A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces. (arXiv:2304.11106v1 [cs.NE])

Brain-computer interfaces are being explored for a wide variety of
therapeutic applications. Typically, this involves measuring and analyzing
continuous-time electrical brain activity via techniques such as
electrocorticogram (ECoG) or electroencephalography (EEG) to drive external
devices. However, due to the inherent noise and variability in the
measurements, the analysis of these signals is challenging and requires offline
processing with significant computational resources. In this paper, we propose
a simple yet efficient machine learning-based approach for the exemplary
problem of hand gesture …

analysis applications arxiv brain brain activity brain signals classification computational computer continuous devices drive eeg exemplary gesture recognition interfaces machine machine learning network noise offline paper processing recognition resources