual selection. The correlation between age and N1-P2 peak-to-peak amplitude after the two types of CI artifact selection is shown in Figure 3-c. A significant negative correlation between age and peak-to- peak amplitude was found after automatic selection (AUTO: rs = -0.67, p = .002), replicating previous findings (MAN: rs = -0.70, p =.001) (Viola, et al., 2011). 3.2. Tones and Noise Study (TNS) 11
294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 After running CIAC, AEPs were reconstructed for the 12 CI users. The residual artifacts in this study were larger than when the same CI users were stimulated with environmental sounds. Figure 4 shows the AEPs for a fronto-central electrode before and after semi-automatic attenuation of CI artifacts for the two studies (ESS, left and TNS, right). Age and N1 amplitude were systematically correlated in both studies (ESS: rS = -0.48, p =.12; TNS: rS = -0.52, p =.09) but failed to reach significance. In this study CIAC revealed a mean sensitivity of 87.3 % ± 0.13 and a mean specificity of 65.6 % ± 0.10, when its performance was compared to the manual selection performed by an independent expert. The specificity was lower than in the previous study. After investigating the properties of the ICs selected by the algorithm and the ones selected by the expert, it was found that the expert seemed to be more conservative, while the algorithm selected also noise related ICs affected by residual CI artifact. 3.3. Test-retest reliability In order to investigate test-retest reliability, the coefficient of determination was computed for N1 peak amplitudes and latencies, as shown in Figure 5. In line with what was reported in previous studies where normal hearing individuals with a broad age range were assessed (Walhovd and Fjell, 2002), a high test-retest reliability was found for N1 latencies (rS = 0.77; R 2 = 0.59) and for N1 amplitudes (rS =0.69; R 2 = 0.48), suggesting that both parameters were reliably reconstructed in both studies. 4. Discussion CIAC is a tool that semi-automatically selects ICs representing CI artifacts, thus aiming for a more objective and efficient attenuation of electrical CI artifacts and facilitating the reconstruction of AEPs in CI users. The algorithm was optimized taking into account known properties of the ICs representing CI artifacts. We also aimed at reducing the number of computational steps and thresholds to a minimum. As a result we consider the selection of CI artifacts with CIAC a quick, comprehensive procedure. The performance of CIAC was evaluated using a total of 30 EEG recording from 18 adult CI users using different types of CI devices, and which were stimulated in two studies with either environmental sounds or tones and noise, separated in time approximately one year. CIAC revealed a 12
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