This chapter ties all the concepts learned so far by focusing on a case study involving patient P11. The process encompasses the design and implementation of an auditory brain–computer interface (BCI) that utilizes eye movements to facilitate communication independent of visual capabilities. The chapter outlines the experimental design, including signal acquisition, preprocessing, feature extraction, and the deployment of machine learning algorithms such as Support Vector Machine (SVM) to classify responses. Real-world trials illustrate the challenges and complexities involved, emphasizing the need for accuracy, reliability, and real-time feedback in the communication system. Through a detailed discussion of the methodology and results, the chapter not only demonstrates the practical application of theoretical knowledge in neurotechnology but also highlights the profound impact of these systems on enhancing the quality of life for individuals with severe communication barriers. This exploration serves as a foundational study for numerous studies like those discussed extensively in Volume 2.