ArticleSensors (Basel, Switzerland)2017
Improving the Accuracy and Training Speed of Motor Imagery Brain-Computer Interfaces Using Wavelet-Based Combined Feature Vectors and Gaussian Mixture Model-Supervectors.
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- LPGGNet: Learning from Local-Partition-Global Graph Representations for Motor Imagery EEG Recognition.Brain sciences · 2025Article
- Cortico-muscular coupling to control a hybrid brain-computer interface for upper limb motor rehabilitation: A pseudo-online study on stroke patients.Frontiers in human neuroscience · 2022Article
- Clinician awareness of brain computer interfaces: a Canadian national survey.Journal of neuroengineering and rehabilitation · 2020Article
- Motor Imagery EEG Classification Based on Decision Tree Framework and Riemannian Geometry.Computational intelligence and neuroscience · 2019Article
- Use of the Stockwell Transform in the Detection of P300 Evoked Potentials with Low-Cost Brain Sensors.Sensors (Basel, Switzerland) · 2018Article
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Authors and funding
3 authors.
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Abstract
In this paper, we propose a set of wavelet-based combined feature vectors and a Gaussian mixture model (GMM)-supervector to enhance training speed and classification accuracy in motor imagery brain-computer interfaces. The proposed method is configured as follows: first, wavelet transforms are applied to extract the feature vectors for identification of motor imagery electroencephalography (EEG) and principal component analyses are used to reduce the dimensionality of the feature vectors and linearly combine them. Subsequently, the GMM universal background model is trained by the expectation-maximization (EM) algorithm to purify the training data and reduce its size. Finally, a purified and reduced GMM-supervector is used to train the support vector machine classifier. The performance of the proposed method was evaluated for three different motor imagery datasets in terms of accuracy, kappa, mutual information, and computation time, and compared with the state-of-the-art algorithms. The results from the study indicate that the proposed method achieves high accuracy with a small amount of training data compared with the state-of-the-art algorithms in motor imagery EEG classification.
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