NEUROCUB >>
CLINICAL DEPLOYMENT
Who this program is for
• Rehabilitation centers
• Hospitals and post-acute departments
• Post-coma recovery units
• Stroke rehabilitation programs
• Traumatic brain injury (TBI) recovery
• Neurodegenerative condition support programs
• Supervised home-care rehabilitation
These are cases where patients often start from the most basic levels of perception, recognition, and attention — and where consistent daily cognitive load is essential, but human resources are limited.
Within clinical deployment, Neurocub is built around five core pillars:
• Neurocub bedside infrastructure — the NEUROBOT workstation, patient interface, and supervised interaction environment
• Structured cognitive therapy system — progressive exercise frameworks, recovery domains, and session architecture
• Adaptive engagement engine — dynamic adjustment of difficulty, pacing, and task selection for each patient
• Clinical performance metrics — attention, reaction, recognition, memory, task stability, and engagement dynamics
• Operational integration — how Neurocub fits into existing rehab workflows, documentation, and staff routines
Neurocub is designed to deliver continuous, supervised bedside cognitive therapy — creating new therapeutic capacity while maintaining structured clinical control, consistency, and measurable session data.
What participating clinics receive
Clinics participating in the Neurocub Clinical Deployment receive not a prototype — but a fully structured, evolving rehabilitation system.
This includes:
• A ready-to-deploy NeuroBot station configured for clinical or supervised home use
• An adaptive cognitive rehabilitation environment tailored to neurological recovery
• Objective patient analytics and recovery dynamics dashboards
• Tools for observing engagement, progress, fatigue, and regression patterns
• Reduction of routine cognitive workload on therapists and nursing staff
• A new layer of structured data for clinical observation and decision-making
Neurocub is designed to become a parallel cognitive environment that works continuously alongside medical teams — maintaining daily stimulation, tracking subtle changes, and supporting long-term recovery trajectories.
Core purpose of deployment
The Neurocub Clinical Deployment exists to build something that does not yet fully exist in rehabilitation practice:
A continuously working, AI-assisted cognitive recovery system that:
• Operates daily, not episodically
• Adapts dynamically, not manually
• Measures objectively, not subjectively
• Supports staff, not replaces them
• And creates structured recovery data where today it is fragmented or absent
This deployment framework keeps Neurocub clinically grounded while scaling daily bedside sessions.
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Neurocub It is a multi-layered therapeutic system designed to continuously interact with the patient, analyze neurological responses in real time, and dynamically build an adaptive cognitive recovery pathway.
Neurocub Therapy Architecture & Patient Interaction Model.
The physical and digital environment where therapy happens. This includes the Neurobot station, the visual field, touch input, sound, voice interaction, and future extensions such as gaze tracking and emotional response recognition. This layer is responsible for delivering stimuli and capturing every form of patient response.
A structured therapeutic content engine consisting of cognitive cards, visual objects, sequences, recognition tasks, memory blocks, attention drills, and progressive exercises. This is not random content — each element belongs to a classified therapeutic category and difficulty spectrum.
The analytical core of Neurocub. Here, the system processes patient reactions: timing, accuracy, hesitation, repetition patterns, engagement drops, recovery signals, and response stability. Every interaction becomes a neurological data point.
This layer dynamically adjusts therapy parameters in real time: difficulty, pacing, repetition, sensory load, task complexity, and cognitive domain focus. Instead of static programs, each patient receives a continuously evolving therapeutic pathway.
Responsible for sustaining participation, emotional stability, and cognitive willingness. This includes positive reinforcement, progress signaling, micro-achievements, pacing control, fatigue management, and frustration detection. The goal is to keep therapy active without overwhelming neurological load.
The clinical interface layer. Here therapists and clinicians access structured recovery data: engagement trends, cognitive stability, response curves, improvement vectors, and regression alerts. This transforms therapy sessions into objective, trackable recovery dynamics.
Neurobot V.2.0
How a patient works with the system
The therapeutic session cycle
Every Neurocub session follows a consistent therapeutic logic.
This structure allows standardization for clinical environments, while still enabling deep personalization for each patient.
1. Session initialization
A session begins with the selection of a starting mode.
This can be:
• Prescribed by a clinician or therapist
• Automatically generated by Neurocub based on accumulated patient data
• Continued from the previous therapeutic state
At this stage, the system sets initial cognitive load, sensory intensity, task category, and interaction format.
2. Presentation of a card or task
The system presents a cognitive stimulus:
• Visual recognition cards
• Object association
• Color, shape, or symbol tasks
• Memory and sequence blocks
• Attention and reaction exercises
Each element is not only therapy — it is also a measurement instrument.
3. Patient response capture
Neurocub is designed to register multiple layers of patient response, including:
• Touch interaction
• Choice and selection behavior
• Voice and speech attempts
• Reaction timing
• Repetition and hesitation patterns
• Engagement continuity
Future system expansions may include gaze tracking and emotional micro-expression recognition.
The system does not only register success or failure — it observes how the response was formed.
4. Real-time AI processing
As the patient interacts, Neurocub analyzes the session live.
The system evaluates:
• Cognitive load tolerance
• Stability of attention
• Reaction latency
• Error types and patterns
• Behavioral consistency
• Micro-signals of fatigue, confusion, or recovery
This analysis occurs continuously, not after the session ends.
5. Adaptive task generation
Based on live analysis, Neurocub adjusts the next interaction step:
• Simplifies or increases complexity
• Changes pacing
• Shifts cognitive domain focus
• Introduces reinforcement or recovery intervals
• Modifies sensory input intensity
This allows therapy to remain inside the optimal neurological stimulation zone..
6. Micro-state evaluation
Throughout the session, Neurocub builds short-interval micro-assessments:
• Cognitive stability
• Engagement depth
• Reaction coherence
• Progress or regression signals
These micro-evaluations prevent therapy from becoming static or blind.
7. Cognitive model update
At the end of each session cycle, Neurocub updates the patient’s internal cognitive profile.
This profile reflects:
• Functional cognitive domains
• Response dynamics
• Load tolerance
• Progress velocity
• Risk markers
• Engagement sustainability
This evolving model becomes the foundation for every future session.
Why this structure matters clinically
Because Neurocub operates as a closed-loop therapeutic system, it enables something traditional rehabilitation tools cannot:
• Continuous therapy instead of episodic sessions
• Objective dynamics instead of subjective impressions
• Adaptive pathways instead of fixed programs
• Structured cognitive data instead of fragmented observations
Each session simultaneously serves three purposes:
• Therapy
• Measurement
• Model training
This is what allows Neurocub to function not as a supplement, but as an intelligent rehabilitation environment.
Example: Bedside Cognitive Exercise on Neurobot Station 1.3
This video demonstrates a real bedside cognitive exercise performed on the Neurocub Neurobot Station 1.3. The workstation is positioned directly next to the patient’s bed, providing a stable and accessible interface for structured cognitive interaction without requiring patient mobility. During the session, the patient engages with adaptive visual and cognitive tasks while the system continuously analyzes responses and updates the patient’s neurological profile in real time. Such sessions are designed to be simple to initiate, comfortable to use, and suitable for daily repetition in post-acute, post-coma, or limited-mobility conditions. The goal of the clinical deployment is to evaluate how Neurocub can transform passive bedside time into structured therapeutic engagement while reducing routine cognitive workload on clinical staff.
Neurocub Mini-Session >>
Core Interaction Algorithm.
I. Session Initialization
Before the first card appears, Neurocub forms a starting therapeutic context.
The system uses:
• history of previous sessions (if available)
• current cognitive profile of the patient
• therapist-defined parameters (if set)
• built-in safety baselines
Based on this, Neurocub determines:
• starting difficulty level
• allowed cognitive load range
• priority cognitive domains
• emotionally neutral themes
• session mode (activation, stabilization, development, support)
If the patient is new, the system launches in a safe baseline mode.
II. First Card Selection
The first card is not random.
It is selected:
• from the lowest difficulty range
• with a high probability of successful interaction
• with emotionally neutral content
• with simple, clear interaction mechanics
The goal of the first card is calibration, not training.
III. Patient Interaction Event
The patient interacts with the card.
Neurocub captures not only correctness, but the full behavioral pattern:
• reaction time
• response duration
• micro-corrections
• number of attempts
• returns and pauses
• voice activity
• emotional markers
• refusal or withdrawal patterns
Each card becomes a cognitive event, not just a task.
IV. Real-Time AI Analysis
After each interaction, Neurocub launches a real-time data analysis pipeline:
• raw behavioral signals
• interaction metrics
• cognitive indicators
• emotional markers
• fatigue signals
• overload risk patterns
• learning dynamics
From this, the system forms a momentary cognitive state — a live snapshot of the patient’s current neurological condition.
V. Dynamic Patient Profile Update
After each step, Neurocub updates a multidimensional patient model, including:
• attention stability
• processing speed
• error structure
• decision confidence
• learning gradient
• fatigue accumulation
• emotional feedback
• engagement continuity
The profile is not stored.
It evolves.
VI. Therapy Decision Layer
(Core intelligence point)
At this stage Neurocub does not choose a card.
It makes a therapeutic decision.
For example:
• continue the current domain
• shift the domain
• simplify
• increase complexity
• stabilize
• introduce a pause
• change interaction mechanics
• return to a comfort zone
• gently expand cognitive load
This is a clinical logic layer, not a content layer.
VII. Next Task Generation
Only after the therapeutic decision is made does Neurocub select the next card — based on:
• the updated patient profile
• the previous card
• session trajectory
• active therapy mode
• emotional response
• progression tempo
A card is chosen not because it is similar —
but because it is optimal for the patient at that moment.
VIII. Continuous Closed Loop
The cycle continues:
interaction → analysis → profile update → therapy decision → next task
until one of the following occurs:
• cognitive load limit is reached
• fatigue patterns emerge
• session objective is completed
• interaction quality declines
IX. Session Consolidation
At the end of the session Neurocub:
• consolidates global changes
• updates the long-term patient model
• generates a session snapshot
• flags clinically relevant events
• prepares structured data for the clinical dashboard
This transforms a session into a clinical unit, not just an activity.
What makes this system fundamentally different
• A card is not a picture. It is a measurement instrument.
• A response is not an answer. It is a neurocognitive process.
• A profile is not statistics. It is a living patient model.
• Card selection is not recommendation. It is a therapeutic decision.
Neurocub Session Report — Core Blocks
This block provides the clinical passport of the session: duration, number of completed cards, therapy mode, and active cognitive domains. It defines the structural context of the session and serves as the reference frame for all further interpretation.
This section reflects how long and how stably the patient was able to interact with the system. It highlights engagement continuity, attention sustainability, and the moment when fatigue patterns began to emerge.
This block analyzes how cognitive performance evolved throughout the session. It reflects changes in processing speed, decision confidence, error structure, and learning patterns rather than isolated task results.
This section captures emotional and behavioral signals observed during interaction. It highlights frustration markers, positive engagement responses, motivation dynamics, and emotional stability throughout the session.
This block presents the updated multidimensional cognitive profile generated by Neurocub. It reflects changes in attention stability, learning speed, visual processing, fatigue sensitivity, and emotional response patterns.
This section summarizes clinically relevant events detected by the system and provides an AI-generated interpretation of the session. It highlights significant improvements, risk markers, and generates structured recommendations for the next session’s starting mode, load level, and cognitive focus.
Neurocub Session Report
Clinical Value & Clinical Deployment
Clinical Deployment. Building the future of cognitive recovery.
Neurocub is being introduced into real clinical environments as part of an early clinical deployment designed to explore a new model of cognitive recovery. The focus is bedside and in-room deployment, where patients can engage in structured cognitive interaction while the system continuously learns, adapts, and builds an evolving neurological profile. This stage is not about scale, but about clinical understanding — observing how adaptive cognitive systems can support rehabilitation workflows, enrich patient engagement, and generate new layers of recovery data.
By collaborating with hospitals, rehabilitation centers, and clinical teams, the Neurocub Clinical Deployment aims to validate therapeutic value, safety, usability, and clinical relevance. It is an exploration of how intelligent therapeutic systems can extend clinical capabilities, personalize cognitive recovery pathways, and transform passive hospital time into active neurological rehabilitation. This is the foundation of a future where cognitive recovery becomes measurable, adaptive, and continuously responsive to each individual patient.
Neurocub is designed as a scalable therapeutic system capable of supporting hundreds of patients in parallel across hospital departments and care environments. Every interaction generates structured data, allowing clinical teams to observe documented daily progress, emerging cognitive patterns, and long-term recovery dynamics instead of isolated session notes. As we see consistent, measurable cognitive engagement in patients, our goal is to extend this capability to as many individuals as possible who require daily neurological stimulation and structured recovery support. This clinical deployment marks the beginning of applying AI-driven analytics to cognitive therapy, forming the foundation for a new generation of adaptive, data-driven rehabilitation systems.
