Deep Cross-Subject Mapping of Neural Activity

TL;DR

This paper introduces a deep generative model-based approach for cross-subject neural decoding, enabling neural activity from one individual to decode motor intentions in another, improving generalization in brain-computer interfaces.

Contribution

It proposes a deep conditional variational autoencoder framework for mapping neural activity across subjects, addressing variability and non-stationarity in neural signals.

Findings

Achieved up to 8% improvement in cross-subject decoding accuracy.

Demonstrated robustness of the approach across different subjects and conditions.

Advances towards generalized brain-computer interfaces.

Abstract

Objective. In this paper, we consider the problem of cross-subject decoding, where neural activity data collected from the prefrontal cortex of a given subject (destination) is used to decode motor intentions from the neural activity of a different subject (source). Approach. We cast the problem of neural activity mapping in a probabilistic framework where we adopt deep generative modelling. Our proposed algorithm uses deep conditional variational autoencoder to infer the representation of the neural activity of the source subject into an adequate feature space of the destination subject where neural decoding takes place. Results. We verify our approach on an experimental data set in which two macaque monkeys perform memory-guided visual saccades to one of eight target locations. The results show a peak cross-subject decoding improvement of $8\%$ over subject-specific decoding. Conclusion. We demonstrate that a neural decoder trained on neural activity signals of one subject can be used to robustly decode the motor intentions of a different subject with high reliability. This is achieved in spite of the non-stationary nature of neural activity signals and the subject-specific variations of the recording conditions. Significance. The findings reported in this paper are an important step towards the development of cross-subject brain-computer that generalize well across a population.