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Article

ERP Signatures of Stimulus Choice in Gaze-Independent BCI Communication

by
Alice Mado Proverbio
1,2,* and
Yldjana Dishi
1
1
Cognitive Electrophysiology Laboratory, Department of Psychology, University of Milano-Bicocca, Piazza dell’Ateneo Nuovo 1, 20162 Milan, Italy
2
Master’s Program in “Human-Centered Artificial Intelligence” HCAI, Department of Philosophy, University of Milan, 20122 Milan, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(22), 11888; https://doi.org/10.3390/app152211888
Submission received: 25 September 2025 / Revised: 29 October 2025 / Accepted: 7 November 2025 / Published: 8 November 2025
(This article belongs to the Special Issue Brain–Computer Interfaces: Development, Applications, and Challenges)
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Abstract

This study aimed to identify electrophysiological markers (event-related potentials, ERPs) of intentional, need-related mental activity under controlled gaze fixation, with potential applications in brain–computer interface (BCI) development for individuals with severe motor impairments. Methods: Using stimuli from the PAIN Pictionary—a pictogram database for non-verbal communication in locked-in syndrome (LIS) contexts—neural responses were recorded via high-density EEG in 30 neurologically healthy adults (25 included after artifact-based exclusion). Participants viewed randomized sequences of pictograms representing ten fundamental need categories (e.g., “I am cold”, “I’m in pain”), with one category designated as the target per sequence. Each pictogram was followed by a visual cue prompting a button press: during training, participants executed the press; during the main task, they performed right-hand motor imagery while maintaining central fixation. Results: ERP analyses revealed a robust P300 response (450–650 ms; p < 0.0002) over centro-parietal regions for target cues, reflecting enhanced attentional allocation and stimulus choice. An early Contingent Negative Variation (CNV, 450–750 ms; p = 0.008) over fronto-lateral sites indicated anticipatory attention and motor preparation, while a left-lateralized late CNV (2250–2750 ms; p = 0.035) appeared to embody the preparation of a finalized motor plan for the forthcoming right-hand imagined response. A centro-parietal P600 component (600–800 ms; p = 0.044) emerged during response monitoring, reflecting evaluative and decisional processes. SwLORETA source analyses localized activity within a distributed network spanning prefrontal, premotor, motor, parietal, and limbic areas. Conclusions: These findings demonstrate that motor imagery alone can modulate pattern-onset ERP components without overt movement or gaze shifts, supporting the translational potential of decoding need-related intentions for thought-driven communication systems in individuals with profound motor impairments.

1. Introduction

Brain–computer interfaces (BCIs) based on ERPs enable direct communication without muscular output [1,2,3,4]. Among these, the P300 speller [5] remains a benchmark paradigm, exploiting the transient parietal positivity elicited by rare, attended stimuli. Unlike steady-state approaches, P300-based spellers rely on brief onset events, most commonly flashes of rows, columns, or symbols, to elicit a discriminable response when the target is attended. Subsequent refinements have focused on optimizing these transient-onset designs to improve accuracy and usability. Variants such as the checkerboard paradigm [6], region-based stimulation [7], and rapid serial visual presentation [8], have reduced spatial confounds, alleviated visual fatigue, and extended applicability to users with restricted gaze control. Manipulations of stimulus salience—through changes in color, shape, or motion—have further enhanced the signal-to-noise ratio of the P300. A graphical interface of the SSVEP-based BCI system usually consists of different commands, e.g., letters or symbols (even faces, e.g., [9]), that flicker at specific frequencies. User pays attention to a particular flickering command, while ignoring others, which induces SSVEP with the corresponding frequency (e.g., [10,11,12]).
One of the unresolved methodological issues in BCI research concerns the role of ocular fixation in stimulus-driven paradigms. In steady-state visual evoked potential (SSVEP)–based BCIs, the absence of strict control over eye position makes it difficult to disentangle whether the elicited responses reflect genuine covert attentional selection or simply overt gaze shifts toward the target. At this regard, an ERP/BCI study was conducted [13] to examine the role of covert vs. overt visual spatial attention, eye gaze, in shaping P300 amplitudes for a BCI speller system. 24 healthy volunteers participated in the study, and their electroencephalogram and eye movements were recorded in three different experimental conditions: overt attention, covert attention, and gaze fixation. Results showed that performance was significantly lower in the covert attention condition (5% median accuracy compared to 90% with overt attention). Gaze fixation without allocation of attention yielded an 80% accuracy. Quite similarly, Brunner et al. [14] examined the influence of eye gaze on the performance of a ‘P300’ speller BCI. They assessed 17 healthy participants using a ‘P300’ matrix speller under two distinct conditions. In the first condition (‘letter’), participants directed their gaze toward the target letter, whereas in the second condition (‘center’), they focused on a fixation cross located at the center of the matrix. The findings indicate that the effectiveness of the ‘P300’ matrix speller in typical subjects is substantially influenced by the direction of gaze.
This ambiguity is not trivial: if successful communication relies primarily on residual oculomotor control, such paradigms would be of limited utility for patients with severe motor impairment, particularly those in a locked-in state. In contrast, transient-onset P300 paradigms [15] allow for a more direct assessment of attentional selection mechanisms, independent of sustained fixation, thereby offering a potentially more robust framework for communication in populations with impaired gaze control.
Recently, ERPs have been employed to communicate motivational states through both visual pictograms and verbal commands [16,17,18]. Furthermore, a growing body of evidence indicates that ERPs can reliably reflect category-specific mental representations in immobilized and silent individuals, providing robust markers capable of discriminating between mental representations of multiple object classes. For example, studies have shown discriminable ERP patterns for ten different objects spanning faces, animals, language, and music [19], four objects including dogs, trees, and planes [20], as well as emotional facial expressions, such as happy versus neutral [21]. ERPs have also been shown to accurately predict six distinct visual categories—flowers, airplanes, cars, parks, sun, and old town—from neural responses [22]. In the present study, we implemented a carefully controlled experimental paradigm in which ocular fixation was strictly maintained, requiring participants to covertly select one class of target pictograms among six, each representing a fundamental physiological or psychological need. These targets were presented centrally in a pseudo-randomized sequence alongside distractors spanning ten distinct categories. Selection was carried out solely through cognitive intention, with participants merely imagining the act of pressing a key in response to a visual prompt. This design allowed us to isolate event-related potential (ERP) modulations specifically associated with selective attention, independent of overt eye movements, thereby offering a robust and adaptable framework for probing attentional mechanisms in individuals unable to execute ocular shifts. As summarized in Table 1, prior BCI paradigms have typically targeted either attention-based (P300/RSVP) or motor-imagery control streams, often relying on arbitrary or low-semantic stimuli and allowing free gaze. More recent gaze-independent or covert attention paradigms have constrained ocular behavior but still focus on arbitrary symbols or simple semantic cues. The present study uniquely integrates motivational, need-related pictograms, strict ocular fixation, and pure motor imagery without overt responses, revealing a co-modulation of P300, CNV, and P600 components within a single, semantically grounded paradigm. This combination distinguishes our work from conventional P300, RSVP, and motor imagery frameworks, establishing a conceptual foundation for future thought-driven communication of basic needs. What remains to be shown in translational BCI stages is the implementation and validation of an online decoding pipeline.
The present study was designed as a proof-of-concept neurophysiological investigation to establish the feasibility and signal validity of decoding need-related intentional states under controlled gaze fixation. We hypothesized that target pictograms would elicit more pronounced P300 and CNV components relative to non-targets, independent of gaze, even under conditions of absolute immobility. Additionally, we expected target response prompts, as compared to non-targets, to evoke an enhanced late positive component (P600), reflecting the monitoring and control processes involved in the execution of imagined button presses. Rather than implementing a full decoding pipeline, our primary goal was to characterize the electrophysiological architecture—in terms of ERP components and their cortical generators—underlying intentional, motor-imagery–based responses to semantically structured pictograms. This foundational step is essential for informing and optimizing subsequent brain–computer interface (BCI) implementations targeting patients with severe motor impairments.

2. Materials and Methods

2.1. Participants

Initial recruitment included 30 participants (15 males, 15 females), based on a priori power analysis (G*Power 3.1; δ ≥ 0.5, α = 0.05, Power = 0.8). Five were excluded due to incomplete tasks (n = 3) or excessive EEG noise and poor task engagement (n = 2). The final sample comprised twenty-five right-handed, neurotypical university students (14 males, 11 females; mean age = 22.08, SD = 2.49). All participants had completed upper secondary education; 10 held a master’s degree, 15 a bachelor’s degree. The sample was ethnically diverse: Latin American, Iranian (n = 3), Albanian (n = 2), Egyptian (n = 1), and Italian (n = 18). Exclusion criteria included psychiatric, neurological, or neurodevelopmental disorders, left-handedness, uncorrected visual impairments, and psychoactive substance use within 48 h. Laterality was confirmed via the Edinburg Inventory (M = 0.815, SD = 0.13). Italian proficiency was not required, allowing participation of international students. The study was approved on 17 February 2025 by the Department of Psychology “Research Evaluation Committee for minimal risk projects” (CRIP, protocol RM-2025-914) under the aegis of the University Ethics Committee. All participants provided their written informed consent; they were not paid but received academic credits for their participation. The study was funded by the Italian Ministry of University and Research (Grant 2023-NAZ-0206, PsyFuture—Department of Excellence 2023–2027).

2.2. Stimuli

Stimuli were selected from the validated “Motivational Pictionary” [29], comprising pictograms of young adults expressing ten motivational states and physiological needs. Adult validation (18–33 years) showed high communicative accuracy (98.4%) via categorization tasks and Likert-scale clarity ratings. Pictograms were equiluminant across categories (range: 71.4–74.5 cd/m2), as shown by ANOVA.
Three macro-categories were considered in this experimental paradigm: visceral needs (hunger, thirst), somatosensory sensations (cold, pain), and secondary needs (music, movement). Only six (out of twelve) classes of stimuli were selected to calibrate task difficulty for patients with neurological impairments or in coma, in line with BCI paradigms where the number of concurrent choices is generally limited to 4–6 per trial to maximize discriminability and response reliability while minimizing cognitive load. Need selection was based on their relevance to homeostatic regulation, essential care, and patient communication, with potential translational applications in BCIs for non-communicative patients. From the affective category, only fear was retained, due to its marked communicative salience in clinical contexts.
Motivational and emotional states were visually cued with small “speech clouds” to enhance interpretability (see Figure 1).

2.3. Procedure

The experimental paradigm consisted of 12 sequences programmed with Stim2 for Compumedics Neuroscan (Abbotsford, VIC, Australia) each comprising 26 pictograms interleaved with a visual prompt—a hand with the index finger extended—serving as a constant reminder of task requirements. Participants were instructed to maintain their right index finger poised on the response pad and to engage in motor imagery of a button press whenever a target stimulus was recognized. Across the task, 312 pictograms (excluding prompts) were presented. Sequences were organized around six motivational states—hunger, thirst, cold, pain, music, and movement—each functioning as a target in two sequences, with 15 target presentations per state. Pictograms depicting heat, play, fear, and sleep served exclusively as additional non-target distractors. Sequence order was individually randomized to minimize habituation and order effects. Each pictogram was presented for 2000 ms, followed by a 750 ms inter-stimulus interval (ISI). Prompts were displayed for 500 ms, with an inter-trial interval (ITI) of 1750 ms. Both the inter-stimulus interval (ISI) and inter-trial interval (ITI) were held constant throughout the experiment. Stimuli subtended a visual angle of 6°47′16′′ × 4°46′37′′ on a monitor positioned 114 cm from the participant, who was instructed to maintain central fixation throughout. To this end, a fixation cross was permanently displayed at the center of the screen. Prior to data collection, a structured training phase was administered. Participants first performed overt responses to designated target states; subsequently, they practiced the required task of vividly imagining the motor response without executing it. Training was conducted until participants demonstrated successful task acquisition. Task comprehension was confirmed, and participants were reminded to remain still, comfortable, and to sustain vivid motor imagery in correspondence with the presentation of target-related pictograms. The experimental protocol entailed the presentation of 314 visual stimuli, comprising 90 target instances and 224 non-target instances. Taken together with the corresponding prompts, the paradigm encompassed 628 discrete events. The total duration of the EEG acquisition was approximately 40 min. Following the experimental session, participants completed a subjective questionnaire assessing the vividness and ease of motor imagery and simulation. Specifically, participants were asked to assess both the ease and the perceived effectiveness of performing the simulated key press subsequent to their target choice. Ratings were provided on a 5-point Likert scale (1 = “not at all easy” to 5 = “very easy”).

2.4. EEG Recordings and Analysis

EEG was acquired via Compumedics Neuroscan system, with a 128 channel montage [30] referenced to linked mastoids (chosen to minimize lateralized bias and maintain comparability with previous EEG studies using similar paradigms) and maintained at <5 kΩ. Ocular activity was monitored with horizontal and vertical EOG (bipolar montage, supraorbital and outer canthus). Signals were sampled at 500 Hz, and band-pass filtered between 0.016 and 50 Hz, a range commonly adopted to remove slow drifts and high-frequency noise while preserving neural activity within the conventional EEG spectrum. ERPs were time-locked to stimulus onset via SynAmps 2/RT and Curry9 (Compumedics Europe GmbH, Freiburg, Germany). Trials exceeding ±50 μV were rejected; blinks were corrected (covariance reduction), and noisy channels (rarely) interpolated from four neighbors. On average, 90% of trials were retained per participant, ensuring sufficient data for reliable averaging.
Epochs were segmented into event-specific windows and analyzed in Curry9 and EEProbe (ANT Neuro GmbH, Berlin, Germany) ERPs were derived by offline averaging. Mean area amplitude was computed at electrode clusters of interest, based on topographical maps and previous literature. P300 was quantified at Cz, CPz, Pz, CCPP1h, and CCPP2h (450–650 ms, cue) and at Cz, CPz, and Pz (600–800 ms, prompt). Early CNV to cues was measured at F9, F10, FT9, FT10, F11, F12 (450–750 ms), and late CNV at the same sites (2250–2750 ms). A frontocentral N400 was measured at F3, F4, FC5, FC6 sites in response to prompts. For each component, three-way repeated-measures ANOVAs tested Targetness (target/non-target), Electrode, and Hemisphere (left/right) effects, separately for pictogram and prompt responses.
Tukey post hoc comparisons were carried out to test differences among means. The effect size for the statistically significant factors was estimated using partial Eta Squared (ηp2) and the Greenhouse-Geisser correction was applied to account for non-sphericity of the data. Statistical analyses were performed using STATISTICA software (version 10.0, StatSoft Inc., Tulsa, OK, USA).

2.5. Source Reconstruction

In order to identify the intracranial sources explaining the surface electrical potentials Standardized low-resolution electromagnetic tomography (sLORETA; [31]) was performed on ERP voltages. In particular, LORETA was applied to mean voltages in the 450–750 ms, and in the 2250–2750 ms intervals following target pictogram presentation, to capture the emergence of the contingent negative variation (CNV), and in the 600–800 ms interval following prompt onset, to capture the decision-related P3 response. LORETA is a discrete linear solution to the inverse EEG problem, and it corresponds to the 3D distribution of neural electric activity that maximises similarity (i.e., maximises synchronisation) in terms of orientation and strength between neighbouring neuronal populations (represented by adjacent voxels). In this study, an improved version of standardized weighted low-resolution brain electromagnetic tomography was used (swLORETA, [32]), which incorporates a singular value decomposition-based lead field weighting method. The source space properties included: localization within the grey matter; a grid spacing of 5 points (the distance between 2 calculation points) and an estimated signal-to-noise ratio (SNR) of 3, which defines the regularisation (higher values indicating less regularisation and therefore less blurred results). Using a value of 3–4 for SNR computation in Tikhonov’s regularisation results in superior accuracy for all assessed inverse problems. swLORETA was performed on the grand-averaged data to identify statistically significant electromagnetic dipoles (p < 0.05) in which larger magnitudes correlated with more significant activation.
The data were automatically re-referenced to the average reference (CAR) as part of the LORETA analysis. A realistic boundary element model (BEM) was derived from a T1-weighted 3D MRI dataset through segmentation of the brain tissue. This BEM consisted of a homogeneous compartment comprising 3446 vertices and 6888 triangles. Advanced source analysis (ASA) employs a realistic head model of three layers (scalp, skull and brain) created using the boundary element model. This realistic head model comprises a set of irregularly shaped boundaries and the conductivity values for the compartments between them. Each boundary is represented by a series of interconnected points, forming plane triangles to create an approximation. The triangulation leads to a more or less evenly distributed mesh of triangles as a function of the chosen grid value. A smaller value for the grid spacing results in finer meshes and vice versa. With the aforementioned realistic head model of three layers, the segmentation is assumed to include current generators of brain volume, including both grey and white matter. Scalp, skull, and brain region conductivities were assumed to be 0.33, 0.0042 and 0.33, respectively. The source reconstruction solutions were projected onto the 3D MRI of the Colin27 brain (also referred to as the Collins brain), supplied by the Montreal Neurological Institute (MNI). Individual MRIs were not available; therefore, this standardized template was used for all participants.
The probabilities of source activation based on Fisher’s F test were provided for each independent EEG source, whose values are indicated in a “unit” scale in nA (the larger the value, the more significant the activation). It should be noted, however, that the spatial resolution of swLORETA is somewhat limited compared to other neuroimaging techniques like MEG or fMRI. Both the head model’s segmentation and generation were conducted through the use of ASA4.10.1, a software program developed by ANT (Advanced Neuro Technology) [33].

3. Results

3.1. Behavioural Data

The repeated-measures ANOVA revealed a significant main effect of Need [F(5, 145) = 3.463, p = 0.005], indicating that efficacy/vividness ratings differed across motivational conditions. They were highest for hunger and thirst (p < 0.01), and weakly declined across the other conditions, though remaining within the effective range (Figure 2). The mean vividness ratings for the different motivational states and corresponding needs were as follows: thirst and the need to drink (M = 4.50, SE = 0.10, 95% CI [4.29, 4.71]); hunger and the need for food (M = 4.33, SE = 0.14, 95% CI [4.05, 4.62]); feeling cold and the need for warmth (M = 4.23, SE = 0.16, 95% CI [3.90, 4.57]); pain and the need for a painkiller (M = 4.10, SE = 0.15, 95% CI [3.78, 4.42]); movement (M = 4.03, SE = 0.16, 95% CI [3.72, 4.35]); music listening (M = 3.97, SE = 0.21, 95% CI [3.55, 4.39]).
Two participants were excluded from the EEG analyses owing to self-reported lack of full attentiveness during the session and an average efficacy score below 3.

3.2. Electrophysiological Results

The P300 component to pictograms was analyzed at centroparietal sites within the 450–650 ms post-stimulus interval. A repeated-measures ANOVA on mean area amplitudes elicited by pictograms revealed a significant main effect of Targetness [F(1, 24) = 20.02, p < 0.00016, ε = 1; ηp2 = 0.46] with much larger P300s to target (M = −0.91 μV, SE = 1.48) than non-target cues (M = −3.54 μV, SE = 1.06), as can be appreciated in Figure 3 and Table 2.
The ANOVA performed on CNV negative potential in the time window between 450 and 750 ms over the fronto-lateral and inferior fronto-temporal regions, the ANOVA revealed a significant main effect of Targetness [F(1, 24) = 8.39, p = 0.0079; ε = 1; ηp2 = 0.26], with larger amplitudes to target (−5.27 µV, SE = 0.70) than non-target cues (−4.28 µV, SE = 0.79).
Based on the grand averages, isocolour topographical voltage maps of the Early CNV component were generated, showing a predominantly anterior distribution of the motor readiness potential (see Figure 4b).
A Late negativity was quantified on the same sites within the 2250–2750 ms time window. The ANOVA yielded the significance of Targetness x Hemisphere [F(1, 24) = 5.02, p < 0.035, ε = 1; ηp2 = 0.17] with much larger negativity to target than non-target pictograms only over the left hemisphere, as shown by post hoc comparisons among means (see Table 2 and Figure 4c).
An anterior N400 was quantified over medial prefrontal sites in the 300–500 ms time window in response to prompts. The ANOVA showed no significance of targetness [F(1, 24) = 0.006, p = 0.94]. A P600 was quantified over centro/parietal sites in the 600–800 ms time window in response to prompts (Figure 5). The ANOVA showed the significance of targetness [F(1, 24) = 4.52, p = 0.0439; ε = 1; ηp2 = 0.16], with larger responses to target (2.58 μV, SE = 1) than non-target prompts (1.19 μV, SE = 1.2).

3.3. Results—Source Localization (swLORETA)

According to swLORETA the early CNV (450–750 ms) to target pictograms was primarily generated within bilateral frontal cortices, including the superior and middle frontal gyri (BA10, BA46), regions classically implicated in decision-making and selective attention (see Table 3 for a list of active electromagnetic dipoles and Figure 6a for neuroimaging data). Concurrent activation in premotor regions (BA6) of both hemispheres indicated preparatory motor activity linked to imagined button presses. Temporal lobe sources, particularly in the fusiform (BA37), superior (BA38, BA22), and inferior temporal gyri (BA20–21), supported processes of pictogram recognition and selective attention to socially or bodily relevant features. Together, these activations suggest an early integration of attentional selection, semantic/pictorial analysis, and motor readiness during motivational cue processing.
The late CNV (2250–2750 ms) revealed a stronger recruitment of frontal generators, with sustained activation of superior and middle frontal cortices (BA9–10), consolidating decision-making processes (see Table 4 and Figure 6b). Temporal activations (BA20–22, BA38) reflected ongoing pictogram decoding with contributions from regions associated with socio-emotional and motivational content.
Importantly, motor-related sources emerged more prominently at this stage, including premotor (BA6) and primary motor regions (BA4), alongside parietal sites (supramarginal gyrus, BA40) involved in embodiment and mirror neuron mechanisms. These results indicate a transition from attentional and semantic processing toward motor planning and preparation for intentional responses.
Finally, the P600 (600–800 ms) to response prompts engaged medial frontal generators (BA6, BA10), consistent with motor imagery, decision-making, and evaluative processes (see Table 5 and Figure 6c). Subcortical activations, particularly within the globus pallidus, together with limbic regions (uncus, insula), pointed to motivational and reward-related mechanisms. Additional fusiform and parietal activations (BA37, BA40) supported visuomotor embodiment and mirror neuron activity, while inferior frontal (BA47) and superior temporal sources (BA41–22) suggested involvement in motivational drive, craving, and inner speech. Overall, the P600 reflected an integration of decisional, motor, and motivational networks, consolidating the intentional selection of communicative acts.

4. Discussion

The present study aimed to assess whether reliable markers of target selection could be obtained from participants maintaining a fixed gaze in a pictogram-based communication paradigm. Previous literature [13,14] has highlighted the difficulty of disentangling the effects of attention from gaze in patients capable of eye movements, showing that the P300 is often unreliable or undetectable under fixed-gaze conditions. By contrast, other authors [24,28] provide evidence that gaze-independent P300-based BCIs are feasible: covert attention to sequentially presented characters allows accurate communication without eye movements, and alphanumeric stimuli can be effectively employed under varied display conditions without imposing significant cognitive load. Building on these findings, we sought to investigate a gaze-independent BCI system enabling patients to directly select the desired pictogram, rather than relying on indirect attentional effects toward alphanumeric targets.
ERP analyses revealed a distinct temporal sequence of neural activity associated with stimulus processing, motor preparation, and response monitoring. Initially, a robust P300 response emerged over centro-parietal regions (450–650 ms) for target cues, reflecting heightened attentional allocation and stimulus choice. Concurrently, an early Contingent Negative Variation (CNV), recorded over fronto-lateral sites (450–750 ms), signaled anticipatory attention and initial motor preparation. Later in the sequence, a left-lateralized late CNV (2250–2750 ms) appeared to specifically embody the preparation of a finalized motor plan for the forthcoming right-hand imagined response. Finally, the emergence of a centro-parietal P600 component (600–800 ms) during response monitoring highlighted the underlying evaluative and decisional processes required to complete the action. The present findings delineate a nuanced neural differentiation between target and non-target pictograms, underscoring the brain’s capacity to modulate attentional and motor preparatory processes in response to behaviorally salient stimuli. The pronounced centro-parietal P300 reflects enhanced attentional allocation and context updating, indicating that participants carefully evaluated the presented stimuli and intentionally selected the relevant one for communication. This stronger cognitive engagement supports faster and more accurate stimulus discrimination, which is essential for effective BCI control.
Early CNV amplitudes, recorded between 450 and 750 ms over fronto-lateral and inferior fronto-temporal regions, were notably augmented for target stimuli, suggesting the engagement of prefrontal and premotor areas in anticipatory motor preparation [34]. This anterior distribution of the early CNV component reflects the brain’s proactive stance in preparing for forthcoming motor actions, consistent with models of motor readiness [2,3,35,36,37,38]. The late CNV, quantified between 2250 and 2750 ms, exhibited a lateralized pattern, with enhanced negativity for target stimuli predominantly in the left hemisphere, controlling the right responding hand [38]. This lateralization implicates the contralateral motor cortex in the final stages of motor planning, highlighting the embodied nature of motor preparation processes. The P600 component elicited by response prompts further corroborates the integration of motor planning with evaluative and motivational processes. The amplified P600 response to target prompts suggests that motor imagery is intricately coupled with motivational and reward-related circuits, facilitating the translation of cognitive intentions into potential communicative actions.
Collectively, these ERP findings delineate a coherent temporal progression from attentional orienting (P300) and early motor readiness (early CNV) to lateralized motor preparation (late CNV) and evaluative integration (P600). This sequence underscores the sensitivity of ERP components to covert, intentional, and motivationally guided cognitive states, reinforcing their utility in brain–computer interface paradigms where overt motor responses or eye movements are constrained. While the current results highlight clear and reproducible ERP markers (P300, CNV, P600) associated with intentional, need-related motor imagery, we acknowledge that this study was not designed as a full-fledged applied BCI implementation. Instead, it represents a conceptual and methodological groundwork aimed at validating the neurophysiological feasibility of the paradigm. Establishing these electrophysiological signatures under strictly controlled conditions was an essential prerequisite to ensure the robustness and interpretability of subsequent decoding analyses and patient-based applications.
Source localization revealed that target pictograms engaged a distributed fronto-temporal network supporting decision-making, selective attention, and motor preparation. Early CNV generators included dorsolateral and superior frontal cortices (BA10, BA46), premotor regions (BA6), and temporal sites involved in pictogram recognition, indicating rapid integration of attentional and motor readiness signals. Late CNV sources extended into primary motor and parietal cortices (BA4, BA40), reflecting embodied motor planning. These circuits are very similar to the ones subtending the generation of CNV signals during overt motor actions [39,40], including the shift toward prefrontal cortex, middle frontal cortex and primary motor cortex for the late CNV stage (e.g., [41]). Critically, the P600 prompts recruited medial frontal and limbic structures, including the globus pallidus and insula, suggesting that motor imagery was tightly coupled with motivational and reward-related processes. The dorsal premotor cortex (PMd) is pivotal for selecting and preparing movements [42,43]. Functional imaging studies reveal a lateralization of PMd activity: the left PMd is engaged during movements of both hands, whereas the right PMd shows stronger activation predominantly during left-hand movements, with the exact pattern modulated by task demands. Furthermore, a large literature predicts involvement of the superior and medial prefrontal cortex BA10, one of the strongest active source for all ERP components, in higher-order decision-making processes, including choice selection and response initiation [44,45]. It is possible to identify clear distinctions between motor imagery and overt action: empirical evidence indicates that actual movements elicit more focal contralateral M1 activation [46], greater engagement of cerebellar [47] and subcortical circuits supporting coordination and timing, attenuated parietal “mirror-like” activity [48], and a reduced contribution from limbic regions as motivational processes yield to sensorimotor control.
One of the main strengths of the present work lies in bridging motor and cognitive potentials of non-invasive, shared-independent BCIs with neural activation data (specifically derived from LORETA) applied to the very markers selected for BCI control. The validation of these markers (P300, CNV, and P600) also relies on demonstrating that they reflect neural activity associated with decision making, stimulus selection, response preparation, and performance monitoring.
Finally, it is important to emphasize that, within this paradigm, participants maintained strict fixation throughout the task, ensuring that no overt eye movements contributed to the observed effects—unlike in standard P300-speller paradigms, where eye gaze is not systematically controlled. In paradigms where fixation is not enforced, as is typical in SSVEP studies (e.g., [49,50]), a confounding interaction emerges between overt attentional deployment and ocular gaze (see [13,51]), such that the advantages of BCI performance may be restricted to patients capable of executing eye movements. Thus, attentional orienting in this task was purely covert and gaze-independent, demonstrating that intentional, motivationally grounded communicative states can be decoded directly from distributed cortical and subcortical networks without reliance on ocular control.

5. Study Limitations

Several limitations of the present study should be acknowledged. First, the sample size was relatively small, which may limit the generalizability of the findings. Second, the study focused exclusively on healthy participants, leaving open questions about the applicability of the results to patient populations with neurological impairments. It should also be noted that our sample consisted of university students, who may be cognitively more capable than individuals in a minimally conscious state or with brain lesions—a common characteristic in many BCI studies, which may influence the generalizability of findings to clinical populations. Finally, the study design relied on a single experimental paradigm, and further research is needed to confirm whether the observed neuroelectrical patterns and the associated potential for BCI usability extend across different paradigms and real-world settings. A key limitation of the present study is the absence of an implemented decoding pipeline or online classification metrics. However, the current dataset provides the necessary signal characterization for training and benchmarking future classifiers. Our ongoing work focuses on developing and validating the decoding architecture—including feature extraction, probability calibration, and cross-session generalization—in both healthy and LIS populations. In this context, the present findings constitute a critical feasibility milestone toward a translational, gaze-independent BCI system for communication of basic needs.

6. Conclusions

The present study demonstrates that covert, gaze-independent motor imagery elicits a distributed fronto-temporal-parietal network encompassing prefrontal, motor, and limbic regions, reflecting the integration of attentional, motivational, and motor preparation processes. Notably, this paradigm allows for decoding intentional communicative states without reliance on eye movements or overt motor output. In comparison to overt actions, which predominantly activate contralateral M1 and subcortical motor circuits while diminishing limbic contribution, this approach offers a robust and flexible alternative to conventional P300-speller or motor-based BCI paradigms. These findings underscore the potential of utilizing motivationally grounded, covert neural signals for BCI applications, facilitating more natural and efficient communication channels for individuals with severe motor impairments. The high statistical significance of the analysis of variance applied to individual ERP signals reveals a pronounced similarity among the neuroelectrical markers, indicating a promising potential for optimal usability in BCI applications.

Author Contributions

Conceptualization, A.M.P.; methodology, A.M.P. and Y.D.; formal analysis, Y.D.; investigation, A.M.P. and Y.D.; resources, A.M.P.; data curation, Y.D. and A.M.P.; writing—original draft preparation, A.M.P. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Italian Ministry of University and Research under Grant No. 2023-NAZ-0206, PsyFuture—Dipartimento di Eccellenza 2023–2027, awarded to the Department of Psychology of the University of Milano-Bicocca.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki and approved by the Department of Psychology Research Evaluation Committee (CRIP, protocol RM-2025-914) for studies involving humans, on 17 February 2025.

Informed Consent Statement

Informed written consent was obtained from all subjects involved in the study.

Data Availability Statement

The stimuli and experimental procedure used in this study are available from the Bicocca Open Archive Research Data repository: Proverbio, Alice Mado; Pischedda, Francesca (2025), “Pictionary-based communication tool for assessing individual needs and motivational states in locked-in patients: P.A.I.N. set”, Bicocca Open Archive Research Data, V2, doi: 10.17632/bz3pkct536.2. All data supporting the findings of this study are included within the article. Additional information or EEG data sharing are available from the author upon reasonable request, particularly to support scientific collaboration (e.g., for single-trial P3 or CNV classification, development and implementation of a prototype).

Acknowledgments

We are grateful to Francesca Pischedda, Giulia Gnecchi, Nafiseh Shabani, Pasquale Scognamiglio and Milos Milovanovic for their support.

Conflicts of Interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Abbreviations

The following abbreviations were used in this manuscript:
ALSAmyotrophic Lateral Sclerosis
AUCArea Under the Curve
ANOVAAnalysis of Variance
ASAAdvanced Source Analysis
BCIBrain–Computer Interface
BEMBoundary element model
CARCommon Average Reference
CNVContingent Negative Variation
EBLEmotional Body Language
EEGElectroencephalogram
EOGElectro-oculogram
ERPEvent-Related Potential
ISIInter-stimulus Interval
ITIInter-trial Interval
ITRInformation Transfer Rate
LISLocked-in syndrome
LORETALow-Resolution Electromagnetic Tomography
MRIMagnetic Resonance Imaging
RSVPRapid Serial Visual Presentation
SEStandard Error
SSVEPSteady-state visual evoked potential

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Figure 1. Experimental trial structure. Each trial began with the presentation of a pictogram stimulus (2000 ms), followed by an inter-stimulus interval (ISI; 750 ms). A response prompt was then displayed (500 ms), after which an inter-trial interval (ITI; 1750 ms) occurred before the onset of the next trial.
Figure 1. Experimental trial structure. Each trial began with the presentation of a pictogram stimulus (2000 ms), followed by an inter-stimulus interval (ISI; 750 ms). A response prompt was then displayed (500 ms), after which an inter-trial interval (ITI; 1750 ms) occurred before the onset of the next trial.
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Figure 2. Perceived task efficacy. Mean ratings of simulation efficacy (y-axis) are shown for six categories of needs (x-axis). Data points represent mean values, with error bars indicating standard errors of the mean. Overall, participants judged simulations as moderately to highly effective, with drink and food needs rated as relatively more efficacious than the others did.
Figure 2. Perceived task efficacy. Mean ratings of simulation efficacy (y-axis) are shown for six categories of needs (x-axis). Data points represent mean values, with error bars indicating standard errors of the mean. Overall, participants judged simulations as moderately to highly effective, with drink and food needs rated as relatively more efficacious than the others did.
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Figure 3. ERPs to pictograms: P300. (a) Grand-average ERP waveforms are shown for representative centro-parietal and parietal electrodes (C1, Cz, C2, CCP1h, CPz, CCP2h, CP1, Pz, CP2, CPP1h, CPP2h, P1, P2) for target (black) and non-target (red) stimuli. Time (s) is plotted on the x-axis and amplitude (µV) on the y-axis. Robust P300 components are evident at posterior sites for target relative to non-target stimuli, providing decision-making evidence. (b) Scalp topographies (right) illustrate the statistical distribution of the target vs. non-target difference between 450 and 650 ms, with maximal positivity over centro-parietal regions. The color scale represents voltage differences (µV).
Figure 3. ERPs to pictograms: P300. (a) Grand-average ERP waveforms are shown for representative centro-parietal and parietal electrodes (C1, Cz, C2, CCP1h, CPz, CCP2h, CP1, Pz, CP2, CPP1h, CPP2h, P1, P2) for target (black) and non-target (red) stimuli. Time (s) is plotted on the x-axis and amplitude (µV) on the y-axis. Robust P300 components are evident at posterior sites for target relative to non-target stimuli, providing decision-making evidence. (b) Scalp topographies (right) illustrate the statistical distribution of the target vs. non-target difference between 450 and 650 ms, with maximal positivity over centro-parietal regions. The color scale represents voltage differences (µV).
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Figure 4. ERPs to pictograms: CNV (a) Grand-average ERP waveforms show evoked responses to “target” (black line) and “non-target” (red line) pictograms at various electrode sites (e.g., F7, F9, F11). An early and a late-onset CNV negative deflection are evident, which were more pronounced for target pictograms, reflecting preparatory motor and cognitive processing. (b) Topographical maps of potential differences. Topographical maps illustrate the spatial distribution of the early (450–750 ms) and late (2250–2750 ms) CNVs. Potential differences are shown on a color scale from blue (negative potential, indicating greater activity) to red (positive potential). Early CNV maps show a negative potential in the fronto-central region for both target and non-target conditions, evidencing the start of clear motor programming activity. (c) The late CNV maps highlight the significant difference between conditions: a more extensive and negative potential is visible for the “target” condition (target minus non-target difference), consistent with refined motor response preparation.
Figure 4. ERPs to pictograms: CNV (a) Grand-average ERP waveforms show evoked responses to “target” (black line) and “non-target” (red line) pictograms at various electrode sites (e.g., F7, F9, F11). An early and a late-onset CNV negative deflection are evident, which were more pronounced for target pictograms, reflecting preparatory motor and cognitive processing. (b) Topographical maps of potential differences. Topographical maps illustrate the spatial distribution of the early (450–750 ms) and late (2250–2750 ms) CNVs. Potential differences are shown on a color scale from blue (negative potential, indicating greater activity) to red (positive potential). Early CNV maps show a negative potential in the fronto-central region for both target and non-target conditions, evidencing the start of clear motor programming activity. (c) The late CNV maps highlight the significant difference between conditions: a more extensive and negative potential is visible for the “target” condition (target minus non-target difference), consistent with refined motor response preparation.
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Figure 5. ERPs to response prompts. (a) ERP grand-average waveforms s show responses evoked by “target” (red line) and “non-target” (black line) prompts at various electrode sites. The traces show two key components: a negative-going deflection around 400 ms post-stimulus, the N400, and a late positive-going wave, the P600. The P600 was notably more pronounced for the target condition, suggesting its role in response evaluation and decision-making processes. (b) The topographical maps illustrates the scalp distribution of the P600 component (600–800 ms) for the target minus non-target condition. It can be appreciated the strong positive potential (red) over the central-parietal region for the target prompts. This widespread positive distribution is characteristic of the P600 and is consistent with its function in response evaluation and execution. The larger P600 response to targets indicates response monitoring and control actviities specifically dedicated to target stimuli.
Figure 5. ERPs to response prompts. (a) ERP grand-average waveforms s show responses evoked by “target” (red line) and “non-target” (black line) prompts at various electrode sites. The traces show two key components: a negative-going deflection around 400 ms post-stimulus, the N400, and a late positive-going wave, the P600. The P600 was notably more pronounced for the target condition, suggesting its role in response evaluation and decision-making processes. (b) The topographical maps illustrates the scalp distribution of the P600 component (600–800 ms) for the target minus non-target condition. It can be appreciated the strong positive potential (red) over the central-parietal region for the target prompts. This widespread positive distribution is characteristic of the P600 and is consistent with its function in response evaluation and execution. The larger P600 response to targets indicates response monitoring and control actviities specifically dedicated to target stimuli.
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Figure 6. (a) Coronal, axial and sagittal brain images of swLORETA applied to the early CNV potential and highlighting the active common electromagnetic dipoles elicited by target pictograms in the 450–650 time window. (b) Brain images of swLORETA applied to the late CNV potential and highlighting the active common electromagnetic dipoles elicited by target pictograms in the 2250–2750 ms time window. (c) Brain images of swLORETA applied to P600 response and highlighting the active common electromagnetic dipoles elicited by target propmpts in the 600–800 ms time window. The different colours represent differences in the magnitude of the electromagnetic signal (in nA) recorded in the specific time window. The numerical labels beneath each brain image denote the corresponding slice coordinates, referenced to the Montreal Neurological Atlas. L = Left, R = Right; A = anterior, P = posterior.
Figure 6. (a) Coronal, axial and sagittal brain images of swLORETA applied to the early CNV potential and highlighting the active common electromagnetic dipoles elicited by target pictograms in the 450–650 time window. (b) Brain images of swLORETA applied to the late CNV potential and highlighting the active common electromagnetic dipoles elicited by target pictograms in the 2250–2750 ms time window. (c) Brain images of swLORETA applied to P600 response and highlighting the active common electromagnetic dipoles elicited by target propmpts in the 600–800 ms time window. The different colours represent differences in the magnitude of the electromagnetic signal (in nA) recorded in the specific time window. The numerical labels beneath each brain image denote the corresponding slice coordinates, referenced to the Montreal Neurological Atlas. L = Left, R = Right; A = anterior, P = posterior.
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Table 1. Comparative overview of paradigms in P300, RSVP, and motor-imagery BCI research. Legend: ALS = Amyotrophic Lateral Sclerosis; ITR = Information Transfer Rate; AUC = Area Under the Curve; RSVP = Rapid Serial Visual Presentation.
Table 1. Comparative overview of paradigms in P300, RSVP, and motor-imagery BCI research. Legend: ALS = Amyotrophic Lateral Sclerosis; ITR = Information Transfer Rate; AUC = Area Under the Curve; RSVP = Rapid Serial Visual Presentation.
Study Type/ParadigmKey ReferencesStimulus Type & SemanticsResponse ModeGaze ConstraintEEG/ERP DataPopulationDecoding AI AlgorithmKey Features
Classic P300 SpellerFarwell & Donchin [5]; Krusienski et al. [23]; Townsend et al. [6]; Treder & Blankertz [7]; Riccio et al. [24]Alphanumeric grid; arbitrary symbolsOvert or covert selection via attentionFree gazeP3b (300–600 ms)Healthy, ALS/LISYes—classification accuracy, ITRCommunication via character-based target detection
RSVP-based BCIAcqualagna & Blankertz [8]; Spüler [25] Rapid serial visual presentation of words/picturesCovert attention; button pressPartially constrainedP3a/P3bHealthy, ALSYes—AUC, ITRSequential single-target detection
Motor Imagery BCIsWolpaw et al. [4]; Neuper & Pfurtscheller [26]; Chaudhary et al. [3]; Pacheco et al. [27]Abstract cues or limb iconsMotor imagery (hand/foot)Free gazeCNV, μ/β ERD/ERSHealthy, ALSYes—classifier accuracyContinuous motor imagery control paradigms
Gaze-independent/Covert attention paradigmsBrunner et al. [14]; Riccio et al. [24]; Liu et al. [28]Semantic or affective symbols; sequential character groupsCovert attention, sometimes button pressFixed gaze/gaze-independentP300 variantsHealthy, LISPartialAttention-based BCIs with minimal eye movement
Present study (current work)—Motivational pictograms (“PAIN Pictionary”) representing need statesMotor imagery only (no overt response)Strict central fixation (no gaze shifts)Joint P300–CNV–P600 modulationHealthy (feasibility step)No decoding in current study (planned pipeline)First demonstration of this approach
Table 2. Mean area amplitude values (µV) and standard errors (SE) for ERP components elicited by cues and response prompts. The table reports mean area amplitudes, SE, and 95% confidence intervals (CIs) for target and non-target stimuli across ERP components. The P300 (450–650 ms) showed larger mean amplitudes for target compared to non-target stimuli, indicating stimulus selection. The early CNV (450–750 ms), reflecting specific motor preparation, revealed a pronounced negative shift in response to target stimuli. The late CNV (2250–2750 ms), reflecting more advanced motor programming, indicated lateralized motor-preparatory activity of the right hand, i.e., the effective key-press motor simulation. Finally, the P600 (600–800 ms) to response prompts showed significantly enhanced amplitudes for targets relative to non-targets. Sample size across conditions was N = 25. Hem. = hemisphere.
Table 2. Mean area amplitude values (µV) and standard errors (SE) for ERP components elicited by cues and response prompts. The table reports mean area amplitudes, SE, and 95% confidence intervals (CIs) for target and non-target stimuli across ERP components. The P300 (450–650 ms) showed larger mean amplitudes for target compared to non-target stimuli, indicating stimulus selection. The early CNV (450–750 ms), reflecting specific motor preparation, revealed a pronounced negative shift in response to target stimuli. The late CNV (2250–2750 ms), reflecting more advanced motor programming, indicated lateralized motor-preparatory activity of the right hand, i.e., the effective key-press motor simulation. Finally, the P600 (600–800 ms) to response prompts showed significantly enhanced amplitudes for targets relative to non-targets. Sample size across conditions was N = 25. Hem. = hemisphere.
ERPs to Pictograms—Mean Area Amplitude Values
CategoryHem.Mean AreaSE−95%+95%N
P300 (450–650)
Target−0.9131.476−3.9602.13425
Non Target−3.5431.057−5.724−1.36225
Early CNV (450–750 ms)
Target−5.2660.700−6.711−3.82125
Non Target−4.2790.787−5.904−2.65325
Late CNV (2250–2750 ms)
TargetLeft−1.4971.275−4.1281.13425
TargetRight0.3361.225−2.1922.864225
Non-TargetLeft−0.5661.176−2.991.861625
Non TargetRight−0.6131.362−3.4232.19725
ERPs to response prompts—Mean area amplitude values
P600 (600–800 ms)
Target
Non-Target		2.5851.0170.4864.68425
1.1931.195−1.2733.65825
Table 3. Electro-magnetic dipoles significantly active during the processing of cues in the early CNV/P300 time window (450–750 ms). Legend: BA = Brodmann Areas; Magn. = dipole strength (in nA); Hem. = hemisphere.
Table 3. Electro-magnetic dipoles significantly active during the processing of cues in the early CNV/P300 time window (450–750 ms). Legend: BA = Brodmann Areas; Magn. = dipole strength (in nA); Hem. = hemisphere.
EARLY CNV TO TARGET PICTOGRAMS (450–750 ms)
Magn.Hem.LobeGyrusBAFunctional Correlates
2.435LFSuperior Frontal10Decision making
2.431LTSuperior Temporal22/38/20/37/18Visual attention (Face & Body)
2.158RFSuperior Frontal10Decision making
1.628RFMiddle Frontal46Selective attention
1.437LFSuperior Frontal6Premotor (Right hand)
1.411RFPrecentral6Premotor (Left hand)
1.383RTInferior/Middle Temporal20/21/37Visual attention
1.02LPPostcentral2Somatosensory
0.95RCerebPost. Lobe, Declive/Motor preparation
Table 4. Electro-magnetic dipoles significantly active during the processing of cues in the late CNV time window (2250–2750 ms). Legend: BA = Brodmann Areas; Magn. = dipole strength (in nA); Hem. = hemisphere; EBL = emotional body-language.
Table 4. Electro-magnetic dipoles significantly active during the processing of cues in the late CNV time window (2250–2750 ms). Legend: BA = Brodmann Areas; Magn. = dipole strength (in nA); Hem. = hemisphere; EBL = emotional body-language.
LATE CNV TO TARGET PICTOGRAMS (2250–2750 ms)
Magn.Hem.LobeGyrusBAFunctional Correlates
4.787RFSuperior/Middle Frontal10Decision making
2.937LTMiddle Temporal21Visual attention
2.705RTMiddle/Superior Temporal20/22/42/37Attention, EBL, Motivation
2.218RPSupramarginal40Mirror neuron/embodiment
1.992LFMiddle Frontal9Decision making
1.837LOCuneus18/38Visual processing (Body)
1.264LFSuperior Frontal6Premotor (right hand)
1.21RCerebPost. Lobe, Declive/Motor preparation
1.191LFSuperior Frontal6Premotor (right hand)
1.173RFPrecentral4M1, Motor command (BCI)
1.057RFSuperior Frontal8Attention (FEF)
0.878RLimbicCingulate24Empathy, Motivation
Table 5. Electro-magnetic dipoles significantly active during processing of response prompts in the P600 time window (600–800 ms post-stimulus). Legend: BA = Brodmann Areas; Magn. = dipole strength (in nA); Hem. = hemisphere.
Table 5. Electro-magnetic dipoles significantly active during processing of response prompts in the P600 time window (600–800 ms post-stimulus). Legend: BA = Brodmann Areas; Magn. = dipole strength (in nA); Hem. = hemisphere.
P600 TO RESPONSE PROMPTS (600–800 ms)
Magn.Hem.LobeGyrusBAFunctional Correlates
1.484RFMedial Frontal6MNS—Motor imagery and preparation, embodiment
1.381RFMedial Frontal10Decision making
1.133ROFusiform20/37Occipital body area (Hands)
1.105LFMiddle Frontal46Attention
1.081LFSuperior Frontal10Decision making
0.922RFMiddle Frontal47Motivation/Crave
0.875R, LBasal GangliaGlobus Pallidus/Craving; Motivation; Reward
0.809L, RPInferior Parietal40MNS—Motor imagery, embodiment
0.793LLimbicUncus36Craving
0.752LTSuperior Temporal41Inner speech
0.727LFInferior Frontal47Motivation & Reward
0.657LOMiddle Temporal22EBL/Motivation
0.543RSublobarInsula13Craving
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Proverbio, A.M.; Dishi, Y. ERP Signatures of Stimulus Choice in Gaze-Independent BCI Communication. Appl. Sci. 2025, 15, 11888. https://doi.org/10.3390/app152211888

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Proverbio AM, Dishi Y. ERP Signatures of Stimulus Choice in Gaze-Independent BCI Communication. Applied Sciences. 2025; 15(22):11888. https://doi.org/10.3390/app152211888

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Proverbio, Alice Mado, and Yldjana Dishi. 2025. "ERP Signatures of Stimulus Choice in Gaze-Independent BCI Communication" Applied Sciences 15, no. 22: 11888. https://doi.org/10.3390/app152211888

APA Style

Proverbio, A. M., & Dishi, Y. (2025). ERP Signatures of Stimulus Choice in Gaze-Independent BCI Communication. Applied Sciences, 15(22), 11888. https://doi.org/10.3390/app152211888

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