Spatial resolution problem of MEG and EEG may cause underestimated connectivity. Estimation of source activity for specific brain regions may include the contribution of multiple neural populations. In other words, each neural region contains multiple sources with various orientations and temporal profiles.
Visual decoding and encoding are crucial aspects in investigating the representation of visual information in the human brain. This paper proposes a bidirectional model for decoding and encoding of visual stimulus based on manifold representation of the temporal and spatial information extracted from magnetoencephalographic data.
Magnetoencephalography (MEG) non-invasively measures the electromagnetic signals induced by brain activities. It can provide spatiotemporal brain activation imaging with high temporal resolution to facilitate functional brain research in both clinical and basic neuroscience fields.
Independent component analysis (ICA) has been widely used to attenuate interference caused by noise components from the electromagnetic recordings of brain activity. However, the scalp topographies and associated temporal waveforms provided by ICA may be insufficient to distinguish functional components from artifactual ones.
Brain-computer interface (BCI) provides a communication channel for patients with sever neuromuscular disorders to signal their intentions directly with their brain activities, instead of the normal output pathways between the brain and muscles.
To discover brain functionalities, we need to do brain source imaging. Magnetoencephalography (MEG) can record brain signals non-invasively with superior temporal resolution and high Signal-to-Noise Ratio (SNR).