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Related Concept Videos

Color Vision01:24

Color Vision

647
Color perception begins in the retina, the light-sensitive layer at the back of the eye. Two main theories explain how colors are seen: the trichromatic theory and the opponent-process theory. The trichromatic theory, proposed by Thomas Young in 1802 and extended by Hermann von Helmholtz in 1852, suggests that color vision is based on three types of cone receptors in the retina. These cones are sensitive to different but overlapping ranges of wavelengths corresponding to red, blue, and green.
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Vision01:24

Vision

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Vision is the result of light being detected and transduced into neural signals by the retina of the eye. This information is then further analyzed and interpreted by the brain. First, light enters the front of the eye and is focused by the cornea and lens onto the retina—a thin sheet of neural tissue lining the back of the eye. Because of refraction through the convex lens of the eye, images are projected onto the retina upside-down and reversed.
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Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

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The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor cortex....
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Revealing Neural Circuit Topography in Multi-Color
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Reconstructing sources location of visual color cortex by the task-irrelevant visual stimuli through machine learning

Yijia Wu1,2, Yanni Zhang2, Yanjing Mao1

  • 1Academy for Engineering & Technology, Fudan University, Shang Hai, China.

Heliyon
|December 30, 2022
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Summary

This study decodes brain activity to locate visual color sensing sources. Machine learning successfully reconstructed endocranial neuronal locations from electroencephalography data in the primary visual cortex.

Keywords:
ColorComputerDecodingEEGMachine learningReconstructingVisual

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Biophysics

Background:

  • Visual color sensing originates from endocranial neuronal electrical discharges reaching the cerebral cortex.
  • The precise spatial location of these neural sources remains challenging to determine.
  • Understanding this neural mechanism is crucial for advancing brain-computer interfaces and neurological diagnostics.

Purpose of the Study:

  • To emulate visual color signal generation using task-irrelevant stimuli.
  • To experimentally track the consequences of activated neurons on the cerebral cortex.
  • To investigate the possibility of reconstructing endocranial neuronal source locations from electroencephalography (EEG) data.

Main Methods:

  • Utilized electroencephalography (EEG) to document changes in brain color sensing.
  • Applied machine learning decoding to analyze the spatial correlation of visual evoked potential (VEP) power distribution in primary visual cortex (V1) regions.
  • Employed EEG inversion techniques to trace and reconstruct neural source locations.

Main Results:

  • Found a positive correlation between sensing classification accuracy in V1 and the spatial correlation of VEP power distribution.
  • Successfully decoded channel locations to trace brain activity neural source locations.
  • Demonstrated the reconstructive possibility of endocranial neuronal source location using visual color EEG in V1.

Conclusions:

  • Visual color EEG in the primary visual cortex (V1) can successfully reconstruct the location of endocranial neuronal sources.
  • Machine learning decoding of channel location is a viable method for EEG inversion in visual neuroscience.
  • This research provides a novel approach to mapping neural activity related to visual perception.