A brain functional network feature extraction method based on directed transfer function and graph theory for MI-BCI decoding tasks

Background

The development of Brain-Computer Interface (BCI) technology has brought tremendous potential to various fields. In recent years, prominent research has focused on enhancing the accuracy of BCI decoding algorithms by effectively utilizing meaningful features extracted from electroencephalographic (EEG) signals.

Objective

This paper proposes a method for extracting brain functional network features based on directed transfer function (DTF) and graph theory. The method incorporates the extracted brain network features with common spatial pattern (CSP) to enhance the performance of motor imagery (MI) classification task.

Methods

The signals from each electrode of the EEG, utilizing a total of 32 channels, are used as input signals for the network nodes. In this study, 26 healthy participants were recruited to provide EEG data. The brain functional network is constructed in Alpha and Beta bands using the DTF method. The node degree (ND), clustering coefficient (CC), and global efficiency (GE) of the brain functional network are obtained using graph theory. The DTF network features and graph theory are combined with the traditional signal processing method, the CSP algorithm. The redundant network features are filtered out using the Lasso method, and finally, the fused features are classified using a support vector machine (SVM), culminating in a novel approach we have termed CDGL.

Results

For Beta frequency band, with 8 electrodes, the proposed CDGL method achieved an accuracy of 89.13%, a sensitivity of 90.15%, and a specificity of 88.10%, which are 14.10, 16.69, and 11.50% percentage higher than the traditional CSP method (75.03, 73.46, and 76.60%), respectively. Furthermore, the results obtained with 8 channels were superior to those with 4 channels (82.31, 83.35, and 81.74%), and the result for the Beta frequency band were better than those for the Alpha frequency band (87.42, 87.48, and 87.36%). Similar results were also obtained on two public datasets, where the CDGL algorithm's performance was found to be optimal.

Conclusion

The feature fusion of DTF network and graph theory features enhanced CSP algorithm's performance in MI task classification. Increasing the number of channels allows for more EEG signal feature information, enhancing the model's sensitivity and discriminative ability toward specific activities in brain regions. It should be noted that the functional brain network features in the Beta band exhibit superior performance improvement for the algorithm compared to those in the Alpha band.

Brain activity during meditation in first-time meditators

AIM OF THE STUDY

The electrophysiological correlates of meditation states in both short and long-term meditators have been increasingly documented; however, little is known about the brain activity associated with first-time meditation experiences. The goal of this study was to investigate the electrophysiological correlates of a single guided mindfulness meditation session in subjects with no previous meditation experience.

MATERIALS AND METHODS

We analyzed electroencephalogram (EEG) changes in signal power, hemispheric asymmetry, and information flow between EEG channels, in 16 healthy subjects who were new to meditation practice.

RESULTS

Our results show that information flow decreases in the theta (4-8 Hz) and alpha ranges (8-13 Hz) during mindfulness meditation exercise as compared to control: a passive listening condition. These changes are accompanied by a general trend in the decrease of alpha power over the whole scalp. One possible interpretation of these results is that there is an increased level of alertness/vigilance associated with the meditation task rather than reaching the target state.

CONCLUSIONS

Our study expands on the existing body of knowledge concerning neural oscillations during breathing meditation practice by showing that in participants with no previous meditation training, EEG correlates are different from the electrophysiological signatures of mindfulness meditation found in studies of more advanced practitioners.

Statistical Analysis of Graph-Theoretic Indices to Study EEG-TMS Connectivity in Patients With Depression

Aim

The objective of this work was to demonstrate the usefulness of a novel statistical method to study the impact of transcranial magnetic stimulation (TMS) on brain connectivity in patients with depression using different stimulation protocols, i.e., 1 Hz repetitive TMS over the right dorsolateral prefrontal cortex (DLPFC) (protocol G1), 10 Hz repetitive TMS over the left DLPFC (G2), and intermittent theta burst stimulation (iTBS) consisting of three 50 Hz burst bundle repeated at 5 Hz frequency (G3).

Methods

Electroencephalography (EEG) connectivity analysis was performed using Directed Transfer Function (DTF) and a set of 21 indices based on graph theory. The statistical analysis of graph-theoretic indices consisted of a combination of the k-NN rule, the leave-one-out method, and a statistical test using a 2 × 2 contingency table.

Results

Our new statistical approach allowed for selection of the best set of graph-based indices derived from DTF, and for differentiation between conditions (i.e., before and after TMS) and between TMS protocols. The effects of TMS was found to differ based on frequency band.

Conclusion

A set of four brain asymmetry measures were particularly useful to study protocol- and frequency-dependent effects of TMS on brain connectivity.

Significance

The new approach would allow for better evaluation of the therapeutic effects of TMS and choice of the most appropriate stimulation protocol.

Causal Coupling Between Electrophysiological Signals, Cerebral Hemodynamics and Systemic Blood Supply Oscillations in Mayer Wave Frequency Range

The aim of the study was to assess causal coupling between neuronal activity, microvascular hemodynamics and blood supply oscillations in the Mayer wave frequency range. An electroencephalogram, cerebral blood oxygenation changes, an electrocardiogram and blood pressure were recorded during rest and during a movement task. Causal coupling between them was evaluated using directed transfer function, a measure based on the Granger causality principle. The multivariate autoregressive model was fitted to all the signals simultaneously, which made it possible to construct a complete scheme of interactions between the considered signals. The obtained pattern of interactions in the resting state estimated in the 0.05-0.15 Hz band revealed a predominant influence of blood pressure oscillations on all the other variables. Reciprocal connections between blood pressure and heart rate variability time series indicated the presence of feedback loops between these signals. During movement, the pattern of connections did not change dramatically. The number of connections decreased, but the couplings between blood pressure and heart rate variability signal were not significantly changed, and the strong influence of the decreased blood hemoglobin concentration on the oxygenated hemoglobin concentration persisted. For the first time our results provided a comprehensive scheme of interactions between electrical and hemodynamic brain signals, heart rate and blood pressure oscillations. Persistent reciprocal connections between blood pressure and heart rate variability time series suggest possible feedforward and feedback coupling of cardiovascular variables which may lead to the observed oscillations in Mayer wave range.

The EEG Split Alpha Peak: Phenomenological Origins and Methodological Aspects of Detection and Evaluation

Electroencephalographic (EEG) patterns were analyzed in a group of ambulatory patients who ranged in age and sex using spectral analysis as well as Directed Transfer Function, a method used to evaluate functional brain connectivity. We tested the impact of window size and choice of reference electrode on the identification of two or more peaks with close frequencies in the spectral power distribution, so called "split alpha." Together with the connectivity analysis, examination of spatiotemporal maps showing the distribution of amplitudes of EEG patterns allowed for better explanation of the mechanisms underlying the generation of split alpha peaks. It was demonstrated that the split alpha spectrum can be generated by two or more independent and interconnected alpha wave generators located in different regions of the cerebral cortex, but not necessarily in the occipital cortex. We also demonstrated the importance of appropriate reference electrode choice during signal recording. In addition, results obtained using the original data were compared with results obtained using re-referenced data, using average reference electrode and reference electrode standardization techniques.

Volitional control of heartbeat and its dependence on pallium

It is believed that we cannot change our heart rhythm by will because the heartbeat is mainly controlled by the autonomic nervous system (ANS), which cannot be affected directly by subjective will. An experiment was designed to determine whether the heartbeat and ANS could be controlled by volition, and, if it is true, how they were controlled. It was demonstrated that the ANS could be partly controlled by volition. The volition, which tended to slow down the heartbeat, initiated synchronized activity in the medial prefrontal cortex, inhibited the sympathetic system, and then decreased the heartbeat. On the other hand, another kind of volition, which sped up the heartbeat, initiated desynchronized activity at the precentral, central, parietal, and occipital regions, inhibited the parasympathetic system and excited the sympathetic system, and then increased the heartbeat. Moreover, information flow from posterior cortex to anterior cortex was observed during the experiment. The parietal area played an important role in triggering the sensorimotor cortex and integrating the information, and the information flow from the central and precentral cortex to heart was dominant. All that demonstrated that volition can partly control the heartbeat, but the behavior was different from the motor nervous system.

Review of the methods of determination of directed connectivity from multichannel data

The methods applied for estimation of functional connectivity from multichannel data are described with special emphasis on the estimators of directedness such as directed transfer function (DTF) and partial directed coherence. These estimators based on multivariate autoregressive model are free of pitfalls connected with application of bivariate measures. The examples of applications illustrating the performance of the methods are given. Time-varying estimators of directedness: short-time DTF and adaptive methods are presented.

Directed Interactions Between Auditory and Superior Temporal Cortices and their Role in Sensory Integration

Recent studies using functional imaging and electrophysiology demonstrate that processes related to sensory integration are not restricted to higher association cortices but already occur in early sensory cortices, such as primary auditory cortex. While anatomical studies suggest the superior temporal sulcus (STS) as likely source of visual input to auditory cortex, little evidence exists to support this notion at the functional level. Here we tested this hypothesis by simultaneously recording from sites in auditory cortex and STS in alert animals stimulated with dynamic naturalistic audio–visual scenes. Using Granger causality and directed transfer functions we first quantified causal interactions at the level of field potentials, and subsequently determined those frequency bands that show effective interactions, i.e. interactions that are relevant for influencing neuronal firing at the target site. We found that effective interactions from auditory cortex to STS prevail below 20 Hz, while interactions from STS to auditory cortex prevail above 20 Hz. In addition, we found that directed interactions from STS to auditory cortex make a significant contribution to multisensory influences in auditory cortex: Sites in auditory cortex showing multisensory enhancement received stronger feed-back from STS during audio–visual than during auditory stimulation, while sites with multisensory suppression received weaker feed-back. These findings suggest that beta frequencies might be important for inter-areal coupling in the temporal lobe and demonstrate that superior temporal regions indeed provide one major source of visual influences to auditory cortex.