# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Correlation Coefficient Generation
SIR between the attended and the unattended speaker, as shown in (50), probabilistic attention measures with a large variability lead to SIRs with a large variability. ### Subjective Evaluation of Open-Loop and Closed-Loop AAD

For the open-loop and the closed-loop AAD phase, Figure 9 presents the perceived effort to follow the attended speaker, to ignore the unattended speaker, to switch attention between both speakers, and the level of story understanding. In terms of the perceived effort to follow the attended speaker and to ignore the unattended speaker (Figure 8(a),8(b)), it can be observed that the lowest median effort is obtained for the open-loop AAD, while a higher median effort is required for the closed-loop AAD, especially when using the SW-SSM algorithm. This can be attributed to the negative SIR improvement in some windows (see Figure 8), where the attended speaker is wrongly attenuated and the unattended speaker is wrongly amplified. Nevertheless, a statistical multiple comparison test (Kruskal-Wallis test followed by post-hoc [PERSON] and [PERSON] test) revealed no significant difference (\(p>0.05\)) between all considered open-loop and closed-loop AAD cases. Similarly, in terms of the effort to switch attention between both speakers (Figure 8(c)), a statistical multiple comparison test revealed no significant difference (\(p>0.05\)) between all considered open-loop and closed-loop AAD cases. These results show that the proposed closed-loop cognitive-driven gain controller system demands a similar perceived effort to follow the attended speaker, to ignore the unattended speaker and to switch attention compared to the open-loop AAD system. In terms of the level of story understanding (Figure 8(d)), the highest median understanding level is obtained for the open-loop AAD, while a lower median understanding level is

Figure 8: SIR improvement of the proposed cognitive-driven gain controller system when using the LW-GLM, LW-SSM, and SW-SSM algorithms. The boxplots display the median, the first quartile, the third quartile, the minimum, and the maximum of the SIR improvement across participants. Figure 7: Delay to detect a cued attention switch for (**a**) the open-loop AAD phase and (**b**) the closed-loop AAD phase when using the LW–GLM, LW–SSM, and SW-SSM algorithms. The boxplots display the median, the first quartile, the third quartile, the minimum, and the maximum of the switch detection delay across participants. obtained for the closed-loop AAD. This is consistent with the perceived cognitive effort (Figure 9a,b,c), where the open-loop AAD demands the lowest effort, possibly resulting in more cognitive resources available for story understanding compared to the closed-loop AAD. Nevertheless, a statistical multiple comparison test revealed no significant difference (\(p>0.05\)) between all considered open-loop and closed-loop AAD cases. Finally, Figure 10 presents the level of improvement in system usage achieved by the participants throughout the sessions of the closed-loop AAD phase. It can be observed for all considered AAD algorithms that a significant improvement in system usage is obtained. Figure 10: Subjective improvement in system usage for the closed-loop AAD system when using the LW–GLM, LW–SSM, and SW–SSM algorithms. Figure 9: Subjective evaluation results of the open-loop and the closed-loop AAD phase using the LW–GLM, LW–SSM, and SW–SSM algorithms in terms of perceived effort to (**a**) follow the attended speaker, (**b**) ignore the unattended speaker, (**c**) switch attention between both speakers, and (**d**) understand the story. ## 4 Discussion

The experimental results for the open-loop AAD system show that the largest median decoding performance is obtained by the LW-GLM algorithm (65%). This is in accordance with the experimental results in Reference [6], where it has been shown that open-loop AAD using a low number of electrodes with a correlation window smaller than 15 s results in a decoding performance lower than 75%.