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.

[1]  Amlan Chakrabarti,et al.  Architecture for complex network measures of brain connectivity , 2017, 2017 IEEE International Symposium on Circuits and Systems (ISCAS).

[2]  Cornelis J. Stam,et al.  Declining functional connectivity and changing hub locations in Alzheimer’s disease: an EEG study , 2015, BMC Neurology.

[3]  Arjan Hillebrand,et al.  Different functional connectivity and network topology in behavioral variant of frontotemporal dementia and Alzheimer's disease: an EEG study , 2016, Neurobiology of Aging.

[4]  K. Sridharan,et al.  50 Years of CORDIC: Algorithms, Architectures, and Applications , 2009, IEEE Transactions on Circuits and Systems I: Regular Papers.

[5]  Cornelis J. Stam,et al.  Increased cortico-cortical functional connectivity in early-stage Parkinson's disease: An MEG study , 2008, NeuroImage.

[6]  Jack E. Volder.  The CORDIC Trigonometric Computing Technique , 1959, IRE Trans. Electron. Comput..

[7]  C. Stam,et al.  Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources , 2007, Human brain mapping.

[8]  Nikulin Vadim,et al.  Detrended fluctuation analysis: a scale-free view on neuronal oscillations , 2012 .

[9]  Haruhiko Nishimura,et al.  Approaches of Phase Lag Index to EEG Signals in Alzheimer’s Disease from Complex Network Analysis , 2016 .

[10]  Francisco del Pozo,et al.  HERMES: Towards an Integrated Toolbox to Characterize Functional and Effective Brain Connectivity , 2013, Neuroinformatics.

[11]  Karl J. Friston.  Functional and Effective Connectivity: A Review , 2011, Brain Connect..

[12]  Jeffrey M. Hausdorff,et al.  Physionet: Components of a New Research Resource for Complex Physiologic Signals". Circu-lation Vol , 2000 .