Electrophysiological Imaging of Brain Activity and Connectivity – Challenges and Opportunities

By Bin He, Lin F. Yang, Christopher T. Wilke, Han Yuan. IEEE Transactions on Biomedical Engineering, 2011

303 citations recorded by the source corpus.

Unlocking the dynamic inner workings of the brain continues to remain a grand challenge of the 21st century. To this end, functional neuroimaging modalities represent an outstanding approach to better understand the mechanisms of both normal and abnormal brain functions. The ability to image brain function with ever increasing spatial and temporal resolution has made a significant leap over the past several decades. Further delineation of functional networks could lead to improved understanding of brain function in both normal and diseased states. This paper reviews recent advancements and current challenges in dynamic functional neuroimaging techniques, including electrophysiological source imaging, multimodal neuroimaging integrating fMRI with EEG/MEG, and functional connectivity imaging.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/a12c1dc6579f07dfcb5eef3992d6ddfbf7cc2c8e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Real-time Neuroimaging and Cognitive Monitoring Using Wearable Dry EEG (2015, unknown): “roimaging and BCI [16], [64]–[66], to neuroergonomics [67], [68], and extending to diverse general-purpose applications [4].”
EEG Source Imaging Enhances the Decoding of Complex Right Hand Motor Imagery Tasks (2015, background): “The basic principle of EEG inverse transforms are to project the electrical values recorded on the scalp over a discrete geometrical representation of the brain [19], [47].”
Towards the utilization of EEG as a brain imaging tool (2012, background): “Several studies have shown that functional connectivity patterns of cortical activity in large-scale networks can be effectively determined when combining EEG source imaging with frequencydomain multivariate connectivity measures (Astolfi et al., 2005; He et al., 2011).”
Progress in Brain Computer Interface: Challenges and Opportunities (2019, background): “EEG [71] provides relatively poor spatial resolution due to noninvasive scalp recordings compared to fMRI, but finer temporal resolution [32], [72].”
Brain-Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives (2014, unknown): “disorders (for a review, see [72] and [73]).”