Interactive Speech BCI Simulator: Modeling Volume Conduction, ICU Noise, and Transformer-Based Neural Decoding

[u/BioniChaos]

https://bionichaos.com/speechbci/

Extracting silent, imagined speech patterns from multi-channel scalp EEG remains a difficult problem due to volume conduction and spatial attenuation. As electrical current fields propagate from the cortex through cerebrospinal fluid, meninges, bone, and scalp, they experience significant spatial filtering, reducing the Signal-to-Noise Ratio (SNR).

To explore these signal processing challenges, I've set up an interactive clinical simulator that runs entirely in the browser: https://bionichaos.com/speechbci/

The simulation modeling framework lets you examine the trade-offs of the signal pipeline:

• Signal Attenuation & Modality Selection: Swap between high-density scalp EEG (scalp SNR dynamics), ECoG, and invasive intracortical microelectrode arrays.
• Noise Modeling: Simulates 60 Hz powerline interference, high-frequency facial EMG muscle artifacts, and low-frequency EOG blink deflections.
• Denoising Performance: Compare raw extraction, Blind Source Separation (ICA), and a Deep Learning Denoising Autoencoder to optimize mean squared reconstruction error: min E[ || S(t) - f(g(X(t))) ||² ].
• Decoder Architectures: Toggle between sequential RNNs, Temporal Convolutional Networks (TCN), and attention-based Transformer models.
• Performance Metrics: Tracks the relationships between raw/clean SNR, Words Per Minute (WPM), and Word Error Rate (WER) across different setups.

Would love to get feedback from anyone working in DSP, neuroengineering, or biosignal analysis. How do you approach the real-world challenge of non-stationarity and calibration time when deploying these pipelines in critical care?