Academic-grade platform for real-time EEG signal acquisition, motor-intent decoding, and bidirectional motor command synthesis. Built for researchers, engineers, and neurotechnology teams.
Real-time bidirectional sync between EEG acquisition hardware and the Python signal-processing backend. Packets are timestamped, chunked, and dispatched to motor command decoders.
Monitoring API throughput, signal decode latency, and motor output command rate — live, per second.
Open-access EEG and neural signal datasets curated for BCI research. DOI-indexed, versioned, and preprocessed in NumPy-compatible HDF5 and EDF+ formats.
Every component is designed for high-throughput, low-latency neurotechnology research workflows.
Supports OpenBCI, BrainProducts, g.tec, and LSL-compatible hardware. Streaming at up to 2kHz with automatic artifact rejection and impedance monitoring.
Python-based CNN/LSTM inference pipeline. Sub-5ms decode latency on GPU, sub-20ms on CPU. Motor intent classification across 4–12 command classes.
Binary-framed WebSocket protocol for continuous signal streaming. REST fallback for batch acquisition. Full Python SDK and OpenAPI 3.0 specification available.
Spectral analysis (FFT, Welch PSD), ERD/ERS mapping, ICA decomposition, and coherence visualization — accessible via API or web dashboard.
AES-256 encrypted storage for participant data. IRB-compliant audit trails, role-based access control, and anonymization pipelines included.
MNE-Python, SciPy, NumPy, and PyTorch-compatible data formats. Jupyter notebook templates and Docker images with pre-installed dependencies ready to run.
Authenticate to access the live telemetry dashboard, manage API keys, configure signal pipelines, and download restricted research datasets requiring institutional credentials.