## 3 Results
### Neurophysiological Analysis
#### 3.1.1 CLIS Patient  
Figure 4a shows the ERPs for target and non-target events at channel PO8 obtained during the calibration in the session that provided the best online BCI performance (AVNsf, which achieved 90% accuracy for the two-class BCI) at channel PO8. The P300 component can be seen in this experiment with an amplitude of \(-3.06\)\(\upmu\)V, as well as a N400 ERP with an amplitude of \(-8.15\)\(\upmu\)V, which can be associated with semantic and face processing [27]. These two components are also statistically different between target and non-target stimuli, which shows that the patient was perceptually and cognitively aware and performing the task correctly. Figure 3: Overview of the BCI pipeline used in this study. The participant engages in the task with a P300-based BCI based on visuo-auditory stimulation. The collected data undergoes preprocessing followed by feature extraction, where the most relevant features are selected for classification using a Naïve-Bayes classifier across seven possible classes. A Yes/No filter may be used to limit the choice to only these two classes, and finally feedback is provided for the detected symbol/word. Unfortunately, this well-defined and discriminative pattern was not consistent across sessions and interfaces. Figure 5 displays the ERPs average for target and non-target events for all V, A, AV, and AVN conditions on channels Fz and POz, along with the time windows where the target and non-target events are statistically different (green bars, two-paired \(t\)-test, \(p<0.05\)). In contrast to the previous case (involving only one session), plots in Figure 5 do not exhibit the expected target ERPs. Additionally, we observe that target and non-target ERPs are very similar. Although there are time segments showing statistical significance, they are widely spread and not associated with well-defined peaks. The absence of well-defined patterns in the waveform makes data analysis more challenging in narrowing down relevant time windows. The audio component shows an increase in the amplitude in the frontal region, which is not observed in the occipitopairetal region. For both the A and V conditions, time windows where the target and non-target events are statistically different are very few and small (around 450 ms and 650 ms at POz for the V condition, and around 300 ms at Fz and 450 ms at POz for the A condition). However, when we combine visual and audio stimulation in both the AV and AVN conditions, the number and width of statistically different time windows increase (large discriminative window between 500 and 700 ms at POz for the AV condition, and several discriminative windows between 450 and 650 ms in the AVN condition for the same electrode). This indicates that the hybrid interfaces have a better potential for online classification in this patient than a V-only or A-only type of stimulation. Figure 4: **(a)** ERPs for the target and non-target stimuli obtained in one calibration session for the AVNsf condition for the CLIS patient at electrode PO8. The green bars indicate time windows where target and non-target ERPs were statistically different (two-paired \(t\)-test, \(p<0.05\)); **(b)** Grand average ERPs for the control group under the same condition. EEG data were not normalized for both groups. To infer the variability of the intertrial signal, a representative example, collected from one session with the AVNsf condition, is shown in Figure 6a. The color map displays inconsistent amplitude across trials, indicating a lack of coherence in the perceptual and cognitive processing of the oddball paradigm. In contrast, in Figure 6b, the color map from one of the control group participants for the same condition shows consistent intertrial responses to the stimuli. Furthermore, the variability was quantitatively measured through the signal-to-noise (SNR) ratio.