ArticleSensors (Basel, Switzerland)2022
Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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15 citing papers in PubMed.
- Evaluating Validation Strategies in Motor Imagery EEG: A Full-Cohort GAF-PLV Analysis and Matched Sensitivity Study.Brain sciences · 2026Article
- Enhancing context-aware SARS disorder management: a proposed multi-agent simulation framework with machine learning and bio-sensor data integration.Frontiers in medical technology · 2026Article
- Tactile-evoked EEG Dataset for Natural Perception Using an Integrated Stimulation-Recording Framework.Scientific data · 2025Article
- Neurophysiological Approaches to Lie Detection: A Systematic Review.Brain sciences · 2025Review
- EEG Signal Prediction for Motor Imagery Classification in Brain-Computer Interfaces.Sensors (Basel, Switzerland) · 2025Article
- AMEEGNet: attention-based multiscale EEGNet for effective motor imagery EEG decoding.Frontiers in neurorobotics · 2025Article
- [Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2024Article
- Adaptive LDA Classifier Enhances Real-Time Control of an EEG Brain-Computer Interface for Decoding Imagined Syllables.Brain sciences · 2024Article
- Developing a tablet-based brain-computer interface and robotic prototype for upper limb rehabilitation.PeerJ. Computer science · 2024Article
- 3D convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery EEG signal.Frontiers in neurorobotics · 2024Article
- Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data.Diagnostics (Basel, Switzerland) · 2023Article
- Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms.Sensors (Basel, Switzerland) · 2023Article
- The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights.Sensors (Basel, Switzerland) · 2023Article
- Study on characteristic of epileptic multi-electroencephalograph base on Hilbert-Huang transform and brain network dynamics.Frontiers in neuroscience · 2023Article
- Emergency Braking Evoked Brain Activities during Distracted Driving.Sensors (Basel, Switzerland) · 2022Article
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7 authors.
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Abstract
An efficient feature extraction method for two classes of electroencephalography (EEG) is demonstrated using Common Spatial Patterns (CSP) with optimal spatial filters. However, the effects of artifacts and non-stationary uncertainty are more pronounced when CSP filtering is used. Furthermore, traditional CSP methods lack frequency domain information and require many input channels. Therefore, to overcome this shortcoming, a feature extraction method based on Online Recursive Independent Component Analysis (ORICA)-CSP is proposed. For EEG-based brain-computer interfaces (BCIs), especially online and real-time BCIs, the most widely used classifiers used to be linear discriminant analysis (LDA) and support vector machines (SVM). Previous evaluations clearly show that SVMs generally outperform other classifiers in terms of performance. In this case, Adaptive Support Vector Machine (A-SVM) is used for classification together with the ORICA-CSP method. The results are promising, and the experiments are performed on EEG data of 4 classes' motor images, namely Dataset 2a of BCI Competition IV.
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