Industry Use Cases

This section showcases realistic BCI applications powered by NimbusSDK’s RxLDA and RxGMM models across healthcare, assistive technology, research, and productivity domains.

Healthcare & Medical Devices

Motor Imagery Rehabilitation System

Challenge: Help stroke patients regain motor function through neurofeedback-guided rehabilitation. Solution: Real-time motor imagery detection using RxLDA for 4-class motor imagery (left hand, right hand, feet, tongue).

Python
Julia

from nimbus_bci import NimbusLDA, StreamingSession
from nimbus_bci.data import BCIMetadata
import numpy as np
from dataclasses import dataclass, field
from typing import List, Dict
from datetime import date

# Rehabilitation session manager
@dataclass
class RehabilitationSession:
    model: NimbusLDA
    patient_id: str
    baseline_accuracy: float
    session_history: List[Dict] = field(default_factory=list)

def create_rehab_session(patient_id: str) -> RehabilitationSession:
    # Train or load motor imagery model
    clf = NimbusLDA()
    # Load calibration data and fit
    X_calib = np.random.randn(100, 16)  # Replace with real calibration data
    y_calib = np.random.randint(0, 4, 100)
    clf.fit(X_calib, y_calib)
    
    print(f"Running baseline assessment for patient {patient_id}...")
    baseline_acc = 0.65  # run_assessment_trial()
    
    return RehabilitationSession(clf, patient_id, baseline_acc, [])

def run_therapy_session(session: RehabilitationSession, session_number: int) -> Dict:
    # Configure streaming for real-time feedback
    metadata = BCIMetadata(
        sampling_rate=250.0,
        paradigm="motor_imagery",
        feature_type="csp",
        n_features=16,
        n_classes=4,
        chunk_size=250  # 1-second chunks for responsive feedback
    )
    
    streaming_session = StreamingSession(session.model.model_, metadata)
    
    # Track performance
    successes = 0
    total_trials = 20
    confidences = []
    
    for trial in range(1, total_trials + 1):
        print(f"\n--- Trial {trial}/{total_trials} ---")
        
        # Patient performs motor imagery task (4 seconds)
        cued_task_label = np.random.randint(0, 4)
        task_names = ["Left Hand", "Right Hand", "Feet", "Tongue"]
        print(f"Cue: {task_names[cued_task_label]}")
        
        # Collect 4 chunks (4 seconds total)
        for chunk_idx in range(4):
            # In real application: acquire from EEG device
            chunk = np.random.randn(16, 250)  # acquire_eeg_chunk()
            
            # Real-time processing
            result = streaming_session.process_chunk(chunk)
            
            # Provide immediate visual feedback
            if result.confidence > 0.7:
                # display_positive_feedback(result.prediction)
                pass
        
        # Finalize trial
        final_result = streaming_session.finalize_trial(method="weighted_vote")
        confidences.append(final_result.confidence)
        
        # Check success
        if final_result.prediction == cued_task_label:
            successes += 1
            print(f"✓ Correct! (Confidence: {final_result.confidence:.3f})")
            # provide_success_feedback()
        else:
            print(f"✗ Incorrect (Confidence: {final_result.confidence:.3f})")
            # provide_encouragement()
        
        streaming_session.reset()
    
    # Session results
    accuracy = successes / total_trials
    mean_confidence = np.mean(confidences)
    improvement = accuracy - session.baseline_accuracy
    
    session_data = {
        "session_number": session_number,
        "accuracy": accuracy,
        "mean_confidence": mean_confidence,
        "improvement": improvement,
        "date": date.today().isoformat()
    }
    
    session.session_history.append(session_data)
    
    print(f"\n=== Session {session_number} Complete ===")
    print(f"Accuracy: {accuracy * 100:.1f}%")
    print(f"Mean Confidence: {mean_confidence:.3f}")
    print(f"Improvement vs Baseline: {improvement * 100:.1f}%")
    
    return session_data

# Clinical deployment
patient_session = create_rehab_session("P001")

# Daily therapy sessions (recommended: 3-5 per week)
for session_num in range(1, 11):
    results = run_therapy_session(patient_session, session_num)
    
    # Track progress in clinical records
    # log_to_patient_record(patient_session.patient_id, results)
    
    # Check for significant improvement
    if results["improvement"] > 0.3:
        print("\n🎉 Significant improvement detected!")
        print("Consider advancing to next rehabilitation stage")

using NimbusSDK
using Statistics

# Rehabilitation session manager
mutable struct RehabilitationSession
    model::RxLDAModel
    patient_id::String
    baseline_accuracy::Float64
    session_history::Vector{Dict}
end

function create_rehab_session(patient_id::String)
    # Load motor imagery model
    model = load_model(RxLDAModel, "motor_imagery_4class_v1")
    
    # Initial assessment
    println("Running baseline assessment for patient $patient_id...")
    baseline_acc = run_assessment_trial()
    
    return RehabilitationSession(model, patient_id, baseline_acc, [])
end

function run_therapy_session(session::RehabilitationSession, session_number::Int)
    # Configure streaming for real-time feedback
    metadata = BCIMetadata(
        sampling_rate = 250.0,
        paradigm = :motor_imagery,
        feature_type = :csp,
        n_features = 16,
        n_classes = 4,
        chunk_size = 250  # 1-second chunks for responsive feedback
    )
    
    streaming_session = init_streaming(session.model, metadata)
    
    # Track performance
    successes = 0
    total_trials = 20
    confidences = Float64[]
    
    for trial in 1:total_trials
        println("\n--- Trial $trial/$(total_trials) ---")
        
        # Patient performs motor imagery task (4 seconds)
        # cued_task = ["left_hand", "right_hand", "feet", "tongue"][rand(1:4)]
        cued_task_label = rand(1:4)
        println("Cue: $(["Left Hand", "Right Hand", "Feet", "Tongue"][cued_task_label])")
        
        # Collect 4 chunks (4 seconds total)
        chunk_predictions = Int[]
        chunk_confidences = Float64[]
        
        for chunk_idx in 1:4
            # In real application: acquire from EEG device
            chunk = acquire_eeg_chunk()  # Simulated
            
            # Real-time processing
            result = process_chunk(streaming_session, chunk)
            push!(chunk_predictions, result.prediction)
            push!(chunk_confidences, result.confidence)
            
            # Provide immediate visual feedback
            if result.confidence > 0.7
                display_positive_feedback(result.prediction)
            end
        end
        
        # Finalize trial
        final_result = finalize_trial(streaming_session; method=:weighted_vote)
        push!(confidences, final_result.confidence)
        
        # Check success
        if final_result.prediction == cued_task_label
            successes += 1
            println("✓ Correct! (Confidence: $(round(final_result.confidence, digits=3)))")
            provide_success_feedback()
        else
            println("✗ Incorrect (Confidence: $(round(final_result.confidence, digits=3)))")
            provide_encouragement()
        end
    end
    
    # Session results
    accuracy = successes / total_trials
    mean_confidence = mean(confidences)
    improvement = accuracy - session.baseline_accuracy
    
    session_data = Dict(
        "session_number" => session_number,
        "accuracy" => accuracy,
        "mean_confidence" => mean_confidence,
        "improvement" => improvement,
        "date" => today()
    )
    
    push!(session.session_history, session_data)
    
    println("\n=== Session $session_number Complete ===")
    println("Accuracy: $(round(accuracy * 100, digits=1))%")
    println("Mean Confidence: $(round(mean_confidence, digits=3))")
    println("Improvement vs Baseline: $(round(improvement * 100, digits=1))%")
    
    return session_data
end

# Clinical deployment
patient_session = create_rehab_session("P001")

# Daily therapy sessions (recommended: 3-5 per week)
for session_num in 1:10
    results = run_therapy_session(patient_session, session_num)
    
    # Track progress in clinical records
    log_to_patient_record(patient_session.patient_id, results)
    
    # Check for significant improvement
    if results["improvement"] > 0.3
        println("\n🎉 Significant improvement detected!")
        println("Consider advancing to next rehabilitation stage")
    end
    
    # Recommended: 1-2 day break between sessions
end

Clinical Results

Application: Motor imagery neurofeedback for stroke rehabilitation
Model: RxLDA 4-class motor imagery
Latency: 15-20ms per chunk (real-time feedback)
Typical Accuracy: 65-85% after calibration
Sessions: 20-30 sessions for measurable improvement

P300-Based Communication for Locked-In Syndrome

Challenge: Enable communication for patients with locked-in syndrome who cannot speak or move. Solution: P300 speller using RxGMM for binary target/non-target detection.

Python
Julia

from nimbus_bci import NimbusGMM
import numpy as np
from dataclasses import dataclass

# P300 Speller Implementation
@dataclass
class P300Speller:
    model: NimbusGMM
    character_matrix: np.ndarray
    intensification_count: int

def create_p300_speller() -> P300Speller:
    # Train P300 detection model (binary classification)
    clf = NimbusGMM(n_components=2)
    # Load calibration data and fit
    X_calib = np.random.randn(100, 16)  # Replace with real P300 calibration
    y_calib = np.random.randint(0, 2, 100)  # 0=non-target, 1=target
    clf.fit(X_calib, y_calib)
    
    # Standard 6x6 character matrix
    char_matrix = np.array([
        ['A', 'B', 'C', 'D', 'E', 'F'],
        ['G', 'H', 'I', 'J', 'K', 'L'],
        ['M', 'N', 'O', 'P', 'Q', 'R'],
        ['S', 'T', 'U', 'V', 'W', 'X'],
        ['Y', 'Z', '1', '2', '3', '4'],
        ['5', '6', '7', '8', '9', '0']
    ])
    
    return P300Speller(clf, char_matrix, 10)  # 10 repetitions per row/col

def spell_character(speller: P300Speller) -> dict:
    print("Focus on the target character...")
    
    # Storage for row/column scores
    row_scores = np.zeros(6)
    col_scores = np.zeros(6)
    
    # Present intensifications
    for rep in range(speller.intensification_count):
        # Flash each row
        for row in range(6):
            # flash_row(row)
            eeg_response = np.random.randn(16)  # record_p300_epoch()
            
            # Detect P300
            result = detect_p300(speller.model, eeg_response)
            
            if result["prediction"] == 1:  # Target detected
                row_scores[row] += result["confidence"]
        
        # Flash each column
        for col in range(6):
            # flash_column(col)
            eeg_response = np.random.randn(16)  # record_p300_epoch()
            
            # Detect P300
            result = detect_p300(speller.model, eeg_response)
            
            if result["prediction"] == 1:  # Target detected
                col_scores[col] += result["confidence"]
    
    # Identify target character
    target_row = np.argmax(row_scores)
    target_col = np.argmax(col_scores)
    selected_char = speller.character_matrix[target_row, target_col]
    
    # Calculate confidence
    overall_confidence = (row_scores[target_row] + col_scores[target_col]) / \
                        (2 * speller.intensification_count)
    
    print(f"Selected: {selected_char} (Confidence: {overall_confidence:.3f})")
    
    return {"character": selected_char, "confidence": overall_confidence}

def detect_p300(model: NimbusGMM, eeg_epoch: np.ndarray) -> dict:
    # Reshape to (1, n_features) for single trial
    features = eeg_epoch.reshape(1, -1)
    
    # Run inference
    prediction = model.predict(features)[0]
    probabilities = model.predict_proba(features)[0]
    confidence = probabilities[prediction]
    
    return {"prediction": prediction, "confidence": confidence}

# Clinical deployment
speller = create_p300_speller()

print("P300 Speller Ready")
print("Patient can now spell messages...")

# Spell a message
message = []
for i in range(10):  # Spell 10 characters
    result = spell_character(speller)
    
    if result["confidence"] > 0.75:
        message.append(result["character"])
        print(f"Message so far: {''.join(message)}")
    else:
        print("Low confidence - retry")

using NimbusSDK

# P300 Speller Implementation
struct P300Speller
    model::RxGMMModel
    character_matrix::Matrix{Char}
    intensification_count::Int
end

function create_p300_speller()
    # Load P300 detection model
    model = load_model(RxGMMModel, "p300_binary_v1")
    
    # Standard 6x6 character matrix
    char_matrix = [
        'A' 'B' 'C' 'D' 'E' 'F';
        'G' 'H' 'I' 'J' 'K' 'L';
        'M' 'N' 'O' 'P' 'Q' 'R';
        'S' 'T' 'U' 'V' 'W' 'X';
        'Y' 'Z' '1' '2' '3' '4';
        '5' '6' '7' '8' '9' '0'
    ]
    
    return P300Speller(model, char_matrix, 10)  # 10 repetitions per row/col
end

function spell_character(speller::P300Speller)
    println("Focus on the target character...")
    
    # Storage for row/column scores
    row_scores = zeros(6)
    col_scores = zeros(6)
    
    # Present intensifications
    for rep in 1:speller.intensification_count
        # Flash each row
        for row in 1:6
            flash_row(row)
            eeg_response = record_p300_epoch()  # 0.8s post-stimulus
            
            # Detect P300
            result = detect_p300(speller.model, eeg_response)
            
            if result.prediction == 1  # Target detected
                row_scores[row] += result.confidence
            end
        end
        
        # Flash each column
        for col in 1:6
            flash_column(col)
            eeg_response = record_p300_epoch()
            
            # Detect P300
            result = detect_p300(speller.model, eeg_response)
            
            if result.prediction == 1  # Target detected
                col_scores[col] += result.confidence
            end
        end
    end
    
    # Identify target character
    target_row = argmax(row_scores)
    target_col = argmax(col_scores)
    selected_char = speller.character_matrix[target_row, target_col]
    
    # Calculate confidence
    overall_confidence = (row_scores[target_row] + col_scores[target_col]) / 
                        (2 * speller.intensification_count)
    
    println("Selected: $selected_char (Confidence: $(round(overall_confidence, digits=3)))")
    
    return (character=selected_char, confidence=overall_confidence)
end

function detect_p300(model::RxGMMModel, eeg_epoch::Matrix{Float64})
    # Prepare BCIData
    metadata = BCIMetadata(
        sampling_rate = 250.0,
        paradigm = :p300,
        feature_type = :erp,
        n_features = size(eeg_epoch, 1),
        n_classes = 2
    )
    
    data = BCIData(reshape(eeg_epoch, size(eeg_epoch, 1), size(eeg_epoch, 2), 1), metadata)
    
    # Run inference
    results = predict_batch(model, data)
    
    return (prediction=results.predictions[1], confidence=results.confidences[1])
end

# Clinical deployment
speller = create_p300_speller()

println("P300 Speller Ready")
println("Patient can now spell messages...")

# Spell a message
message = Char[]
for i in 1:10  # Spell 10 characters
    result = spell_character(speller)
    
    if result.confidence > 0.75
        push!(message, result.character)
        println("Message so far: $(String(message))")
    else
        println("Low confidence - retry")
    end
end

Communication Application

Application: P300 speller for locked-in syndrome
Model: RxGMM binary P300 detection
Speed: 5-10 characters per minute
Accuracy: 85-95% with 10 repetitions
ITR: 10-15 bits/minute

Assistive Technology

Wheelchair Control System

Challenge: Enable paralyzed users to control motorized wheelchairs using motor imagery. Solution: 4-class motor imagery BCI with safety-critical confidence thresholds.

Python
Julia

from nimbus_bci import NimbusLDA, StreamingSession
from nimbus_bci.data import BCIMetadata
import numpy as np
from dataclasses import dataclass
from typing import Dict

# Safety-critical wheelchair controller
@dataclass
class WheelchairBCI:
    model: NimbusLDA
    safety_threshold: float
    command_mapping: Dict[int, str]

def create_wheelchair_controller() -> WheelchairBCI:
    # Train or load motor imagery model
    clf = NimbusLDA()
    X_calib = np.random.randn(100, 16)
    y_calib = np.random.randint(0, 4, 100)
    clf.fit(X_calib, y_calib)
    
    # Map motor imagery classes to wheelchair commands
    command_map = {
        0: "forward",
        1: "backward",
        2: "left",
        3: "right"
    }
    
    return WheelchairBCI(clf, 0.85, command_map)  # High threshold for safety

def control_wheelchair(controller: WheelchairBCI):
    # Configure for safety-critical operation
    metadata = BCIMetadata(
        sampling_rate=250.0,
        paradigm="motor_imagery",
        feature_type="csp",
        n_features=16,
        n_classes=4,
        chunk_size=500  # 2-second chunks for high confidence
    )
    
    session = StreamingSession(controller.model.model_, metadata)
    
    print("Wheelchair BCI Active")
    print(f"Safety threshold: {controller.safety_threshold}")
    
    # Control loop (simplified for demo)
    for i in range(20):  # In real app: while is_active()
        # Collect longer trial for safety (2 seconds)
        chunk = np.random.randn(16, 500)  # acquire_wheelchair_trial()
        
        result = session.process_chunk(chunk)
        
        # Safety-critical decision making
        if result.confidence >= controller.safety_threshold:
            command = controller.command_mapping[result.prediction]
            
            print(f"✓ Command: {command} (Confidence: {result.confidence:.3f})")
            # execute_wheelchair_command(command)
        else:
            print(f"⚠️  Low confidence ({result.confidence:.3f}) - no action")
            # execute_wheelchair_command("stop")
        
        session.reset()
        
        # Safety override: always allow emergency stop
        # if emergency_button_pressed():
        #     execute_wheelchair_command("emergency_stop")
        #     break

# Deployment
wheelchair_bci = create_wheelchair_controller()
control_wheelchair(wheelchair_bci)

using NimbusSDK

# Safety-critical wheelchair controller
struct WheelchairBCI
    model::RxLDAModel
    safety_threshold::Float64
    command_mapping::Dict{Int, Symbol}
end

function create_wheelchair_controller()
    model = load_model(RxLDAModel, "motor_imagery_4class_v1")
    
    # Map motor imagery classes to wheelchair commands
    command_map = Dict(
        1 => :forward,
        2 => :backward,
        3 => :left,
        4 => :right
    )
    
    return WheelchairBCI(model, 0.85, command_map)  # High threshold for safety
end

function control_wheelchair(controller::WheelchairBCI)
    # Configure for safety-critical operation
    metadata = BCIMetadata(
        sampling_rate = 250.0,
        paradigm = :motor_imagery,
        feature_type = :csp,
        n_features = 16,
        n_classes = 4,
        chunk_size = 500  # 2-second chunks for high confidence
    )
    
    session = init_streaming(controller.model, metadata)
    
    println("Wheelchair BCI Active")
    println("Safety threshold: $(controller.safety_threshold)")
    
    # Control loop
    while is_active()
        # Collect longer trial for safety (2 seconds)
        chunk = acquire_wheelchair_trial()
        
        result = process_chunk(session, chunk)
        
        # Safety-critical decision making
        if result.confidence >= controller.safety_threshold
            command = controller.command_mapping[result.prediction]
            
            println("✓ Command: $command (Confidence: $(round(result.confidence, digits=3)))")
            execute_wheelchair_command(command)
        else
            println("⚠️  Low confidence ($(round(result.confidence, digits=3))) - no action")
            # Stop wheelchair or maintain current command
            execute_wheelchair_command(:stop)
        end
        
        # Safety override: always allow emergency stop
        if emergency_button_pressed()
            execute_wheelchair_command(:emergency_stop)
            break
        end
    end
end

# Deployment
wheelchair_bci = create_wheelchair_controller()
control_wheelchair(wheelchair_bci)

Assistive Technology

Application: Motor imagery wheelchair control
Model: RxLDA 4-class motor imagery
Safety: 85% confidence threshold + emergency stop
Latency: 20-25ms inference + 2s decision window
Reliability: Mission-critical with fail-safe defaults

Research & Neuroscience

Brain-Computer Interface Research Platform

Challenge: Provide researchers with flexible, accurate BCI models for experiments. Solution: NimbusSDK’s RxLDA/RxGMM models with custom training for research protocols.

Python
Julia

from nimbus_bci import NimbusLDA, NimbusGMM
import numpy as np
from typing import List, Dict

# Research experiment manager
def run_bci_experiment(paradigm: str, n_subjects: int) -> List[Dict]:
    results = []
    
    for subject_id in range(1, n_subjects + 1):
        print("\n" + "="*60)
        print(f"Subject {subject_id} / {n_subjects}")
        print("="*60)
        
        # 1. Collect calibration data
        print("Phase 1: Calibration (50 trials)")
        X_calib = np.random.randn(50, 16)  # collect_calibration_data()
        y_calib = np.random.randint(0, 4, 50)
        
        # 2. Train subject-specific model
        print("Phase 2: Model Training")
        if paradigm == "motor_imagery":
            clf = NimbusLDA()
            clf.fit(X_calib, y_calib)
        elif paradigm == "p300":
            clf = NimbusGMM(n_components=2)
            clf.fit(X_calib, y_calib)
        
        # 3. Evaluate on test set
        print("Phase 3: Evaluation (100 trials)")
        X_test = np.random.randn(100, 16)  # collect_test_data()
        y_test = np.random.randint(0, 4, 100)
        
        predictions = clf.predict(X_test)
        probabilities = clf.predict_proba(X_test)
        confidences = np.max(probabilities, axis=1)
        
        # 4. Calculate metrics
        accuracy = np.mean(predictions == y_test)
        n_classes = len(np.unique(y_test))
        itr = calculate_ITR(accuracy, n_classes, 4.0)
        mean_conf = np.mean(confidences)
        
        subject_results = {
            "subject_id": subject_id,
            "paradigm": paradigm,
            "accuracy": accuracy,
            "itr": itr,
            "mean_confidence": mean_conf,
            "calibration_trials": 50,
            "test_trials": 100
        }
        
        results.append(subject_results)
        
        print(f"\nSubject {subject_id} Results:")
        print(f"  Accuracy: {accuracy * 100:.1f}%")
        print(f"  ITR: {itr:.1f} bits/min")
        print(f"  Mean Confidence: {mean_conf:.3f}")
    
    # Aggregate results
    print("\n" + "="*60)
    print(f"Aggregate Results (N={n_subjects})")
    print("="*60)
    
    accuracies = [r["accuracy"] for r in results]
    itrs = [r["itr"] for r in results]
    
    print(f"Accuracy: {np.mean(accuracies) * 100:.1f}% ± {np.std(accuracies) * 100:.1f}%")
    print(f"ITR: {np.mean(itrs):.1f} ± {np.std(itrs):.1f} bits/min")
    
    return results

def calculate_ITR(accuracy: float, n_classes: int, trial_duration: float) -> float:
    """Calculate Information Transfer Rate in bits/min"""
    if accuracy <= 1.0 / n_classes:
        return 0.0
    bits_per_trial = np.log2(n_classes) + accuracy * np.log2(accuracy) + \
                     (1 - accuracy) * np.log2((1 - accuracy) / (n_classes - 1))
    trials_per_min = 60.0 / trial_duration
    return bits_per_trial * trials_per_min

# Run motor imagery study
motor_results = run_bci_experiment("motor_imagery", 10)

# Run P300 study
p300_results = run_bci_experiment("p300", 10)

using NimbusSDK
using Statistics

# Research experiment manager
function run_bci_experiment(paradigm::Symbol, n_subjects::Int)
    results = []
    
    for subject_id in 1:n_subjects
        println("\n" * "="^60)
        println("Subject $subject_id / $n_subjects")
        println("="^60)
        
        # 1. Collect calibration data
        println("Phase 1: Calibration (50 trials)")
        calib_data = collect_calibration_data(paradigm, 50)
        
        # 2. Train subject-specific model
        println("Phase 2: Model Training")
        if paradigm == :motor_imagery
            model = train_model(RxLDAModel, calib_data; iterations=50)
        elseif paradigm == :p300
            model = train_model(RxGMMModel, calib_data; iterations=50)
        end
        
        # 3. Evaluate on test set
        println("Phase 3: Evaluation (100 trials)")
        test_data = collect_test_data(paradigm, 100)
        test_results = predict_batch(model, test_data)
        
        # 4. Calculate metrics
        accuracy = sum(test_results.predictions .== test_data.labels) / 
                  length(test_results.predictions)
        itr = calculate_ITR(accuracy, test_data.metadata.n_classes, 4.0)
        mean_conf = mean(test_results.confidences)
        
        subject_results = Dict(
            "subject_id" => subject_id,
            "paradigm" => paradigm,
            "accuracy" => accuracy,
            "itr" => itr,
            "mean_confidence" => mean_conf,
            "calibration_trials" => 50,
            "test_trials" => 100
        )
        
        push!(results, subject_results)
        
        println("\nSubject $subject_id Results:")
        println("  Accuracy: $(round(accuracy * 100, digits=1))%")
        println("  ITR: $(round(itr, digits=1)) bits/min")
        println("  Mean Confidence: $(round(mean_conf, digits=3))")
    end
    
    # Aggregate results
    println("\n" * "="^60)
    println("Aggregate Results (N=$n_subjects)")
    println("="^60)
    
    accuracies = [r["accuracy"] for r in results]
    itrs = [r["itr"] for r in results]
    
    println("Accuracy: $(round(mean(accuracies) * 100, digits=1))% ± $(round(std(accuracies) * 100, digits=1))%")
    println("ITR: $(round(mean(itrs), digits=1)) ± $(round(std(itrs), digits=1)) bits/min")
    
    return results
end

# Run motor imagery study
motor_results = run_bci_experiment(:motor_imagery, 10)

# Run P300 study
p300_results = run_bci_experiment(:p300, 10)

Research Applications

Use Cases: Cognitive neuroscience, BCI algorithms, clinical trials
Models: Bayesian LDA (RxLDA), Bayesian GMM (RxGMM), Bayesian MPR (RxPolya) with custom training
Flexibility: Subject-specific calibration and adaptation
Metrics: Accuracy, ITR, confidence, confusion matrices
Publication: Transparent probabilistic models for peer review

Market Impact & Adoption

Healthcare BCI

$2.3B by 2027Stroke rehabilitation, locked-in communication, seizure detection

Assistive Technology

$1.8B by 2026Wheelchair control, prosthetic control, environmental control

Research & Clinical

$800M by 2026University research, pharmaceutical trials, diagnostic tools

Gaming & Consumer

$3.7B by 2030Next-generation gaming, meditation apps, productivity tools

Regulatory Considerations

Medical Device Pathway

For medical BCI applications, NimbusSDK provides:

Transparent Models: White-box probabilistic models (RxLDA/RxGMM) for regulatory review
Validation: Standardized testing procedures and performance metrics
Uncertainty Quantification: Explicit confidence measures for safety-critical decisions
Traceability: Complete inference history and decision logs

Data Privacy & Security

Edge Processing: All NimbusSDK inference runs locally - neural data never leaves device
HIPAA Ready: No cloud transmission of patient data
GDPR Compliant: Privacy-by-design architecture
Secure Storage: Encrypted model storage and credential caching

Success Stories

Academic Research

10+ peer-reviewed publications using NimbusSDK for BCI research
5 PhD dissertations featuring RxLDA/RxGMM models
Reproducible results through standardized probabilistic models

Clinical Pilots

Stroke rehabilitation pilot (N=15): 35% improvement vs. traditional therapy
P300 speller trial (N=8): 92% accuracy with 10 repetitions
Motor imagery study (N=20): 78% average accuracy across subjects

Implementation Resources

Basic Examples

Complete working examples for all use cases

Julia SDK Reference

Full API documentation

Model Training

Train & calibrate models

Real-Time Setup

Streaming inference configuration

These industry applications demonstrate the real-world potential of Nimbus BCI’s probabilistic models across healthcare, assistive technology, and research domains. Both Python and Julia SDKs provide production-ready Bayesian classifiers (Bayesian LDA, Bayesian GMM, Bayesian Softmax/MPR) for motor imagery, P300, and SSVEP paradigms.

Getting Started

Core Concepts

Models & Algorithms

Configuration & Setup

Examples

Development

Python SDK

Julia SDK & Cloud API

Industry Use Cases

Industry Use Cases

Healthcare & Medical Devices

Motor Imagery Rehabilitation System

Clinical Results

P300-Based Communication for Locked-In Syndrome

Communication Application

Assistive Technology

Wheelchair Control System

Assistive Technology

Research & Neuroscience

Brain-Computer Interface Research Platform

Research Applications

Market Impact & Adoption

Healthcare BCI

Assistive Technology

Research & Clinical

Gaming & Consumer

Regulatory Considerations

Medical Device Pathway

Data Privacy & Security

Success Stories

Academic Research

Clinical Pilots

Implementation Resources

Basic Examples

Julia SDK Reference

Model Training

Real-Time Setup

Getting Started

Core Concepts

Models & Algorithms

Configuration & Setup

Examples

Development

Python SDK

Julia SDK & Cloud API

​Industry Use Cases

​Healthcare & Medical Devices

​Motor Imagery Rehabilitation System

Clinical Results

​P300-Based Communication for Locked-In Syndrome

Communication Application

​Assistive Technology

​Wheelchair Control System

Assistive Technology

​Research & Neuroscience

​Brain-Computer Interface Research Platform

Research Applications

​Market Impact & Adoption

Healthcare BCI

Assistive Technology

Research & Clinical

Gaming & Consumer

​Regulatory Considerations

​Medical Device Pathway

​Data Privacy & Security

​Success Stories

​Academic Research

​Clinical Pilots

​Implementation Resources

Basic Examples

Julia SDK Reference

Model Training

Real-Time Setup

Industry Use Cases

Healthcare & Medical Devices

Motor Imagery Rehabilitation System

P300-Based Communication for Locked-In Syndrome

Assistive Technology

Wheelchair Control System

Research & Neuroscience

Brain-Computer Interface Research Platform

Market Impact & Adoption

Regulatory Considerations

Medical Device Pathway

Data Privacy & Security

Success Stories

Academic Research

Clinical Pilots

Implementation Resources