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Related Experiment Video

Updated: Apr 17, 2026

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

Published on: July 13, 2019

Learning a common dictionary for subject-transfer decoding with resting calibration.

Hiroshi Morioka¹, Atsunori Kanemura², Jun-ichiro Hirayama³

¹ATR Cognitive Mechanisms Laboratories, Kyoto 619-0288, Japan; Graduate School of Informatics, Kyoto University, Kyoto 606-8501, Japan.

|February 17, 2015

Summary

This summary is machine-generated.

This study introduces a novel method to analyze brain signals across subjects and sessions, improving brain-machine interface (BMI) performance. The approach uses shared spatial bases and resting-state calibration for more robust and adaptable BMI decoding.

Keywords:

Brain–machine interface (BMI)Dictionary learning and sparse coding Electroencephalography (EEG)Multi-subject–session analysis Spatial attention Subject-transfer decoding

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Last Updated: Apr 17, 2026

Transcranial Direct Current Stimulation tDCS of Wernicke's and Broca's Areas in Studies of Language Learning and Word Acquisition

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Published on: July 13, 2019

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

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Transcranial Direct Current Stimulation and Simultaneous Functional Magnetic Resonance Imaging

Transcranial Direct Current Stimulation and Simultaneous Functional Magnetic Resonance Imaging

Published on: April 27, 2014

Area of Science:

Neuroscience
Signal Processing
Biomedical Engineering

Background:

Brain signal variability across subjects and sessions complicates data analysis and degrades brain-machine interface (BMI) performance.
Existing methods struggle with consistent analysis and subject-transfer decoding due to these inherent signal variations.

Purpose of the Study:

To develop a method for extracting shared spatial bases from multi-subject brain signals, compensating for inter-subject and inter-session variability.
To create a subject-transfer decoding approach using resting-state activity for calibration, eliminating the need for task-based calibration.

Main Methods:

Dictionary learning modified to compensate for variations between subjects and sessions was used to extract common spatial bases.
Resting-state electroencephalography (EEG) activity from a target subject was employed for calibration in subject-transfer decoding.

Main Results:

Extracted common brain activities aligned with neuroscience knowledge, demonstrating effective variability compensation.
Subject-transfer decoding performance was significantly improved compared to existing methods.
The methodology enabled information sharing across subjects with low-burden resting calibration.

Conclusions:

The proposed method effectively analyzes multi-subject brain activities on common bases, enhancing BMI adaptability in variable environments.
This approach facilitates practical BMI applications by enabling robust decoding with minimal calibration burden.