A novel method for efficient estimation of brain effective connectivity in EEG. (January 2023) - Khan, Danish M.; Yahya, Norashikin; Kamel, Nidal; Faye, Ibrahima
Highlights: This study develops an efficient and reliable technique for the estimation of brain effective connectivity, termed as, "Efficient Effective Connectivity (EEC)". EEC addresses the fundamental mathematical issues present in the commonly available effective connectivity estimation technique i.e., Partial Directed Coherence (PDC) and Directed Transfer Function (DTF). Consequently, analysis based on these techniques may lead to erroneous results. EEC is validated and compared with PDC and DTF using synthetic and real EEG data in Eye-Open and Eye-Close resting-states. EEC was found to have correctly identified all the significant and non-significant connections. EEC is relatively less susceptible to model order as compared to PDC and DTF. A 3D-CNN based deep learning network is designed, trained, and tested for the classification of resting eye-state effective connectivity estimated from EEC and PDC. The comparison of classification accuracy further strengthens the claim that EEC outperforms PDC. Accurate estimation of effective connectivity (EC) using EEC will enhance understandings of brain functions and improve its reliability in the development of EC-based applications. High classification accuracy of eye-states can be used in various critical and non-critical applications such as brain-computer interface, gaze tracking, drowsiness detection and alarm system during driving, fatigue detection as well as clinical diagnosis and health care systems. Abstract: Background and Objective: Brain connectivity plays a pivotal role in understanding the brain's information processing functions by providing various details including magnitude, direction, and temporal dynamics of inter-neuron connections. While the connectivity may be classified as structural, functional and causal, a complete in-vivo directional analysis is guaranteed by the latter and is referred to as Effective Connectivity (EC). Two most widely used EC techniques are Directed Transfer Function (DTF) and Partial Directed Coherence (PDC) which are based on multivariate autoregressive models. The drawbacks of these techniques include poor frequency resolution and the requirement for experimental approach to determine signal normalization and thresholding techniques in identifying significant connectivities between multivariate sources. Methods: In this study, the drawbacks of DTF and PDC are addressed by proposing a novel technique, termed as Efficient Effective Connectivity (EEC), for the estimation of EC between multivariate sources using AR spectral estimation and Granger causality principle. In EEC, a linear predictive filter with AR coefficients obtained via multivariate EEG is used for signal prediction. This leads to the estimation of full-length signals which are then transformed into frequency domain by using Burg spectral estimation method. Furthermore, the newly proposed normalization method addressed the effect on each source in EEC using the sum of maximum connectivity values over the entire frequency range. Lastly, the proposed dynamic thresholding works by subtracting the first moment of causal effects of all the sources on one source from individual connections present for that source. Results: The proposed method is evaluated using synthetic and real resting-state EEG of 46 healthy controls. A 3D-Convolutional Neural Network is trained and tested using the PDC and EEC samples. The result indicates that compared to PDC, EEC improves the EEG eye-state classification accuracy, sensitivity and specificity by 5.57%, 3.15% and 8.74%, respectively. Conclusion: Correct identification of all connections in synthetic data and improved resting-state classification performance using EEC proved that EEC gives better estimation of directed causality and indicates that it can be used for reliable understanding of brain mechanisms. Conclusively, the proposed technique may open up new research dimensions for clinical diagnosis of mental disorders.