A Convolutional Spiking Network for Gesture Recognition in Brain-Computer Interfaces
This work addresses the problem of efficient and accurate gesture classification for brain-computer interface applications, representing an incremental improvement with a novel hybrid method.
The paper tackles the challenge of noisy and variable brain signal analysis for gesture recognition in brain-computer interfaces by proposing a convolutional spiking neural network with bio-inspired plasticity, achieving 92.74-97.07% accuracy across subjects and data types.
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 classification based on brain signals. We use a hybrid machine learning approach that uses a convolutional spiking neural network employing a bio-inspired event-driven synaptic plasticity rule for unsupervised feature learning of the measured analog signals encoded in the spike domain. We demonstrate that this approach generalizes to different subjects with both EEG and ECoG data and achieves superior accuracy in the range of 92.74-97.07% in identifying different hand gesture classes and motor imagery tasks.