Introduction

Brain-Computer Interfaces (BCIs) enable direct communication between the brain and external devices by translating neural activity into control signals. This vignette demonstrates how to analyze and classify BCI data using PhysioEEG, focusing on motor imagery and steady-state visual evoked potential (SSVEP) paradigms.

Motor Imagery Paradigm

Motor imagery BCI relies on event-related desynchronization (ERD) and event-related synchronization (ERS) in the mu (8-12 Hz) and beta (13-30 Hz) frequency bands. When users imagine left or right hand movements, characteristic power decreases occur over contralateral sensorimotor cortex.

SSVEP Paradigm

SSVEP BCIs use flickering visual stimuli at specific frequencies to elicit steady-state responses in visual cortex. Different flicker frequencies enable multi-class control by detecting which frequency dominates the EEG spectrum.

Classification Pipeline

A typical BCI classification workflow involves: 1. Preprocessing (filtering, artifact removal) 2. Feature extraction (CSP, bandpower, Riemannian geometry) 3. Classification (LDA, SVM with cross-validation) 4. Performance evaluation (accuracy, kappa, information transfer rate)

Setup

library(PhysioEEG)
library(ggplot2)
library(dplyr)

Create simulated BCI data with motor imagery trials (left vs. right hand):

# Generate 80 trials: 40 left hand, 40 right hand
# 16 channels, 250 Hz sampling rate, 4-second trials
eeg <- make_eeg_bci(
  n_trials = 80,
  n_channels = 16,
  sr = 250,
  trial_sec = 4
)

# Examine data structure
eeg
dim(assay(eeg, "raw"))  # 1000 timepoints x 16 channels x 80 trials

# Check trial labels
table(eeg$condition)  # Should show balanced left/right classes

Motor Imagery Analysis

Motor imagery produces ERD during movement imagination and ERS after task completion. The eegMotorImagery() function computes power changes in motor-related frequency bands.

# Compute ERD/ERS in mu and beta bands
# Focus on sensorimotor channels (C3, Cz, C4)
eeg <- eegMotorImagery(
  eeg,
  bands = list(
    mu = c(8, 12),
    beta = c(13, 30)
  ),
  laterality_channels = c("C3", "C4"),
  assay_name = "raw"
)

# ERD/ERS results stored in metadata
erd_results <- metadata(eeg)$motor_imagery

# Examine mu-band power changes
mu_power <- erd_results$mu
head(mu_power)

# Compute laterality index: (C4 - C3) / (C4 + C3)
# Positive values indicate right hemisphere dominance (left hand imagery)
# Negative values indicate left hemisphere dominance (right hand imagery)
laterality <- erd_results$laterality_index

Visualize ERD/ERS patterns:

# Plot time-course of ERD for left vs. right conditions
library(ggplot2)

# Average mu-band power for each condition
mu_left <- mu_power[eeg$condition == "left", ]
mu_right <- mu_power[eeg$condition == "right", ]

# Create data frame for plotting
time_points <- seq(0, 4, length.out = ncol(mu_left))
df_plot <- data.frame(
  time = rep(time_points, 2),
  power = c(colMeans(mu_left), colMeans(mu_right)),
  condition = rep(c("Left", "Right"), each = length(time_points))
)

ggplot(df_plot, aes(x = time, y = power, color = condition)) +
  geom_line(linewidth = 1) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  labs(
    title = "Motor Imagery ERD/ERS (Mu Band)",
    x = "Time (s)",
    y = "Power Change (%)",
    color = "Condition"
  ) +
  theme_minimal()

Common Spatial Patterns (CSP)

CSP is a powerful technique for extracting spatial filters that maximize variance differences between two classes. It finds projections where one class has high variance and the other has low variance.

# Apply bandpass filter to isolate mu+beta band
eeg <- eegFilter(eeg, lowcut = 8, highcut = 30, assay_name = "raw")

# Extract CSP features
# Typically use 3-6 components (first and last pairs)
eeg <- eegCSP(
  eeg,
  labels = eeg$condition,
  n_components = 6,
  assay_name = "filtered"
)

# CSP spatial filters stored in metadata
csp_filters <- metadata(eeg)$csp$filters
dim(csp_filters)  # 16 channels x 6 components

# Projected data (features for classification)
csp_features <- metadata(eeg)$csp$features
dim(csp_features)  # 80 trials x 6 features

Understanding CSP components:

# First components maximize variance for class 1 (left hand)
# Last components maximize variance for class 2 (right hand)

# Visualize first CSP filter (most discriminative for left)
filter_1 <- csp_filters[, 1]
names(filter_1) <- colnames(eeg)

# Create topographic visualization data
topo_data <- data.frame(
  channel = names(filter_1),
  weight = filter_1
)

print(topo_data)

# Examine CSP feature distributions
boxplot(
  csp_features[, 1] ~ eeg$condition,
  main = "CSP Component 1 (Left-specific)",
  xlab = "Condition",
  ylab = "Log Variance"
)

boxplot(
  csp_features[, 6] ~ eeg$condition,
  main = "CSP Component 6 (Right-specific)",
  xlab = "Condition",
  ylab = "Log Variance"
)

Feature Extraction

The eegBCIfeatures() function provides multiple feature extraction methods that can be combined for robust classification.

# Extract features using multiple methods
features_all <- eegBCIfeatures(
  eeg,
  methods = c("bandpower", "csp", "riemannian"),
  labels = eeg$condition,
  assay_name = "filtered"
)

# Features is a list with each method's output
names(features_all)

# Bandpower features: power in specified frequency bands per channel
bp_features <- features_all$bandpower
dim(bp_features)  # 80 trials x (16 channels * n_bands)

# CSP features: log-variance of CSP-projected signals
csp_features <- features_all$csp
dim(csp_features)  # 80 trials x 6 components

# Riemannian features: tangent space projection of covariance matrices
riem_features <- features_all$riemannian
dim(riem_features)  # 80 trials x n_tangent_features

Bandpower-specific feature extraction:

# Extract power in multiple frequency bands
features_bp <- eegBCIfeatures(
  eeg,
  methods = "bandpower",
  labels = eeg$condition,
  assay_name = "raw"
)

# Typically computed for:
# - Delta (1-4 Hz), Theta (4-8 Hz), Alpha (8-13 Hz)
# - Beta (13-30 Hz), Gamma (30-50 Hz)

# Normalize features (z-score per feature dimension)
features_norm <- scale(features_bp$bandpower)

# Examine feature importance via correlation with labels
label_numeric <- ifelse(eeg$condition == "left", 0, 1)
correlations <- cor(features_norm, label_numeric)
head(sort(abs(correlations), decreasing = TRUE))

Riemannian geometry features:

# Riemannian approach treats covariance matrices as points
# on a manifold and projects to tangent space

features_riem <- eegBCIfeatures(
  eeg,
  methods = "riemannian",
  labels = eeg$condition,
  assay_name = "filtered"
)

# These features often outperform traditional methods
# for BCI classification due to better handling of
# non-Euclidean structure of covariance matrices

riem_data <- features_riem$riemannian
dim(riem_data)

SSVEP Detection

For frequency-tagged visual stimuli, SSVEP analysis identifies the target frequency in the EEG spectrum.

# Detect SSVEP at 12 Hz (common flicker frequency)
# Include 2nd and 3rd harmonics (24 Hz, 36 Hz)
ssvep_results <- eegSSVEP(
  eeg,
  target_freq = 12,
  harmonics = 3,
  assay_name = "raw"
)

# Results contain SNR at target frequency and harmonics
snr_fundamental <- ssvep_results$snr[, 1]  # 12 Hz
snr_2nd_harmonic <- ssvep_results$snr[, 2]  # 24 Hz
snr_3rd_harmonic <- ssvep_results$snr[, 3]  # 36 Hz

# High SNR indicates strong SSVEP response
mean(snr_fundamental)
sd(snr_fundamental)

# Binary classification: SSVEP present vs. absent
ssvep_detected <- snr_fundamental > 3  # Threshold at SNR = 3
table(ssvep_detected)

Multi-frequency SSVEP (for multi-class BCI):

# In real multi-class SSVEP BCI, test multiple target frequencies
target_freqs <- c(7, 9, 11, 13)  # 4-class BCI

ssvep_all <- lapply(target_freqs, function(freq) {
  eegSSVEP(eeg, target_freq = freq, harmonics = 2, assay_name = "raw")
})

# For each trial, select frequency with highest SNR
snr_matrix <- sapply(ssvep_all, function(x) x$snr[, 1])
predicted_class <- apply(snr_matrix, 1, which.max)

# predicted_class now contains 1-4 indicating detected frequency
table(predicted_class)

Classification

The eegBCIclassify() function performs classification with cross-validation to estimate generalization performance.

# Classify using CSP features and LDA
# 5-fold cross-validation for robust performance estimation
results_lda <- eegBCIclassify(
  eeg,
  features = csp_features,
  labels = eeg$condition,
  method = "lda",
  cv_folds = 5
)

# Extract performance metrics
accuracy_lda <- results_lda$accuracy
kappa_lda <- results_lda$kappa
confusion_lda <- results_lda$confusion_matrix

cat(sprintf("LDA Accuracy: %.2f%%\n", accuracy_lda * 100))
cat(sprintf("Cohen's Kappa: %.3f\n", kappa_lda))
print(confusion_lda)

Compare multiple classifiers:

# LDA: Linear Discriminant Analysis (assumes Gaussian distributions)
results_lda <- eegBCIclassify(
  eeg,
  features = csp_features,
  labels = eeg$condition,
  method = "lda",
  cv_folds = 10
)

# SVM: Support Vector Machine (non-linear decision boundaries)
results_svm <- eegBCIclassify(
  eeg,
  features = csp_features,
  labels = eeg$condition,
  method = "svm",
  cv_folds = 10
)

# Compare performance
comparison <- data.frame(
  Method = c("LDA", "SVM"),
  Accuracy = c(results_lda$accuracy, results_svm$accuracy),
  Kappa = c(results_lda$kappa, results_svm$kappa)
)

print(comparison)

Feature comparison for classification:

# Classify using different feature sets
# 1. CSP features
acc_csp <- eegBCIclassify(
  eeg,
  features = features_all$csp,
  labels = eeg$condition,
  method = "lda",
  cv_folds = 5
)$accuracy

# 2. Bandpower features
acc_bp <- eegBCIclassify(
  eeg,
  features = features_all$bandpower,
  labels = eeg$condition,
  method = "lda",
  cv_folds = 5
)$accuracy

# 3. Riemannian features
acc_riem <- eegBCIclassify(
  eeg,
  features = features_all$riemannian,
  labels = eeg$condition,
  method = "lda",
  cv_folds = 5
)$accuracy

# Compare feature sets
feature_comparison <- data.frame(
  Features = c("CSP", "Bandpower", "Riemannian"),
  Accuracy = c(acc_csp, acc_bp, acc_riem)
)

print(feature_comparison)

Performance Evaluation

Classification Accuracy

Accuracy is the proportion of correctly classified trials. For balanced two-class problems, chance level is 50%.

# Overall accuracy
results <- eegBCIclassify(
  eeg,
  features = csp_features,
  labels = eeg$condition,
  method = "lda",
  cv_folds = 5
)

overall_acc <- results$accuracy

# Per-class accuracy (sensitivity/recall)
conf_mat <- results$confusion_matrix
class_acc_left <- conf_mat[1, 1] / sum(conf_mat[1, ])
class_acc_right <- conf_mat[2, 2] / sum(conf_mat[2, ])

cat(sprintf("Overall Accuracy: %.2f%%\n", overall_acc * 100))
cat(sprintf("Left Hand Accuracy: %.2f%%\n", class_acc_left * 100))
cat(sprintf("Right Hand Accuracy: %.2f%%\n", class_acc_right * 100))

Cohen’s Kappa

Kappa accounts for chance agreement and is more informative than raw accuracy for imbalanced datasets.

kappa <- results$kappa

# Interpretation:
# < 0.00: Poor agreement (worse than chance)
# 0.00-0.20: Slight agreement
# 0.21-0.40: Fair agreement
# 0.41-0.60: Moderate agreement
# 0.61-0.80: Substantial agreement
# 0.81-1.00: Almost perfect agreement

cat(sprintf("Cohen's Kappa: %.3f\n", kappa))

if (kappa < 0.4) {
  cat("Classification performance: Fair\n")
} else if (kappa < 0.6) {
  cat("Classification performance: Moderate\n")
} else if (kappa < 0.8) {
  cat("Classification performance: Substantial\n")
} else {
  cat("Classification performance: Excellent\n")
}

Information Transfer Rate (ITR)

ITR measures the communication speed of a BCI in bits per minute. It depends on accuracy, number of classes, and selection time.

# ITR formula for binary classification:
# ITR = (log2(N) + P*log2(P) + (1-P)*log2((1-P)/(N-1))) * (60/T)
# where N = number of classes, P = accuracy, T = trial duration (sec)

calculate_itr <- function(accuracy, n_classes, trial_duration) {
  if (accuracy <= 1/n_classes) return(0)  # Below chance

  P <- accuracy
  N <- n_classes

  # Bits per trial
  bits_per_trial <- log2(N) + P * log2(P) +
                    (1 - P) * log2((1 - P) / (N - 1))

  # Bits per minute
  itr <- bits_per_trial * (60 / trial_duration)
  return(itr)
}

# For our 2-class BCI with 4-second trials
itr <- calculate_itr(
  accuracy = overall_acc,
  n_classes = 2,
  trial_duration = 4
)

cat(sprintf("Information Transfer Rate: %.2f bits/min\n", itr))

# Compare different scenarios
scenarios <- expand.grid(
  accuracy = seq(0.6, 0.95, by = 0.05),
  trial_duration = c(3, 4, 5)
)

scenarios$itr <- mapply(
  calculate_itr,
  accuracy = scenarios$accuracy,
  trial_duration = scenarios$trial_duration,
  MoreArgs = list(n_classes = 2)
)

# Find optimal configuration
best <- scenarios[which.max(scenarios$itr), ]
cat(sprintf("\nOptimal configuration:\n"))
cat(sprintf("  Accuracy: %.2f%%\n", best$accuracy * 100))
cat(sprintf("  Trial Duration: %.1f s\n", best$trial_duration))
cat(sprintf("  ITR: %.2f bits/min\n", best$itr))

Cross-Validation Strategies

Proper cross-validation is critical for reliable performance estimation.

# 5-fold CV: Standard for small datasets (n < 100)
cv5 <- eegBCIclassify(
  eeg,
  features = csp_features,
  labels = eeg$condition,
  method = "lda",
  cv_folds = 5
)

# 10-fold CV: More reliable estimate, higher computational cost
cv10 <- eegBCIclassify(
  eeg,
  features = csp_features,
  labels = eeg$condition,
  method = "lda",
  cv_folds = 10
)

# Leave-one-out CV: Maximum variance reduction (n = n_trials)
# Only for small datasets due to computational cost
cv_loo <- eegBCIclassify(
  eeg,
  features = csp_features,
  labels = eeg$condition,
  method = "lda",
  cv_folds = nrow(csp_features)  # 80 folds = leave-one-out
)

cv_comparison <- data.frame(
  Method = c("5-Fold", "10-Fold", "Leave-One-Out"),
  Accuracy = c(cv5$accuracy, cv10$accuracy, cv_loo$accuracy),
  Kappa = c(cv5$kappa, cv10$kappa, cv_loo$kappa)
)

print(cv_comparison)

Summary

This vignette demonstrated a complete BCI classification pipeline:

Data preparation: Simulated motor imagery EEG with left/right hand classes
Motor imagery analysis: ERD/ERS computation in mu and beta bands
Spatial filtering: CSP for optimal discrimination between classes
Feature extraction: Bandpower, CSP, and Riemannian geometry methods
SSVEP detection: Frequency-domain analysis for visual BCIs
Classification: LDA and SVM with cross-validation
Performance metrics: Accuracy, kappa, and information transfer rate

For real BCI applications, additional considerations include: - Online vs. offline analysis pipelines - Adaptive classifiers that update with user feedback - Multi-session training and transfer learning - Real-time artifact rejection - User-specific calibration and optimization

The PhysioEEG package provides flexible tools for exploring these advanced topics in BCI research.

BCI Classification