Implementation of Structural Synchrony and Linear Measures of Brain Network Connectivity for Real-Time State Estimation

Real-time electroencephalogram (EEG)-based state detection and prediction has many applications in Human-Computer Interactions, for example, adaptive training. Network analysis of brain activity derived from EEG data has been correlated to various cognitive states in the neuroscience literature making these network features an attractive measure to use for state detection. However, deriving these network measures in real-time is non-trivial and relatively unexplored in the literature. In this paper, we examine hardware designs and algorithmic performance of an existing non-linear measure of network activity, structural synchrony, as compared to a correlation-based linear measure of network activity, towards real-time human state classification. Our results indicate that the non-linear structural synchrony metric achieves a higher classification accuracy with similar computation speed. We demonstrate that the structure synchrony method can be accelerated in hardware using less resources while achieving similar latency and throughput. Collectively, our results suggest that a real-time implementation of non-linear, network connectivity measures on EEG data is viable and may facilitate on-line human state estimation.