Episodic timing: how spontaneous alpha clocks, retrospectively

AbstractWe seldom time life events intently yet recalling the duration of events is lifelike. Is episodic time the outcome of a rational after-thought or of physiological clocks keeping track of time without our conscious awareness of it? To answer this, we recorded human brain activity with magnetoencephalography (MEG) during quiet wakefulness. Unbeknownst to participants, we asked them after the MEG recording to guess its duration. In the absence of overt attention to time, the relative amount of time participants’ alpha brain rhythms (α ~10 Hz) were in bursting mode predicted participants’ retrospective duration estimate. This relation was absent when participants prospectively measured elapsed time during the MEG recording. We conclude that α bursts embody discrete states of awareness for episodic timing.One-Sentence SummaryIn the human brain, the relative number of alpha oscillatory bursts at ~10 Hz can tell time when the observer does not attend to it.

Download Full-text

Meaning of goodness-of-fit in dipole approximation of human brain activity

Electroencephalography and Clinical Neurophysiology ◽

10.1016/s0013-4694(97)88932-1 ◽

1997 ◽

Vol 103 (1) ◽

pp. 196

Author(s):

J Hara

Keyword(s):

Human Brain ◽

Goodness Of Fit ◽

Brain Activity ◽

Dipole Approximation

Download Full-text

Siamese Reconstruction Network: Accurate Image Reconstruction from Human Brain Activity by Learning to Compare

Applied Sciences ◽

10.3390/app9224749 ◽

2019 ◽

Vol 9 (22) ◽

pp. 4749

Author(s):

Lingyun Jiang ◽

Kai Qiao ◽

Linyuan Wang ◽

Chi Zhang ◽

Jian Chen ◽

...

Keyword(s):

Deep Learning ◽

Human Brain ◽

Brain Activity ◽

Feature Space ◽

Training Data ◽

Reconstruction Method ◽

Learning Method ◽

Training Samples ◽

Visual Reconstruction ◽

Relationship Of

Decoding human brain activities, especially reconstructing human visual stimuli via functional magnetic resonance imaging (fMRI), has gained increasing attention in recent years. However, the high dimensionality and small quantity of fMRI data impose restrictions on satisfactory reconstruction, especially for the reconstruction method with deep learning requiring huge amounts of labelled samples. When compared with the deep learning method, humans can recognize a new image because our human visual system is naturally capable of extracting features from any object and comparing them. Inspired by this visual mechanism, we introduced the mechanism of comparison into deep learning method to realize better visual reconstruction by making full use of each sample and the relationship of the sample pair by learning to compare. In this way, we proposed a Siamese reconstruction network (SRN) method. By using the SRN, we improved upon the satisfying results on two fMRI recording datasets, providing 72.5% accuracy on the digit dataset and 44.6% accuracy on the character dataset. Essentially, this manner can increase the training data about from n samples to 2n sample pairs, which takes full advantage of the limited quantity of training samples. The SRN learns to converge sample pairs of the same class or disperse sample pairs of different class in feature space.

Download Full-text