Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Parallel Processing

Parallel Processing

The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Psychometric Properties of the Japanese Translation of the Detail and Flexibility Questionnaire (DFlex).

Behavioral sciences (Basel, Switzerland)·2026

Same author

Functional brain network organization during the 40-Hz auditory steady-state response in children with and without autism spectrum disorder.

Frontiers in psychiatry·2026

Same author

Correction: Age-group differences between young and middle-aged adults in spatiotemporal EEG dynamics revealed by instantaneous frequency microstate analysis.

Frontiers in aging neuroscience·2026

Same author

Development and psychometric validation of the Kanazawa University loneliness scale for children and adolescents.

Discover mental health·2026

Same author

Age-group differences between young and middle-aged adults in spatiotemporal EEG dynamics revealed by instantaneous frequency microstate analysis.

Frontiers in aging neuroscience·2026

Same author

Steady-state visual evoked field measurement using wearable OPM-MEG compared with EEG.

Neuroscience research·2026

Search research articles

Related Experiment Video

Updated: Dec 10, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Neural Decoding of Multi-Modal Imagery Behavior Focusing on Temporal Complexity.

Naoki Furutani¹, Yuta Nariya², Tetsuya Takahashi³

¹Department of Psychiatry and Neurobiology, Graduate School of Medical Science, Kanazawa University, Kanazawa, Japan.

Frontiers in Psychiatry

|August 28, 2020

Summary

This study introduces complexity analysis for neural decoding of mental imagery, showing high accuracy in decoding visual and motor tasks. Complexity measures reveal modality-independent brain mechanisms for imagery.

Keywords:

convolutional neural network (CNN)expanded multiscale entropy (expMSE)magnetoencephalography (MEG)mental imagery modality specific-regions multivariate pattern analysis (MVPA)neural decoding supramodal regions

More Related Videos

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Related Experiment Videos

Last Updated: Dec 10, 2025

Cross-Modal Multivariate Pattern Analysis

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Decoding Natural Behavior from Neuroethological Embedding

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

Area of Science:

Neuroscience
Cognitive Science
Biophysics

Background:

Mental imagery involves visual, auditory, and motor modalities, with alterations linked to psychiatric disorders.
Previous research correlates imagery with modality-specific and supramodal brain region activity.
Complexity analysis, though available, remains underutilized in neural decoding for imagery.

Purpose of the Study:

To characterize neural oscillations in multimodal imagery using complexity-based neural decoding.
To investigate the time evolution of temporal complexity for neural decoding applications.
To explore modality-independent mechanisms underlying mental imagery.

Main Methods:

Modified complexity measures to analyze temporal complexity evolution.
Utilized magnetoencephalography (MEG) data from healthy subjects performing imagery and non-imagery tasks.
Applied Hilbert-Huang transform for data decomposition and calculated complexity values for decoding.

Main Results:

Intra-subject decoding using complexity achieved high accuracy, generating characteristic sensitivity maps for visual perception (VP), visual imagery (VI), motor execution (ME), and motor imagery (MI).
Sensitivity maps showed inverted patterns in occipital regions (VP vs. VI) and central regions (ME vs. MI).
Two-class (imagery vs. non-imagery) and four-class classifications performed better with complexity than raw data or band power.

Conclusions:

Complexity measures are effective for neural decoding of mental imagery.
Findings suggest the existence of modality-independent mechanisms for imagery.
Time evolution of temporal complexity analysis can enhance understanding of hierarchical brain functions.