# Toward understanding intra-user variability in BCI: a multi-session, multi-context experimental protocol

Pauline Dreyer, Manon Bourdil, Raphaëlle N Roy, Fabien Lotte

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Pauline Dreyer, Manon Bourdil, Raphaëlle N Roy, Fabien Lotte. Toward understanding intra-user variability in BCI: a multi-session, multi-context experimental protocol. CORTICO 2025 - Journées COllectif pour la Recherche Transdisciplinaire sur les Interfaces Cerveau-Ordinateur, Cortico, May 2025, Lyon, France. hal-05151171

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Submitted on 8 Jul 2025

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# TOWARD UNDERSTANDING INTRA-USER VARIABILITY IN BCI: A MULTI-SESSION, MULTI-CONTEXT EXPERIMENTAL PROTOCOL

Pauline Dreyer<fnref n="1" />, <fnref n="2" />, Manon Bourdil<fnref n="2" />, Raphaëlle N. Roy<fnref n="3" />, Fabien Lotte<fnref n="1" />, <fnref n="2" />

pauline.dreyer@inria.fr

Brain-computer interface (BCI) performance is known to vary due to several inter- and intra-user variability factors [1]. EEG signals and BCI performance fluctuate due to the interaction of several potential sources of variability including context, time of day, as well as user engagement and fatigue [2–5]. While machine learning helps mitigate these effects, an understanding of the specific variability factors affecting BCI performance and EEG features remains limited.

We present our ongoing experimental protocol to measure, understand and model the variabilities affecting BCIs. Each participant has to complete 6 sessions, conducted on different days, alternating mornings and afternoons. Sessions begin with baseline recordings, followed by 6 runs: 2 acquisition runs with sham feedback and 4 training runs with online feedback. Each run includes 3 mental tasks in random order: motor imagery (non-dominant hand movement imagery), mental subtraction (successive subtractions) and word association (generating words beginning with a presented letter). Participants perform the tasks using two different interfaces (Figure 1): (A) Graz, a low-stimulating context and (B) Brain Hero, a highly-stimulating context with background noise. They also have to complete multiple questionnaires, including assessments of psychological states (e.g., sleepiness, fatigue) and user experience (e.g., interest, engagement). Sessions end with a recorded debrief.

<figure><img src="image_2.png" /><figcaption>Figure 1: A) Graz interface, B) Brain Hero interface, C) BCI performance by context (Graz vs. Brain Hero) for the 3 subjects (ID1, ID3, ID5); significant difference for ID5 ($p = 0.005$).</figcaption></figure>

Preliminary results from 3 participants who completed all 6 sessions revealed no significant correlation between BCI performance and either psychological state or time of day. However, a significant context-related effect on performance was observed in one participant (Figure 1C).

This study integrates different mental tasks, interface contexts, time-of-day variations and repeated psychological state assessments across six sessions to comprehensively capture the dynamics of intra-user variability in BCI performance and EEG features.

<footnote n="1">¹Inria Center at the University of Bordeaux, Talence, 33405, France</footnote>

<footnote n="2">²LaBRI (Univ. Bordeaux / CNRS/ Bordeaux INP) France</footnote>

<footnote n="3">³Fédération ENAC ISAE-SUPAERO ONERA, Université de Toulouse, France</footnote>
References

[1] Saha S, Baumert M. Intra-and inter-subject variability in EEG-based sensorimotor brain computer interface: A review. Frontiers in computational neuroscience. 2020;13:87.

[2] Grosse-Wentrup M, Schölkopf B. A review of performance variations in SMR-based brain-computer interfaces (BCIs). Brain-Computer Interface Research: A State-of-the-Art Summary. 2013:39–51.

[3] Dehais F et al. Monitoring pilot’s mental workload using ERPs and spectral power with a six-dry-electrode EEG system in real flight conditions. Sensors. 2019;19(6):1324.

[4] Benaroch C et al. Long-term BCI training of a tetraplegic user: Adaptive riemannian classifiers and user training. Frontiers in Human Neuroscience. 2021;15:635653.

[5] Roy RN et al. Retrospective on the first passive brain-computer interface competition on cross-session workload estimation. Frontiers in Neuroergonomics. 2022;3:838342.