Neural pathway activation in the subthalamic region depends on stimulation polarity.

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease (PD); however, there is limited understanding of which subthalamic pathways are recruited in response to stimulation. Here, by focusing on the polarity of the stimulus waveform (cathodic vs. anodic), our goal was to elucidate biophysical mechanisms that underlie electrical stimulation in the human brain.

Multimodal pre-screening can predict BCI performance variability: A novel subject-specific experimental scheme.

Background: Brain-computer interface (BCI) systems currently lack the required robustness for long-term daily use due to inter- and intra-subject performance variability. In this study, we propose a novel personalized scheme for a multimodal BCI system, primarily using functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), to identify, predict, and compensate for factors affecting competence-related and interfering factors associated with performance.

Method: 11 (out of 13 recruited) participants, including five participants with motor deficits, completed four sessions on average.

A dynamical graph-based feature extraction approach to enhance mental task classification in brain-computer interfaces.

Graph theoretic approaches in analyzing spatiotemporal dynamics of brain activities are under-studied but could be very promising directions in developing effective brain-computer interfaces (BCIs). Many existing BCI systems use electroencephalogram (EEG) signals to record and decode human neural activities noninvasively. Often, however, the features extracted from the EEG signals ignore the topological information hidden in the EEG temporal dynamics.

Individual-specific characterization of event-related hemodynamic responses during an auditory task: An exploratory study.

Functional near-infrared spectroscopy (fNIRS) has been established as an informative modality for understanding the hemodynamic-metabolic correlates of cortical auditory processing. To date, such knowledge has shown broad clinical applications in the diagnosis, treatment, and intervention procedures in disorders affecting auditory processing; however, exploration of the hemodynamic response to auditory tasks is yet incomplete. This holds particularly true in the context of auditory event-related fNIRS experiments, where preliminary work has shown the presence of valid responses while leaving the need for more comprehensive explorations of the hemodynamic correlates of event-related auditory processing.

A Graph-Based Nonlinear Dynamic Characterization of Motor Imagery Toward an Enhanced Hybrid BCI.

Decoding neural responses from multimodal information sources, including electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS), has the transformative potential to advance hybrid brain-computer interfaces (hBCIs). However, existing modest performance improvement of hBCIs might be attributed to the lack of computational frameworks that exploit complementary synergistic properties in multimodal features. This study proposes a multimodal data fusion framework to represent and decode synergistic multimodal motor imagery (MI) neural responses.