Proceedings ArticleDOI
Relevant Frequency Band Selection using Sequential Forward Feature Selection for Motor Imagery Brain Computer Interfaces
Jyoti Singh Kirar,Ramesh Kumar Agrawal +1 more
- pp 52-59
TLDR
A novel approach to select a subset of relevant frequency bands using sequential forward feature selection method from a composite filter bank which consists of Prior-known EEG frequency bands and a set of variable size overlapping frequency bands to improve the performance of motor imagery tasks classification.Abstract:
In order to provide basic communication abilities to people with motor disability, motor imagery brain computer interface is one of most widely used technique. In this paper, we present a novel algorithm (Composite Filter bank based stationary CSP) for determining subject as well as task specific discriminative frequency bands for classification of motor imagery tasks. It is noted in the literature that while performing any motor imagery tasks, two major frequency band of EEG spectrum i.e mu (7-12 Hz) as well as beta (12-30 Hz) bands are actively involved. Hence, in most of the literature work EEG signals were filtered using a frequency band of 7-30 Hz usually before using CSP transformation. However, it is possible that some of the frequencies may not provide useful features to distinguish motor imagery tasks. In this paper, we propose a novel approach to select a subset of relevant frequency bands using sequential forward feature selection method from a composite filter bank which consists of Prior-known EEG frequency bands and a set of variable size overlapping frequency bands to improve the performance of motor imagery tasks classification. Experimental results of the proposed work on publicly available datasets validate the effectiveness of the proposed method. Friedman statistical test conducted further shows that the proposed approach significantly outperforms the existing methods.read more
Citations
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Interactive local and global feature coupling for EEG-based epileptic seizure detection
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Classification of Motor Imagery EEG Signals Using Machine Learning
TL;DR: The objective is to create a machine learning model that can be optimized for real-time classification while having a relatively acceptable classification accuracy.
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Transformer-based Spatial-Temporal Feature Learning for EEG Decoding.
TL;DR: Zhang et al. as mentioned in this paper proposed a novel EEG decoding method that mainly relies on the attention mechanism and applied attention transforming on the feature-channel dimension so that the model can enhance more relevant spatial features.
Journal ArticleDOI
MI-CAT: A transformer-based domain adaptation network for motor imagery classification.
TL;DR: Wang et al. as mentioned in this paper adopted a patch embedding layer for the extracted source and target features to divide the features into multiple patches, and comprehensively focused on the intra-domain and inter-domain features by stacked multiple Cross-Transformer Blocks (CTBs), which can adaptively conduct bidirectional knowledge transfer and information exchange between domains.
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Common Spatial Generative Adversarial Networks based EEG Data Augmentation for Cross-Subject Brain-Computer Interface.
TL;DR: In this article, a cross-subject EEG classification framework with a generative adversarial networks (GANs) based method named common spatial GAN (CS-GAN), which used adversarial training between a generator and a discriminator to obtain high-quality data for augmentation.
References
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