## 4 Discussion  
The main goal of this study was to establish an effective communication channel with a CLIS patient. With this purpose, we tested multiple stimulation modalities with various features and combined different layouts and stimulation strategies. Our aim was to identify an effective combination that allows the exploration of perceptual and cognitive functions believed to be common among individuals in CLIS. The rationale behind the modifications implemented in the nine BCI versions was two-fold. Firstly, we aimed to adjust the interfaces to accommodate the perceptual limitations of the patient, which involved testing different visual layouts and stimuli sizes. Secondly, we sought to enhance selective attention and task engagement by introducing stimuli based on images with emotional content and using auditory white noise stimuli associated to non-target events to draw attention to target events. The study shows that the different types of BCI stimulation caused different responses but also that the responses greatly varied within the same type of stimulation. Both the AV and AVN conditions showed larger discrimination windows when compared to visual and auditory-only conditions. This positively correlates with the online performance, where these conditions also attained better results more times. The visual-only conditions showed lower results, likely influenced by possible eye vision deterioration and the patient's tendency to close their eyelids, impacting BCI performance. Nevertheless, the visual component remained important, especially when combined with auditory stimuli, as the combined performance surpassed that of visual-only and auditory-only conditions. The control group achieved very high classification results for all interfaces requiring a low number of repetitions to control the BCI system. This outcome was anticipated due to the simplicity of the BCIs affirming their functionality and the effectiveness of the classification pipeline. The best performing interface for this group was the AVNsf condition, with 98.0 \(\pm\) 4.5% accuracy for 2.6 \(\pm\) 1.5 repetitions for the seven-class BCI, which englobed audiovisual stimulation with the white noise audio component. However, no conclusions about the influence of the white noise component can be made for the CLIS patient since the corresponding interface for the patient had both the highest (90%) and lowest (30%) accuracies for the two-class BCI employed. This leads us to conclude that performance results heavily rely on the mental state of the participant during the sessions. The performance fluctuation across sessions may be related to variation in attention and vigilance, as well as in motivation and mood, due to antidepressant medication. The significant gap between calibration and online results obtained with the patient underscores the necessity of exploring alternative classification approaches that are more adaptable in contexts of high EEG variability. While the current processing methods applied are well-established and have shown effectiveness in healthy populations and individuals in advanced stages of ALS, exhibiting relatively stable data, they proved ineffective when applied to the highly variable data of the CLIS patient. The neurophysiological results obtained with the control group clearly show the expected ERPs in oddball paradigms, namely the N200 and P300 components. However, the neural responses observed in the CLIS patient are inconsistent and do not exhibit evident N200 and P300 ERPs. Moreover, there is no clear waveform discrimination between target and non-target responses, as shown in Figure 5. A notable exception comes from the data collected during one session under AVNsf condition, as depicted in Figure 4, where the P300 is evident and indicates discriminative power. The N200 is also present but appears weak and lacks discriminative power. Figure 5 highlights the low discrimination between target and non-target stimuli, with much smaller time windows observed for the CLIS patient compared to the control group.