arXiv subjects

Virginia de Sa

Publications and source records attributed to Virginia de Sa.

5 recordsLinked to original sources

arXiv2026

Conserved Kinematic Representations enable Zero-Shot Decoding in Handwriting BCIs

While intracortical Brain-Computer Interfaces (iBCIs) that decode imagined handwriting have achieved high communication rates for Latin scripts, they rely on observing every character in the alphabet during training. This poses a challenge in scaling to logographic languages (e.g., Chinese, Japanese), where the character set exceeds thousands of classes. The limitation highlights a fundamental question in motor neuroscience: does the motor cortex represent handwriting through the composition of shared kinematic primitives, that can be exploited by decoders? We introduce a computational framework for aligning neural activity to imagined kinematics in large datasets, enabling the training of a zero-shot capable machine learning algorithm for decoding unseen characters. Our model achieves 64% hits@3 retrieval on unseen letters, suggesting that neural representations of kinematic strokes are robustly conserved across different character contexts. This study provides a framework for dissecting conserved neural dynamics in large-scale intracortical datasets and offers strong evidence for a compositional basis of complex motor control. It also establishes a new paradigm for open-vocabulary iBCI communication with minimal recalibration burden on the user, crucial to increasing adoption of neuroprosthetics in logographic languages.

q-bio.NC↗

arXiv2026

Handwriting decoding as a challenging motor task for EEG Foundation Models

Recent attempts at creating Foundation Models (FMs) for Electroencephalography (EEG) have achieved state-of-the-art performance on multiple tasks including Motor Imagery (MI). These MI tasks have typically involved coarse classification between imagined limb movements. However, the development of foundation models necessitates diverse datasets, both for pretraining and evaluating the progress of these models. In this work, we propose handwriting decoding as a challenging motor task for FMs. We show that several existing datasets are potentially confounded, and introduce a dataset that more rigorously evaluates models. On this dataset, we find that current FMs, despite showing SOTA performance in multiple MI datasets are outperformed by smaller task-specific models. We also highlight challenges specific to EEG-based handwriting decoding to inform future work. In our 4-letter classification task, we show that (a) Knowledge of movement-onset is crucial to reported decoding performance in prior works, with average performance across subjects dropping from $41.3\%$ to $32.4\%$. (b) Increasing test-time signal quality provides significant performance improvements ($45\%$ to $78\%$ in our best subject) compared to scaling training data with single-trial EEG. (c) While scaling training data steadily improves decoding performance, existing FMs do not outperform specialist models in handwriting decoding. We make our code available at https://anonymous.4open.science/r/EEG-Handwriting-BCI-DFCD/

cs.HC↗

arXiv2025

Transferring Spatial Filters via Tangent Space Alignment in Motor Imagery BCIs

We propose a method to improve subject transfer in motor imagery BCIs by aligning covariance matrices on a Riemannian manifold, followed by computing a new common spatial patterns (CSP) based spatial filter. We explore various ways to integrate information from multiple subjects and show improved performance compared to standard CSP. Across three datasets, our method shows marginal improvements over standard CSP; however, when training data are limited, the improvements become more significant.

cs.CV↗

arXiv2025

Decoding Imagined Handwriting from EEG

Patients with extreme forms of paralysis face challenges in communication, adversely impacting their quality of life. Recent studies have reported higher-than-chance performance in decoding handwritten letters from EEG signals, potentially allowing these subjects to communicate. However, all prior works have attempted to decode handwriting from EEG during actual motion. Furthermore, they assume that precise movement-onset is known. In this work, we focus on settings closer to real-world use where either movement onset is not known or movement does not occur at all, fully utilizing motor imagery. We show that several existing studies are affected by confounds that make them inapplicable to the imagined handwriting setting. We also investigate how sample complexity affects handwriting decoding performance, guiding future data collection efforts. Our work shows that (a) Sample complexity analysis in single-trial EEG reveals a noise ceiling, which can be alleviated by averaging over trials. (b) Knowledge of movement-onset is crucial to reported performance in prior works. (c) Fully imagined handwriting can be decoded from EEG with higher-than-chance performance. Taken together, these results highlight both the unique challenges and avenues to pursue to build a practical EEG-based handwriting BCI.

cs.HC↗

arXiv2022

Spectrally Adaptive Common Spatial Patterns

The method of Common Spatial Patterns (CSP) is widely used for feature extraction of electroencephalography (EEG) data, such as in motor imagery brain-computer interface (BCI) systems. It is a data-driven method estimating a set of spatial filters so that the power of the filtered EEG signal is maximized for one motor imagery class and minimized for the other. This method, however, is prone to overfitting and is known to suffer from poor generalization especially with limited calibration data. Additionally, due to the high heterogeneity in brain data and the non-stationarity of brain activity, CSP is usually trained for each user separately resulting in long calibration sessions or frequent re-calibrations that are tiring for the user. In this work, we propose a novel algorithm called Spectrally Adaptive Common Spatial Patterns (SACSP) that improves CSP by learning a temporal/spectral filter for each spatial filter so that the spatial filters are concentrated on the most relevant temporal frequencies for each user. We show the efficacy of SACSP in providing better generalizability and higher classification accuracy from calibration to online control compared to existing methods. Furthermore, we show that SACSP provides neurophysiologically relevant information about the temporal frequencies of the filtered signals. Our results highlight the differences in the motor imagery signal among BCI users as well as spectral differences in the signals generated for each class, and show the importance of learning robust user-specific features in a data-driven manner.

eess.SP↗