Framework for Functional Clustering and Source-to-Sensor Reconstruction of Temporally Evoked Neural Features

Adarsh Mukesh, Shourya Verma, Varsha Mysore Athreya, Ananth Grama, Michael G. Heinz

Confirmation: I have read and agree with the IEEE BHI 2025 conference submission's policy on behalf of myself and my co-authors.

Keywords: EEG, Dimensionality Reduction, Autoencoder, CNN, Reconstruction, Brain Computer Interface

TL;DR: Framework for Functional Clustering and Source-to-Sensor Reconstruction of Temporally Evoked Neural Features

Abstract:

Noninvasive neural recording methods like electroencephalography (EEG) offer high temporal resolution for capturing neural activity. However, interpreting EEG data is challenging scalp-recorded signals (sensor space) reflect complex, integrated activity from multiple cortical regions (source space), complicating the reconstruction of underlying neural dynamics. Traditional approaches like minimum norm estimation require extensive subject-specific data, including MRI scans, precise electrode placement, and detailed anatomical atlases. To address these limitations, we propose a two-part framework: (1) an unsupervised biLSTM autoencoder that reveals clustering patterns in EEG electrode activations and their temporal dynamics during auditory stimulus processing; and (2) a deep learning architecture to predict temporally evoked neural features in sensor space EEG from source representations using a dual-path network with independent stimulus processing and dilated convolutional layers.

The clustering identifies evolving spatiotemporal co-activation patterns between stimulus onset and gaps, revealing functional reorganization. The reconstruction network reduces input dimensionality and integrates features via convolutional blocks with residual connections, trained using a hybrid loss that combines feature-based and spectral terms. Our results demonstrate accurate reconstruction of stimulus-related neural correlates and reveal topographical patterns consistent with the clustering findings. The model generalizes well across subjects. By analyzing both functional organization in sensor signals and source-to-sensor mappings, our framework enhances understanding of EEG transformations. This has significant implications for brain-computer interfaces, neuroimaging, and EEG processing where accurate reconstruction and interpretation are essential.

Track: 4. Clinical Informatics

Registration Id: SYNBTJTDPY3

Submission Number: 58