Brain functional connectivity analysis using single trial EEG for understanding individual mechanisms

In this paper, a method has been developed to track brain functional connectivity (FC) by using cross correlation method. The EEG data was acquired from twenty subjects using oddball paradigm to examine the connectivity across brain lobes for different oddball cases. Pairwise zero lag correlation coefficient of 10–20 electrodes was computed for each segment for 20 samples and the connectivity matrices were formed. For higher strength connectivity, the matrices were thresholded. The connection between the electrodes that have correlation value higher than the specified threshold value at zero lag is plotted. We have done single trial EEG analysis for subjects for different oddball cases (Target with Response-TR, Target with no Response-TNR, No Target no Response-NTNR). Single trial analysis helps to analyse individual mechanisms, behavioral information and a systematic mapping between brain activity and stimulus information space. For target with response, high connectivity was observed for all cases in general for all segments compared with other cases. TNR has less connectivity than TR cases for all segments. High connectivity was observed only in the first segment for NTNR case. For 10–20 electrodes FC can be observed for Fz, F3, F4, C3 or C4 electrodes. C3 or C4 electrodes locations are responsible for motor planning or sensorimotor integrations in the last segment when subject responds to the target or nontarget stimuli. Hence using this method, it is possible to identify the individual differences between different oddball cases within and across subjects using single trial EEG as a function of time.

[1]  Katarzyna J. Blinowska,et al.  Determination of EEG activity propagation: pair-wise versus multichannel estimate , 2004, IEEE Transactions on Biomedical Engineering.

[2]  Humaira Nisar,et al.  Tracking of electroencephalography signals across brain lobes using motion estimation and cross-correlation , 2015, J. Electronic Imaging.

[3]  Humaira Nisar,et al.  Tracking of EEG activity using topographic maps , 2015, 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA).

[4]  Jürgen Kayser,et al.  Event-related potentials in schizophrenia during tonal and phonetic oddball tasks: relations to diagnostic subtype, symptom features and verbal memory , 2001, Biological Psychiatry.

[5]  F. Babiloni,et al.  Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function , 2005, NeuroImage.

[6]  Guizhi Xu,et al.  Functional brain network analysis during auditory oddball task , 2016, 2016 Asia-Pacific International Symposium on Electromagnetic Compatibility (APEMC).

[7]  K Sathian,et al.  Compensating for Zero-lag Correlation Effects in Time-lagged Granger Causality Analysis , 2009, NeuroImage.

[8]  Mahmoud Hassan,et al.  Spatiotemporal Analysis of Brain Functional Connectivity , 2015 .

[9]  F. Wendling,et al.  A new algorithm for spatiotemporal analysis of brain functional connectivity , 2015, Journal of Neuroscience Methods.

[10]  Karl J. Friston,et al.  Evaluation of different measures of functional connectivity using a neural mass model , 2004, NeuroImage.

[11]  Vangelis Sakkalis,et al.  Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG , 2011, Comput. Biol. Medicine.

[12]  P. Rossini,et al.  “Small World” architecture in brain connectivity and hippocampal volume in Alzheimer’s disease: a study via graph theory from EEG data , 2016, Brain Imaging and Behavior.

[13]  Humaira Nisar,et al.  A novel method for tracking and analysis of EEG activation across brain lobes , 2018, Biomed. Signal Process. Control..

[14]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.