Artificial Intelligence · Workshop

ThoughtStream: Neural AI Engineering from Signal to Deployment

Decoding the Mind: Master P300 Signal Analytics for Next-Gen Brain-Computer Interfaces

ModeVirtual / Online
LevelModerate
Fee₹2,499

About Workshop

This workshop introduces participants to advanced Neuro-AI, EEG signal processing, Brain-Computer Interface systems, synthetic neural data generation, transformer-based decoding, and real-time edge deployment. Participants will learn how to preprocess EEG data, apply generative AI for augmentation, build neural decoding models, and develop closed-loop BCI applications using tools such as MNE-Python, PyTorch, EEG-GANs, ONNX Runtime, TensorFlow Lite, Streamlit, and WebSockets.

Aim

The aim of this workshop is to equip participants with advanced theoretical and practical knowledge of Neuro-AI systems by enabling them to process EEG signals, apply generative AI for neural data augmentation, design transformer-based decoding models, and deploy real-time closed-loop BCI applications on cloud and edge-compatible platforms.

What Participants Will Learn

Introduce modern Neuro-AI and BCI architectures.
Explain EEG preprocessing, filtering, and artifact removal techniques.
Demonstrate synthetic EEG data generation using EEG-GANs.
Explore EEGNet, TCNs, and transformer-based neural decoding.
Build understanding of motor imagery and imagined speech decoding concepts.
Train participants in real-time closed-loop BCI system development.
Introduce edge deployment using ONNX Runtime and TensorFlow Lite.
Highlight neuro-privacy, BCI UX, and ethical considerations.

Structure

📅 Day 1: Advanced Signal Architectures & Synthetic Data Augmentation

The Modern Neuro-AI Landscape: Moving past legacy P300 matrices to continuous motor imagery and imagined speech decoding using modern EEG arrays such as OpenBCI, Emotiv, Kernel, and Neuralink.
High-Performance Preprocessing & Filtering: Stream handling via MNE-Python. Implementing Common Spatial Patterns, Surface Laplacian derivations, and real-time Independent Component Analysis for artifact suppression.
Generative AI for Neural Augmentation: Architecting Generative Adversarial Networks, EEG-GANs, and conditioning latent diffusion models to synthesize authentic multi-channel training datasets.

💻 Hands-on Lab

Code an automated Google Colab pipeline that ingests raw EEG data, removes artifacts via ICA, and uses an EEG-GAN to generate 5,000 synthetic high-fidelity training trials.

📅 Day 2: Foundational Neuro-Transformers & Neural Decoding

Deep Learning Paradigms for Brain Data: Mapping long-range neural phase synchronization using spatial-temporal matrix embeddings with EEGNet and Temporal Convolutional Networks.
Designing Foundational Neural Transformers: Building an EEG-Transformer with Multi-Head Self-Attention layers in PyTorch. Implementing masked signal pre-training models using large-scale public data registries.
Speech & Intention Decoding: Algorithmic phonetic mapping from continuous sensory-motor cortices and classification of imagined movements. Utilizing cross-subject transfer learning for zero-shot calibration.

💻 Hands-on Lab

Construct an end-to-end Neural Transformer in PyTorch. Load a multi-subject motor imagery dataset, embed spatial-temporal tensors, apply self-attention blocks, and evaluate cross-subject accuracy.

📅 Day 3: Real-Time Closed-Loop Systems & Production Edge Deployment

Low-Latency Closed-Loop Architecture: Setting up Lab Streaming Layer configurations for synchronous live ingestion. Compressing inference windows down to sub-50ms for seamless feedback loops.
Edge Computing & TinyML for Wearables: Model quantization, knowledge distillation, and structural pruning to convert PyTorch models into lightweight ONNX Runtime and TensorFlow Lite architectures for low-power edge chips.
BCI UX Design & Neuro-Privacy: Triggering actions inside VR, robotics, and web interfaces. Designing adaptive systems based on real-time cognitive workload metrics and hardening biophysical logs against privacy vulnerabilities.

💻 Hands-on Lab

Develop and launch a full-stack, real-time web application using Streamlit and WebSockets that simulates a live brainwave feed, decodes intentional thoughts via an edge Transformer, and executes instant UI controls.

Outcomes

Understand modern EEG-based Neuro-AI and BCI workflows.
Preprocess and clean EEG signals using MNE-Python and ICA.
Apply spatial filtering and feature extraction methods.
Generate synthetic EEG training data using generative AI.
Build basic transformer-based neural decoding models in PyTorch.
Evaluate cross-subject motor imagery classification models.
Develop simulated real-time BCI applications using Streamlit and WebSockets.
Optimize models for wearable and edge-based deployment.

Who Should Attend

Researchers in neuroscience and cognitive science
Academicians and faculty in BCI and neural signal analysis
Industry professionals in healthcare, neurotech, and AI-based assistive systems
Data scientists and AI practitioners working with EEG/BCI data
Graduate/postgraduate students in neuroscience, biomedical engineering, or data science

ThoughtStream: Neural AI Engineering from Signal to Deployment

ThoughtStream: Neural AI Engineering from Signal to Deployment

About Workshop

Aim

What Participants Will Learn

Structure

📅 Day 1: Advanced Signal Architectures & Synthetic Data Augmentation

💻 Hands-on Lab

📅 Day 2: Foundational Neuro-Transformers & Neural Decoding

💻 Hands-on Lab

📅 Day 3: Real-Time Closed-Loop Systems & Production Edge Deployment

💻 Hands-on Lab

Important Dates

Registration Ends

Workshop Dates

What You Will Gain

Outcomes

Who Should Attend

DR. H ANANDARAM

Assistant Professor