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Improved functional connectivity network estimation for brain networks using multivariate partial coherence

Makhtar S, Senik M, Stevenson CW, Mason R, Halliday D

J Neural Eng, 2020 Feb 26.
PMID: 32103827 DOI: 10.1088/1741-2552/ab7a50

OBJECTIVE: Graphical networks and network metrics are widely used to understand and characterise brain networks and brain function. These methods can be applied to a range of electrophysiological data including electroencephalography, local field potential and single unit recordings. Functional networks are often constructed using pair-wise correlation between variables. The objective of this study is to demonstrate that functional networks can be more accurately estimated using partial correlation than with pair-wise correlation.
APPROACH: We compared network metrics derived from unconditional and conditional graphical networks, obtained using coherence and multivariate partial coherence (MVPC), respectively. Graphical networks were constructed using coherence and MVPC estimates, and binary and weighted network metrics derived from these: node degree, path length, clustering coefficients and small-world index.
MAIN RESULTS: Network metrics were applied to simulated and experimental single unit spike train data. Simulated data used a 10x10 grid of simulated cortical neurons with centre-surround connectivity. Conditional network metrics gave a more accurate representation of the known connectivity: Numbers of excitatory connections had range 3-11, unconditional binary node degree had range 6-80, conditional node degree had range 2-13. Experimental data used multi-electrode array recording with 19 single-units from left and right hippocampal brain areas in a rat model for epilepsy. Conditional network analysis showed similar trends to simulated data, with lower binary node degree and longer binary path lengths compared to unconditional networks.
SIGNIFICANCE: We conclude that conditional networks, where common dependencies are removed through partial coherence analysis, give a more accurate representation of the interactions in a graphical network model. These results have important implications for graphical network analyses of brain networks and suggest that functional networks should be derived using partial correlation, based on MVPC estimates, as opposed to the common approach of pair-wise correlation.
Neural network classification of autoregressive features from electroencephalogram signals for brain-computer interface design

Huan NJ, Palaniappan R

J Neural Eng, 2004 Sep;1(3):142-50.
PMID: 15876633

In this paper, we have designed a two-state brain-computer interface (BCI) using neural network (NN) classification of autoregressive (AR) features from electroencephalogram (EEG) signals extracted during mental tasks. The main purpose of the study is to use Keirn and Aunon's data to investigate the performance of different mental task combinations and different AR features for BCI design for individual subjects. In the experimental study, EEG signals from five mental tasks were recorded from four subjects. Different combinations of two mental tasks were studied for each subject. Six different feature extraction methods were used to extract the features from the EEG signals: AR coefficients computed with Burg's algorithm, AR coefficients computed with a least-squares (LS) algorithm and adaptive autoregressive (AAR) coefficients computed with a least-mean-square (LMS) algorithm. All the methods used order six applied to 125 data points and these three methods were repeated with the same data but with segmentation into five segments in increments of 25 data points. The multilayer perceptron NN trained by the back-propagation algorithm (MLP-BP) and linear discriminant analysis (LDA) were used to classify the computed features into different categories that represent the mental tasks. We compared the classification performances among the six different feature extraction methods. The results showed that sixth-order AR coefficients with the LS algorithm without segmentation gave the best performance (93.10%) using MLP-BP and (97.00%) using LDA. The results also showed that the segmentation and AAR methods are not suitable for this set of EEG signals. We conclude that, for different subjects, the best mental task combinations are different and proper selection of mental tasks and feature extraction methods are essential for the BCI design.
HMM based automated wheelchair navigation using EOG traces in EEG

Aziz F, Arof H, Mokhtar N, Mubin M

J Neural Eng, 2014 Oct;11(5):056018.
PMID: 25188730 DOI: 10.1088/1741-2560/11/5/056018

This paper presents a wheelchair navigation system based on a hidden Markov model (HMM), which we developed to assist those with restricted mobility. The semi-autonomous system is equipped with obstacle/collision avoidance sensors and it takes the electrooculography (EOG) signal traces from the user as commands to maneuver the wheelchair. The EOG traces originate from eyeball and eyelid movements and they are embedded in EEG signals collected from the scalp of the user at three different locations. Features extracted from the EOG traces are used to determine whether the eyes are open or closed, and whether the eyes are gazing to the right, center, or left. These features are utilized as inputs to a few support vector machine (SVM) classifiers, whose outputs are regarded as observations to an HMM. The HMM determines the state of the system and generates commands for navigating the wheelchair accordingly. The use of simple features and the implementation of a sliding window that captures important signatures in the EOG traces result in a fast execution time and high classification rates. The wheelchair is equipped with a proximity sensor and it can move forward and backward in three directions. The asynchronous system achieved an average classification rate of 98% when tested with online data while its average execution time was less than 1 s. It was also tested in a navigation experiment where all of the participants managed to complete the tasks successfully without collisions.
Fulltext Modelling the differential effects of age on transcranial magnetic stimulation induced electric fields

Alawi M, Lee PF, Deng ZD, Goh YK, Croarkin PE

J Neural Eng, 2023 Mar 16;20(2).
PMID: 36240726 DOI: 10.1088/1741-2552/ac9a76

Objective. The therapeutic application of noninvasive brain stimulation modalities such as transcranial magnetic stimulation (TMS) has expanded in terms of indications and patient populations. Often neurodevelopmental and neurodegenerative changes are not considered in research studies and clinical applications. This study sought to examine TMS dosing across time points in the life cycle.Approach. TMS induced electric fields with a figure-of-eight coil was simulated at left dorsolateral prefrontal cortex regions and taken in vertex as a control region. Realistic magnetic resonance imaging-based head models (N= 48) were concurrently examined in a cross-sectional study of three different age groups (children, adults, and elderlies).Main results. Age had a negative correlation with electric field peaks in white matter, grey matter and cerebrospinal fluid (P< 0.001). Notably, the electric field map in children displayed the widest cortical surface spread of TMS induced electric fields.Significance. Age-related anatomical geometry beneath the coil stimulation site had a significant impact on the TMS induced electric fields for different age groups. Safety considerations for TMS applications and protocols in children are warranted based on the present electric field findings.
Towards improving Alzheimer's intervention: a machine learning approach for biomarker detection through combining MEG and MRI pipelines

Ahmad AL, Sánchez-Bornot JM, Sotero RC, Coyle DH, Idris Z, Faye I

J Neural Eng, 2026 Oct 29.
PMID: 42956415 DOI: 10.1088/1741-2552/aebb66

OBJECTIVE: To identify robust biomarkers for early cognitive decline, classification performance was evaluated for healthy controls (HC) versus mild cognitive impairment (MCI) using MEG-derived spectral features at the sensor and source levels, and by testing whether adding MRI anatomical features improves discrimination in the BioFIND cohort.
APPROACH: Data from 324 participants (166 HC and 158 MCI) were analyzed using automated artifact rejection and source reconstruction. Spectral biomarkers, including sensor-space and sourcespace representations, were extracted from the magnetometer and gradiometer signals. Five inverse-solution methods were assessed (including linearly constrained minimum variance [LCMV] and exact low-resolution electromagnetic tomography analysis [eLORETA]) to increase the robustness of source-based biomarker selection. Multiple classifiers, including a support vector machine (SVM) and L1-regularized logistic regression (GLMNET), were compared using nested K-fold cross-validation embedded in Monte Carlo replications to limit overfitting.
MAIN RESULTS: The best performance was obtained by combining MRI features with source-based MEG features using GLMNET, achieving 76.31 ± 1.47% accuracy and 82.44 ± 1.44% AUC. MEG-only models showed strong discrimination and outperformed MRI-only analyses, particularly those based on the LCMV and eLORETA features. Across settings, the SVM and GLMNET were the most consistently effective classifiers.
SIGNIFICANCE: These findings support MEG/EEG spectral biomarkers, particularly source-based representations, as sensitive markers of early AD-related functional changes. Integrating MEG with MRI can further improve HC-versus-MCI classification. The study also demonstrated that rigorous validation (nested cross-validation with Monte Carlo replication) is essential for reliable biomarker assessment and for comparing preprocessing, inverse-solution, and classifier choices in translational neuroimaging pipelines.