# Closed-Loop Cognitive-Driven Gain Control of Competing Sounds Using Auditory Attention Decoding
## 2 Methods
### Correlation Coefficient Generation
It should, however, be noted that the decoding performance in Reference [6] was obtained based on an optimal EEG electrode configuration, whereas the decoding performance reported in this paper was obtained based on a fixed EEG electrode configuration. In addition, the experimental results show that there is no significant difference in decoding performance between the open-loop and the closed-loop AAD system using the proposed AAD algorithms. This is consistent with the experimental results in Reference [5], where no significant difference in decoding performance between an open-loop and a closed-loop AAD system using visual feedback has been observed. The experimental results show that the LW-GLM and LW-SSM algorithms using the large correlation window yield a larger median decoding performance compared to the SW-SSM algorithm using the small correlation window. The large correlation window provides a larger amount of data from the reconstructed attended envelope and the envelopes of the speech signals compared to the small correlation window, resulting in more discriminative correlation coefficients, more reliable probabilistic attention measures and a larger decoding performance. This is in accordance with the experimental results in Reference [4; 6], where it has been shown that a larger correlation window results in a larger decoding performance. In addition, the experimental results show that the LW-GLM algorithm yields a larger median decoding performance than the LW-SSM algorithm. This may be explained by the fact that the LW-GLM algorithm infers the probabilistic attention measures based on the mean differential correlation coefficients rather than the absolute value of the correlation coefficients, hence providing a larger dynamic range including positive and negative values. In conclusion, the results demonstrate the feasibility of closed-loop AAD in an online fashion, enabling the listener to interact with an adaptive gain controller (as an ideal speech enhancement algorithm) for a scenario with two competing speakers. On the one hand, the closed-loop cognitive-driven gain controller system improves the SIR between the attended and the unattended speaker. This may make it easier to follow the attended speaker, ignore the unattended speaker and switch attention between both speakers, resulting in a lower cognitive effort compared to open-loop AAD. On the other hand, the closed-loop cognitive-driven gain controller system introduces a significant delay to detect attention switches, which causes the attended speaker to be wrongly attenuated and the unattended speaker to be wrongly amplified for sometime. This may make it more difficult to follow the attended speaker and ignore the unattended speaker, resulting in a higher cognitive effort compared to open-loop AAD. Nevertheless, the subjective evaluation results indicate that overall the closed-loop cognitive-driven gain controller system demands a similar effort as open-loop AAD. A delay to detect attention switches significantly influences the performance of the closed-loop cognitive-driven gain controller system. Recently, methods that are able to decode auditory attention with low delay have been proposed, e.g., based on a state-space model [9; 18], neural networks [12; 18; 35], and common spatial patterns [36]. Therefore, investigating the potential of fast AAD methods for a closed-loop cognitive-driven gain controller system to detect attention switches could be interesting as future work. While the closed-loop AAD experiments were performed without incorporating a practicing phase for the participants, the subjective evaluation results suggest that a significant improvement in system usage was obtained throughout the closed-loop AAD experiment. Future work could, therefore, investigate the impact of incorporating a practicing phase on the decoding and the speech enhancement performance of the cognitive-driven gain controller system.