## 3 Results
### Online and Offline BCI Performance  
Figure 7 shows the online classification accuracies across sessions for the BCIs tested on the patient. A linear regression is fitted for each tested BCI to infer whether there was any performance tendency (only for those interfaces which were tested at least in three sessions and discarding the first four sessions, where the seven-class BCIs were tested). Additionally, the regression considering all interfaces together was also obtained. Accuracies of 80% and 90% were obtained in sessions 8, 12, and 9, with the first two for the AVNff interface and the last for the AVNsf interface. Overall, 59.1% of all sessions were above chance level once the two-class BCI was employed. The average online classification accuracy for the two-class BCI was \(56.4\pm 15.2\%\). Figure 6: ERP color maps obtained in EEGLab at the POz electrode during one calibration session for the AVNsf condition, shown for both the patient **(a)** and participant P1 from the control group **(b)**. Each line represents one trial of the session. The average of the amplitudes for the trials is shown at the bottom of the figure, generating the ERP graph. These color maps provide illustrative examples of the intertrial variability observed in the patient’s data during the same session, compared to a representative participant of the control group. In the patient’s data, intertrial variability is evident in the columns of the color map, where both positive and negative amplitude values occur for the same time point in subsequent trials. Conversely, the ERP color map of participant P1 exhibits consistent amplitudes across trials. The classification results vary from session to session within the same interface. For instance, in one session, the highest accuracy was 90% with AVNsf, whereas in the subsequent session, it dropped to 30%. These inconsistencies in results indicate that different factors influence the user's performance. Additionally, they highlight the difficulty of drawing conclusive comparisons between interfaces based solely on their performance. Looking at the overall tendency, we observe an increase of classification over time. When considering the interfaces individually, the use of familiar faces led to an improvement in classification performance when using both hybrid AV interfaces. Conversely, the use of visual-only interfaces resulted in a decline in classification across sessions. Figure 8 depicts the boxplots illustrating the distribution of the classification performance obtained during both calibration and online operation phases for the two-class BCIs. The results from calibration data were obtained using five-fold cross-validation. The highest median value in online classification was observed with the AVff condition. The highest accuracy in online classification was achieved with the AVNsf interface, reaching 90%, while the same interface also yielded the lowest classification result of 30%. The classification accuracy obtained from calibration data was significantly higher than that obtained online. This discrepancy highlights a lack of generalization in the classification models, likely due to the very high EEG variability. Therefore, the high classification results observed in offline calibration sessions did not translate to similar high classification results in online sessions. All results were obtained for nine repetitions. Figure 7: Online classification achieved by the patient for the different interfaces in each session (typically, each visit consisted of two sessions). The overall classification tendency (linear regression fitting) for the two-class BCI is depicted with thick dark blue line, while the classification tendencies for the Vff, AVff, and AVNff conditions are shown in yellow, green, and light blue, respectively. The remaining interfaces were not tested a sufficient number of times to provide reliable tendency fitting. The online operation was always performed with nine repetitions.