ArticlePloS one2017
Classifying three imaginary states of the same upper extremity using time-domain features.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed, 45 citations in OpenAlex.
- Joint MVMD-based optimal feature selection and FW-LS-TWSVM for motor imagery recognition.Scientific reports · 2026Article
- Classification of Different Motor Imagery Tasks with the Same Limb Using Electroencephalographic Signals.Sensors (Basel, Switzerland) · 2025Article
- Motor Imagery Classification Based on EEG Sensing with Visual and Vibrotactile Guidance.Sensors (Basel, Switzerland) · 2023Article
- Predicting motor behavior: an efficient EEG signal processing pipeline to detect brain states with potential therapeutic relevance for VR-based neurorehabilitation.Virtual reality · 2023Article
- Recognition of motor intentions from EEGs of the same upper limb by signal traceability and Riemannian geometry features.Frontiers in neuroscience · 2023Article
- Recognition of single upper limb motor imagery tasks from EEG using multi-branch fusion convolutional neural network.Frontiers in neuroscience · 2023Article
- Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography.Sensors (Basel, Switzerland) · 2021Article
- Discriminating three motor imagery states of the same joint for brain-computer interface.PeerJ · 2021Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Brain-computer interface (BCI) allows collaboration between humans and machines. It translates the electrical activity of the brain to understandable commands to operate a machine or a device. In this study, we propose a method to improve the accuracy of a 3-class BCI using electroencephalographic (EEG) signals. This BCI discriminates rest against imaginary grasps and elbow movements of the same limb. This classification task is challenging because imaginary movements within the same limb have close spatial representations on the motor cortex area. The proposed method extracts time-domain features and classifies them using a support vector machine (SVM) with a radial basis kernel function (RBF). An average accuracy of 74.2% was obtained when using the proposed method on a dataset collected, prior to this study, from 12 healthy individuals. This accuracy was higher than that obtained when other widely used methods, such as common spatial patterns (CSP), filter bank CSP (FBCSP), and band power methods, were used on the same dataset. These results are encouraging and the proposed method could potentially be used in future applications including BCI-driven robotic devices, such as a portable exoskeleton for the arm, to assist individuals with impaired upper extremity functions in performing daily tasks.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.