## 5 Conclusions  
This work has introduced and validated a method that is able to detect the covariate shift occurrence induced by variations in the positioning of the EEG headset during multiple experimental sessions. The method has been tested both in a controlled setting, in which the differences in headset positioning were simulated by considering near electrodes (e.g., Fz with FPz, Pz with POz), and in a more realistic setting, in which a commercial wearable headset has been used, and it was mimic the situation in which the user was expected to wear the headset in a similar position, or in a completely different position, with respect to an initial one. Results showed that the isolation forest method proposed in this study was able to significantly detect the presence of the covariate shift phenomenon by using just 15 s of new coming data, independent of the severity of the anomaly, and in a completely not supervised way (i.e., the methods needs just original data, but does not need any knowledge about the kind of anomaly that could happen on new recorded data). Our proposed solution differs significantly from existing solutions and studies in the literature for several reasons. Primarily, we utilize a wearable device to mimic what would probably happen in an out-of-the-lab situation. This approach simulates, in fact, the use of passive BCI applications outside of controlled laboratory environments. Furthermore, our method stands out as it does not rely on using an already collected test dataset, whether labelled or unlabelled. It solely calibrates on the data from one session (assumed to be devoid of data affected by covariate shift) and can be used in real-time to predict, for instance, the presence of Covariate Shift during the recording of new sessions. This implies its capability to identify these variations as they occur, without the need for pre-existing data. Additionally, the application field on which our method was developed and tested involves headset movements during different recording sessions in passive BCI applications for neurometric analysis, particularly for analyzing cognitive workload. In addition, applying a simple linear normalization to data does not allow compensation for the negative effect induced by the different positions, and this behavior could be related to the lack of stationarity hypothesis in the EEG signal. We believe that the proposed normalization-based correction method performs well in the laboratory task but not in the realistic one due to the stability of the EEG signal during the recording of the resting-state sessions (i.e., open-eyes). Conceptualization, D.G., F.B. and P.A. ; Data curation, D.G. and G.T. ; Formal analysis, D.G. and A.G.; Funding acquisition, F.B. ; Investigation, D.G. and P.A. ; Methodology, D.G. and N.S. ; Project administration, P.A. ; Resources, D.G. and P.A. ; Software, D.G. ; Supervision, P.A. ; Validation, D.G., V.R., G.B. and G.D.F. ; Visualization, D.G. ; Writing--original draft, D.G. ; Writing--review and editing, D.G., N.S. and P.A. All authors have read and agreed to the published version of the manuscript. This work was co-funded by the European Commission through H2020 projects \"MIND-TOOTH: Wearable device to decode human mind by neurometrics for a new concept of smart interaction with the surrounding environment\" (GA n. 950998) and \"FITDRIVE: Monitoring devices for overall FITness of Drivers\" (GA n. 953432). The individual grants \"CHALLENGES: CompreHensive frAmework to aLEge andaNalyse surGEons' Stress\" (Bando Ateneo Medio 2021), \"BRAINORCHES-TRA: Multimodal teamwork assessment through hyperscanning technique\" (Bando Ateneo Medio 2022) provided by Sapienza University of Rome to [PERSON], \"REMES-Remote tool for emotional states evaluation\" provided to [PERSON], and \"HEAVX-Aviation: Advanced tool for Human Factors evaluation for the AUXiliary systems assessment inAviation\", provided by Sapienza University of Rome to [PERSON] are also acknowledged.