NEST Project Roadmap

Phase 1: Literature Review & Foundation

Comprehensive survey of Sequence Transducers (RNN-T, Neural Transducers) in BCI applications
Analysis of existing EEG-to-text decoding approaches and their limitations
Review of attention mechanisms for neural signal processing
Study of Silent Speech Interface (SSI) methodologies
Documentation of state-of-the-art benchmarks and evaluation metrics

Status: To be re-implemented with automated pipeline
Documentation Target: docs/literature-review/

ZuCo dataset acquisition and exploratory data analysis
Implementation of band-pass filtering (0.5–50 Hz) for artifact removal
Development of Independent Component Analysis (ICA) pipeline for eye-blink/muscle artifact rejection
Electrode selection and channel optimization strategies
Data augmentation techniques for limited EEG samples
Train/validation/test split with subject-independent evaluation protocol

Status: ✅ Complete
Documentation: docs/phase2-preprocessing.md

Implementation of CNN-based spatial feature extractor (SpatialCNN, EEGNet, DeepConvNet)
Development of Temporal Encoder (LSTM/GRU/Transformer/Conformer variants)
Design of cross-attention mechanism between EEG embeddings and text tokens
Integration of Connectionist Temporal Classification (CTC) loss
Implementation of RNN-Transducer and Transformer-Transducer architectures
Training utilities and metrics (WER, CER, BLEU, Perplexity)
Model factory and configuration system
Checkpoint management utilities

Status: ✅ Complete
Documentation: docs/phase3-models.md

Implementation of advanced attention mechanisms (relative position, local, linear)
Development of robust subword vocabularies using BPE/SentencePiece
Subject-independent generalization and cross-subject transfer learning (DANN, CORAL)
Handling of noisy EEG signals and artifact robustness (adversarial training, denoising)
Integration of pre-trained language models for improved decoding (fusion, rescoring)
Subject adaptation techniques (embeddings, adaptive batch norm)
Fine-tuning strategies for new subjects

Status: ✅ Complete
Documentation: docs/phase4-advanced-features.md

Benchmark evaluation using Word Error Rate (WER), CER, and BLEU scores
Beam search decoder with length normalization and coverage penalty
Latency and real-time inference optimization (ONNX, TorchScript, FP16)
Model compression through pruning (magnitude, structured, iterative, sensitivity)
Model quantization (PTQ, QAT, dynamic, mixed-precision)
Real-time streaming inference pipeline with <100ms latency
Comprehensive profiling tools (FLOPs, memory, throughput, layer timing)
Deployment utilities (model export, packaging, configuration)
User study design for practical SSI applications

Status: ✅ Complete (except user study)
Documentation: docs/phase5-evaluation-optimization.md

Preparation of reproducible codebase with documentation
Research paper outline prepared for BCI/AI conferences (NeurIPS, EMNLP, IEEE EMBC)
Open-source release preparation with examples and model cards
Submission of research paper to relevant BCI/AI conferences
User study design for practical SSI applications

Status: ✅ Complete (paper submission in progress)
Documentation: PHASE6_SUMMARY.md

The NEST framework is now production-ready with:

Next Steps: