Decoding the Brain: Our Journey into EEG and Machine Learning

Over the past few weeks, I’ve developed a team that has been diving headfirst into the fascinating world of EEG (electroencephalography) and machine learning. We’re participating in the 2025 EEG Foundation Challenge, a global competition that challenges participants to build models capable of predicting human behavior and latent psychological traits from EEG data.

EEG measures brain activity through electrodes placed on the scalp. Each signal is complex, noisy, and unique to each person. Traditionally, models are trained on small datasets or single tasks, which limits their ability to generalize. Our goal is ambitious: to create models that work across tasks and individuals, which could eventually support applications in cognitive science and mental health.

How We’re Approaching the Challenge

1. Starting with Baselines:

We began with EEGNet and EEGNex, deep learning models specifically designed for EEG signals. These models extract temporal and spatial patterns from the raw EEG data and provide a solid foundation for experimentation.

2. Handling the Data:

We are using the EEGDash and Braindecode libraries to load and preprocess data. These libraries handle essential steps like filtering, epoching, and normalization, allowing us to focus on building and testing models rather than reinventing the wheel.

3. Exploring Advanced Models:

Beyond the baseline, we’re experimenting with cutting-edge ideas:

Graph Convolutional Networks (GCNs): Treating EEG channels as nodes in a graph to model relationships between brain regions. The idea is that regions influencing each other can improve prediction accuracy.

Transformer Networks with Attention: Capturing long-term dependencies and interactions across channels to learn more generalized representations of brain activity.

4. Early Results:

Our preliminary runs with EEGNex have been promising. After training for 20 epochs on validation data, the model achieved an RMSE of approximately 3.9, giving us a solid benchmark to improve upon.

Lessons Learned So Far

Working with EEG data is as much about understanding the preprocessing and structure as it is about coding. Our team spent time exploring EEGDash and Braindecode to make sure we understood how signals are filtered, normalized, and batched. This upfront investment has made our experimentation smoother and more controlled.

We’ve also been navigating the challenges of PyTorch familiarity, library documentation, and GPU access, but the learning curve is paying off. Each step gives us more insight into how to design models that actually generalize.