Hardware architecture for real-time EEG-based functional brain connectivity parameter extraction.

Objective. Design a novel architecture for real-time quantitative characterization of functional brain connectivity (FC) networks derived from wearable electroencephalogram (EEG).Approach. We performed an algorithm to architecture mapping for the calculation of phase lag index to form the functional connectivity networks and the extraction of a set of graph-theoretic parameters to quantitatively characterize these networks. This mapping was optimized using approximations in the mathematical definitions of the algorithms which reduce its computational complexity and produce a more hardware amenable implementation.Main results. The architecture was developed for a 19-channel EEG system. The system can calculate all the functional connectivity parameters in a total time of 131 µs, utilizes 71% of the total logic resources in the FPGA, and shows 51.84 mW dynamic power consumption at 22.16 MHz operation frequency when implemented in a Stratix IV EP4SGX230K FPGA. Our analysis also showed that the system occupies an area equivalent to approximately 937 K 2-input NAND gates, with an estimated power consumption of 39.3 mW at 0.9 V supply using a 90 nm CMOS application specific integrated circuit technology.Significance. The proposed architecture can calculate the FC and extract the graph-theoretic parameters in real-time with low power consumption. This characteristic makes the architecture ideal for applications such as a wearable closed-loop neurofeedback systems, where constant monitoring of the brain activity and fast processing of EEG is necessary to control the appropriate feedback.

A phase lag index hardware calculation for real-time electroencephalography studies.

Among the different techniques used for the analysis of electroencephalograms, the phase lag index has become an important method for the calculation of the functional brain connectivity. Currently, this method is implemented offline due to its high computational complexity restricting it from real-time applications that would require an online neurofeedback. In this paper, we propose a new architecture to calculate the phase lag index of electroencephalograms in real-time. As a proof of concept, a 32 bit and 16-channel system running at 188.32 MHz was synthesized on a Stratix IV GX FPGA. The system was tested and the simulations demonstrated that the system could perform the calculation of the Phase lag index at least 66 times faster than the MATLAB software with a mean square error of less than 5.72×10-6.