ArticleComputational intelligence and neuroscience2016
Motor Imagery Classification Using Mu and Beta Rhythms of EEG with Strong Uncorrelating Transform Based Complex Common Spatial Patterns.
Article in Computational intelligence and neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Brain Signal for Secure EEG Biometric Authentication: A Comprehensive Survey.Sensors (Basel, Switzerland) · 2026Review
- Enhanced EEG signal classification in brain computer interfaces using hybrid deep learning models.Scientific reports · 2025Article
- A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification.Sensors (Basel, Switzerland) · 2023Article
- Recognize enhanced temporal-spatial-spectral features with a parallel multi-branch CNN and GRU.Medical & biological engineering & computing · 2023Article
- Beyond passive observation: feedback anticipation and observation activate the mirror system in virtual finger movement control via P300-BCI.Frontiers in human neuroscience · 2023Article
- Temporal-frequency-phase feature classification using 3D-convolutional neural networks for motor imagery and movement.Frontiers in neuroscience · 2023Article
- [Research on the feature representation of motor imagery electroencephalogram signal based on individual adaptation].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2022Article
- Effective Connectivity for Decoding Electroencephalographic Motor Imagery Using a Probabilistic Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Mapping causal circuit dynamics in stroke using simultaneous electroencephalography and transcranial magnetic stimulation.BMC neurology · 2021Article
- A Simplified CNN Classification Method for MI-EEG via the Electrode Pairs Signals.Frontiers in human neuroscience · 2020Article
- Parallel Mechanism of Spectral Feature-Enhanced Maps in EEG-Based Cognitive Workload Classification.Sensors (Basel, Switzerland) · 2019Article
- Correlation Assisted Strong Uncorrelating Transform Complex Common Spatial Patterns for Spatially Distant Channel Data.Computational intelligence and neuroscience · 2018Article
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6 authors.
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Abstract
Recent studies have demonstrated the disassociation between the mu and beta rhythms of electroencephalogram (EEG) during motor imagery tasks. The proposed algorithm in this paper uses a fully data-driven multivariate empirical mode decomposition (MEMD) in order to obtain the mu and beta rhythms from the nonlinear EEG signals. Then, the strong uncorrelating transform complex common spatial patterns (SUTCCSP) algorithm is applied to the rhythms so that the complex data, constructed with the mu and beta rhythms, becomes uncorrelated and its pseudocovariance provides supplementary power difference information between the two rhythms. The extracted features using SUTCCSP that maximize the interclass variances are classified using various classification algorithms for the separation of the left- and right-hand motor imagery EEG acquired from the Physionet database. This paper shows that the supplementary information of the power difference between mu and beta rhythms obtained using SUTCCSP provides an important feature for the classification of the left- and right-hand motor imagery tasks. In addition, MEMD is proved to be a preferred preprocessing method for the nonlinear and nonstationary EEG signals compared to the conventional IIR filtering. Finally, the random forest classifier yielded a high performance for the classification of the motor imagery tasks.
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