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Encoding

Encoding

Information enters the brain through encoding, which is the input of information into the memory system. Once sensory information is received from the environment, the brain labels or codes it. The information is then organized with similar information and connected to existing concepts. Encoding occurs through automatic processing and effortful processing.
Automatic processing involves the encoding of details like time, space, frequency, and the meaning of words, usually done without conscious...

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Related Experiment Video

Updated: May 2, 2026

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Decoding and encoding of visual patterns using magnetoencephalographic data represented in manifolds.

Po-Chih Kuo¹, Yong-Sheng Chen², Li-Fen Chen³

¹Department of Computer Science, National Chiao Tung University, Hsinchu, Taiwan.

|July 30, 2014

Summary

This summary is machine-generated.

This study introduces a bidirectional model to decode and encode visual stimuli using brain activity data. The model effectively reconstructs images from brain signals and predicts brain activity from images, highlighting the role of temporal components in visual processing.

Keywords:

Magnetoencephalography Manifold Visual decoding Visual encoding

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Stimulus-specific Cortical Visual Evoked Potential Morphological Patterns

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Published on: May 12, 2019

Area of Science:

Neuroscience
Cognitive Science
Biomedical Engineering

Background:

Understanding visual information processing in the brain is essential.
Magnetoencephalography (MEG) provides valuable data on brain activity.

Purpose of the Study:

To propose a bidirectional model for decoding and encoding visual stimuli using manifold representation of MEG data.
To investigate the role of temporal components in visual perception.

Main Methods:

Extracted temporal principal components (TPCs) from MEG data using principal component analysis and beamforming.
Mapped TPCs to spatiotemporal components (STCs) on a low-dimensional manifold.
Developed a linear mapping between STCs and wavelet coefficients for image reconstruction and prediction.

Main Results:

Achieved a 0.71 correlation between reconstructed and original images.
Demonstrated high correlation (r=0.89) between predicted and original brain activity maps.
Revealed spatial layout information within the embedded manifold.

Conclusions:

The proposed model successfully decodes and encodes visual stimuli using brain activity.
Temporal components are crucial for visual processing.
Manifold representation effectively captures visual perception information.