ArticleBMC bioinformatics2017
An improved discriminative filter bank selection approach for motor imagery EEG signal classification using mutual information.
Article in BMC bioinformatics, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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28 citing papers in PubMed.
- SS-RIME: A Scale-Stabilized Approach to EEG Cognitive Workload Classification.Sensors (Basel, Switzerland) · 2026Article
- Identifying optimal channels and features for multi-participant motor imagery experiments across a participant's multi-day multi-class EEG data.Cognitive neurodynamics · 2024Article
- EMPT: a sparsity Transformer for EEG-based motor imagery recognition.Frontiers in neuroscience · 2024Article
- Dual selections based knowledge transfer learning for cross-subject motor imagery EEG classification.Frontiers in neuroscience · 2023Article
- Learning Optimal Time-Frequency-Spatial Features by the CiSSA-CSP Method for Motor Imagery EEG Classification.Sensors (Basel, Switzerland) · 2022Article
- Classification of EEG Using Adaptive SVM Classifier with CSP and Online Recursive Independent Component Analysis.Sensors (Basel, Switzerland) · 2022Article
- Stockwell transform and semi-supervised feature selection from deep features for classification of BCI signals.Scientific reports · 2022Article
- Classification of motor imagery EEG using deep learning increases performance in inefficient BCI users.PloS one · 2022Article
- Deep learning-based self-induced emotion recognition using EEG.Frontiers in neuroscience · 2022Article
- Neural Decoding of EEG Signals with Machine Learning: A Systematic Review.Brain sciences · 2021Review
- A Comprehensive Review on Critical Issues and Possible Solutions of Motor Imagery Based Electroencephalography Brain-Computer Interface.Sensors (Basel, Switzerland) · 2021Review
- Novel channel selection method based on position priori weighted permutation entropy and binary gravity search algorithm.Cognitive neurodynamics · 2021Article
- OPTICAL+: a frequency-based deep learning scheme for recognizing brain wave signals.PeerJ. Computer science · 2021Article
- EEG Source Network for the Diagnosis of Schizophrenia and the Identification of Subtypes Based on Symptom Severity-A Machine Learning Approach.Journal of clinical medicine · 2020Article
- A BCI System Based on Motor Imagery for Assisting People with Motor Deficiencies in the Limbs.Brain sciences · 2020Article
- A new evolutionary preprocessing approach for classification of mental arithmetic based EEG signals.Cognitive neurodynamics · 2020Article
- Recognition of EEG Signal Motor Imagery Intention Based on Deep Multi-View Feature Learning.Sensors (Basel, Switzerland) · 2020Article
- A Dynamic Multi-Scale Network for EEG Signal Classification.Frontiers in neuroscience · 2020Article
- EEG Signal Reconstruction Using a Generative Adversarial Network With Wasserstein Distance and Temporal-Spatial-Frequency Loss.Frontiers in neuroinformatics · 2020Article
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3 authors.
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Abstract
backgroundCommon spatial pattern (CSP) has been an effective technique for feature extraction in electroencephalography (EEG) based brain computer interfaces (BCIs). However, motor imagery EEG signal feature extraction using CSP generally depends on the selection of the frequency bands to a great extent.
methodsIn this study, we propose a mutual information based frequency band selection approach. The idea of the proposed method is to utilize the information from all the available channels for effectively selecting the most discriminative filter banks. CSP features are extracted from multiple overlapping sub-bands. An additional sub-band has been introduced that cover the wide frequency band (7-30 Hz) and two different types of features are extracted using CSP and common spatio-spectral pattern techniques, respectively. Mutual information is then computed from the extracted features of each of these bands and the top filter banks are selected for further processing. Linear discriminant analysis is applied to the features extracted from each of the filter banks. The scores are fused together, and classification is done using support vector machine.
resultsThe proposed method is evaluated using BCI Competition III dataset IVa, BCI Competition IV dataset I and BCI Competition IV dataset IIb, and it outperformed all other competing methods achieving the lowest misclassification rate and the highest kappa coefficient on all three datasets.
conclusionsIntroducing a wide sub-band and using mutual information for selecting the most discriminative sub-bands, the proposed method shows improvement in motor imagery EEG signal classification.
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