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Updated: May 25, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
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Hand movement decoding by phase-locking low frequency EEG signals.

Jiaen Liu1, Christopher Perdoni, Bin He

  • 1Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, USA. liux0736@umn.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|January 19, 2012
PubMed
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Electroencephalography (EEG) can decode hand velocity for brain-computer interfaces (BCI). Low-pass filtered EEG signals (<2 Hz) correlate with and are phase-locked to hand movement velocity.

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Electroencephalography (EEG) offers a noninvasive, low-risk, and inexpensive method for brain-computer interface (BCI) applications.
  • BCIs are crucial for assisting individuals with motor dysfunctions.

Purpose of the Study:

  • To investigate the decoding of hand velocity using EEG signals.
  • To explore the relationship between EEG signals and limb kinematic information.

Main Methods:

  • Utilized a center-out task paradigm for data collection.
  • Employed a linear regression model to analyze EEG recordings.
  • Focused analysis on low-pass filtered EEG signals (<2 Hz).

Main Results:

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Last Updated: May 25, 2026

Simultaneous Scalp Electroencephalography (EEG), Electromyography (EMG), and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

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

Published on: July 26, 2013

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
06:37

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

Published on: July 14, 2023

  • A significant correlation was found between hand velocity and low-pass filtered EEG signals.
  • The filtered EEG signals demonstrated tuning to movement direction.
  • EEG signals were phase-locked to the amplitude of hand velocity.

Conclusions:

  • EEG signals can effectively encode limb kinematic information, supporting the neuronal population vector theory.
  • This research presents a novel approach for implementing brain-computer interfaces.
  • The findings highlight EEG's potential for advanced BCI applications in motor rehabilitation.