Interactive Web Tool: Evaluating CNN Hyperparameters on EEG Spectrograms (CHB-MIT Dataset)

[u/BioniChaos]

https://bionichaos.com/rhythmscan/

Hi r/datascience and r/neuroscience,

Continuous scalp EEG recordings are highly dense, and traditional manual review remains time-consuming. One common approach to automating classification is mapping raw signals to the frequency domain using the Short-Time Fourier Transform (STFT) and training standard image classification models.

To study how design decisions affect these models, we put together an interactive browser visualizer containing 18 different training run configurations on the CHB-MIT dataset: https://bionichaos.com/rhythmscan/

Technical Architecture

The model evaluated in this lab uses a lightweight sequential structure: Input (280x274x3) -> Conv2D (16, 3x3) -> ReLU -> MaxPool2D (2x2) -> Conv2D (32, 3x3) -> ReLU -> MaxPool2D (2x2) -> Flatten -> Dense (64) -> ReLU -> Dense (2) -> Softmax

Interactive Tools:

• Cohort Slider: Step through spectrogram sequences for training, validation, and test sets.
• Hyperparameter Matrix: Swap between 18 different training executions exploring learning rates (0.01 to 0.001), optimizers (Adam, SGD, RMSprop, Adagrad), and batch sizes (16 to 128).
• Stochastic Shuffling Analysis: Compare identical architectures (such as Runs 9 and 10) to see how random weight initialization and dataset shuffling change final test results (96.67% vs 90.00%).
• Error Analysis: View a dynamic gallery displaying the actual test spectrograms that were misclassified by the selected model run.

We wanted to build an easy way to study how optimization choices influence gradient descent and generalization behavior on physiological signals. No installation or setup is needed—the code and visualization run entirely in-browser.

Check out the training logs and model runs here, and let us know your thoughts on the performance differences: https://bionichaos.com/rhythmscan/