Cited by 20EEG and Brain-Computer InterfacesMuscle activation and electromyography studiesGaze Tracking and Assistive Technology

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Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform

Karakullukcu, Nedime, Yilmaz, Bülent·2022·International Journal of Neural Systems

DOI 10.1142/s0129065721500593

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Abstract

Patients with motor impairments need caregivers’ help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, [Formula: see text], less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.

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Sorted by citations

Recent advances in intelligent wearable systems: from multiscale biomechanical features towards human motion intent prediction
Chen, S., Peng, C., Yang, B., Lin, J., Zhou, L., Jiang, Z., Liu, Z., Liu, Y., & Tang, L. (2026) · npj Artificial Intelligence · doi:10.1038/s44387-026-00083-5 · Cited by 9

See all 20

Authors: Karakullukcu, N., & Yilmaz, B.
Affiliations: Abdullah Gül University (TR)
ORCID: Karakullukcu, Nedime
Venue: International Journal of Neural Systems
Publisher: World Scientific Pub Co Pte Ltd
Published: 2021-11-22
Volume: 32
Issue: 01
Pages: 2150059-2150059
ISSN: 0129-0657 (ISSN-L)
1793-6462
DOI: 10.1142/s0129065721500593
OpenAlex: W3214941105
Cited by: 20
References: 58
Citations by year: 2026: 5 · 2025: 5 · 2024: 4 · 2023: 6
FWCI: 1.32
Type: journal article
Language: en
Open access: closed
Locations: International Journal of Neural Systems (publishedVersion)
PubMed (publishedVersion)
Funders: Higher Education Council (HEC) of Turkey (100/2000)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
Physical Sciences › Engineering › Biomedical Engineering › Muscle activation and electromyography studies
Physical Sciences › Computer Science › Human-Computer Interaction › Gaze Tracking and Assistive Technology
Keywords: brain-computer interface, motion intention recognition, motor imagery, synchrosqueezing transform, EEG feature extraction, singular value decomposition, support vector machine, motor impairment
MeSH terms: Algorithms, Electroencephalography, Humans, Imagination, Movement, Intention, Brain-Computer Interfaces

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Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform

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