Brain-Computer Interface (BCI) techniques hold a great promise for neuroprosthetic applications. A desirable BCI system should be portable, minimally invasive, and feature high classification accuracy and efficiency. As two commonly used non-invasive brain imaging modalities, Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) BCI system have often been incorporated in the development of hybrid BCI systems, largely due to their complimentary properties. In this study, we aimed to investigate whether the early temporal information extracted from singular EEG and fNIRS channels on each hemisphere can be used to enhance the accuracy and efficiency of a hybrid EEG-fNIRS BCI system. Eleven healthy volunteers were recruited and underwent simultaneous EEG-fNIRS recording during a motor execution task that included left and right hand movements. Singular EEG and fNIRS channels corresponding to the motor cortices of each hemisphere were selected using a general linear model. Early temporal information was extracted from the EEG channel (0–1 s) along with initial hemodynamic dip information from fNIRS (0–2 s) for classification using a support vector machine (SVM). Results demonstrated a lofty classification accuracy using a minimal number of channels and features derived from early temporal information. In conclusion, a hybrid EEG-fNIRS BCI system can achieve higher classification accuracy (91.02 ± 4.08%) and efficiency by integrating their complimentary properties, compared to using EEG (85.64 ± 7.4%) or fNIRS alone (85.55 ± 10.72%). Such a hybrid system can also achieve minimal response lag in application by focusing on rapidly-evolving brain dynamics.

Emerging evidence indicates that cognitive deficits in Alzheimer's disease (AD) are associated with disruptions in brain network. Exploring alterations in the AD brain network is therefore of great importance for understanding and treating the disease. This study employs an integrative functional near-infrared spectroscopy (fNIRS) – electroencephalography (EEG) analysis approach to explore dynamic, regional alterations in the AD-linked brain network. FNIRS and EEG data were simultaneously recorded from 14 participants (8 healthy controls and 6 patients with mild AD) during a digit verbal span task (DVST). FNIRS-based spatial constraints were used as priors for EEG source localization. Graph-based indices were then calculated from the reconstructed EEG sources to assess regional differences between the groups. Results show that patients with mild AD revealed weaker and suppressed cortical connectivity in the high alpha band and in beta band to the orbitofrontal and parietal regions. AD-induced brain networks, compared to the networks of age-matched healthy controls, were mainly characterized by lower degree, clustering coefficient at the frontal pole and medial orbitofrontal across all frequency ranges. Additionally, the AD group also consistently showed higher index values for these graph-based indices at the superior temporal sulcus. These findings not only validate the feasibility of utilizing the proposed integrated EEG-fNIRS analysis to better understand the spatiotemporal dynamics of brain activity, but also contribute to the development of network-based approaches for understanding the mechanisms that underlie the progression of AD.

Alzheimer's disease (AD) causes the progressive deterioration of neural connections, disrupting structural connectivity (SC) networks within the brain. Graph-based analyses of SC networks have shown that topological properties can reveal the course of AD propagation. Different whole-brain parcellation schemes have been developed to define the nodes of these SC networks, although it remains unclear which scheme can best describe the AD-related deterioration of SC networks. In this study, four whole-brain parcellation schemes with different numbers of parcels were used to define SC network nodes. SC networks were constructed based on high angular resolution diffusion imaging (HARDI) tractography for a mixed cohort that includes 20 normal controls (NC), 20 early mild cognitive impairment (EMCI), 20 late mild cognitive impairment (LMCI), and 20 AD patients, from the Alzheimer's Disease Neuroimaging Initiative. Parcellation schemes investigated in this study include the OASIS-TRT-20 (62 regions), AAL (116 regions), HCP-MMP (180 regions), and Gordon-rsfMRI (333 regions), which have all been widely used for the construction of brain structural or functional connectivity networks. Topological characteristics of the SC networks, including the network strength, global efficiency, clustering coefficient, rich-club, characteristic path length, k-core, rich-club coefficient, and modularity, were fully investigated at the network level. Statistical analyses were performed on these metrics using Kruskal-Wallis tests to examine the group differences that were apparent at different stages of AD progression. Results suggest that the HCP-MMP scheme is the most robust and sensitive to AD progression, while the OASIS-TRT-20 scheme is sensitive to group differences in network strength, global efficiency, k-core, and rich-club coefficient at k -levels from 18 and 39. With the exception of the rich-club and modularity coefficients, AAL could not significantly identify group differences on other topological metrics. Further, the Gordon-rsfMRI atlas only significantly differentiates the groups on network strength, characteristic path length, k-core, and rich-club coefficient. Results show that the topological examination of SC networks with different parcellation schemes can provide important complementary AD-related information and thus contribute to a more accurate and earlier diagnosis of AD.

Support vector machines are powerful tools used to solve the small sample and nonlinear classification problems, but their ultimate classification performance depends heavily upon the selection of appropriate kernel and penalty parameters. In this study, we propose using a particle swarm optimization algorithm to optimize the selection of both the kernel and penalty parameters in order to improve the classification performance of support vector machines. The performance of the optimized classifier was evaluated with motor imagery EEG signals in terms of both classification and prediction. Results show that the optimized classifier can significantly improve the classification accuracy of motor imagery EEG signals.

Emotion and affect play crucial roles in human life that can be disrupted by diseases. Functional brain networks need to dynamically reorganize within short time periods in order to efficiently process and respond to affective stimuli. Documenting these large-scale spatiotemporal dynamics on the same timescale they arise, however, presents a large technical challenge. In this study, the dynamic reorganization of the cortical functional brain network during an affective processing and emotion regulation task is documented using an advanced multi-model electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) technique. Sliding time window correlation and [Formula: see text]-means clustering are employed to explore the functional brain connectivity (FC) dynamics during the unaltered perception of neutral (moderate valence, low arousal) and negative (low valence, high arousal) stimuli and cognitive reappraisal of negative stimuli. Betweenness centralities are computed to identify central hubs within each complex network. Results from 20 healthy subjects indicate that the cortical mechanism for cognitive reappraisal follows a ‘top-down’ pattern that occurs across four brain network states that arise at different time instants (0–170[Formula: see text]ms, 170–370[Formula: see text]ms, 380–620[Formula: see text]ms, and 620–1000[Formula: see text]ms). Specifically, the dorsolateral prefrontal cortex (DLPFC) is identified as a central hub to promote the connectivity structures of various affective states and consequent regulatory efforts. This finding advances our current understanding of the cortical response networks of reappraisal-based emotion regulation by documenting the recruitment process of four functional brain sub-networks, each seemingly associated with different cognitive processes, and reveals the dynamic reorganization of functional brain networks during emotion regulation.