Abstract
In the field of brain-computer interface (BCI), selecting efficient and robust features is very seductive for artificial intelligence (AI)-assisted clinical diagnosis. In this study, based on an embedded feature selection model, we construct a stacked deep structure for feature selection in a layer-by-layer manner. Its promising performance is guaranteed by the stacked generalized principle that random projections added into the original features can help us to continuously open the manifold structure existing in the original feature space in a stacked way. With such benefits, the original input feature space becomes more linearly separable. We use the epilepsy EEG data provided by the University of Bonn to evaluate our model. Based on the EEG data, we construct three classification tasks. On each task, we use different feature selection models to select features and then use two classifiers to perform classification based on the selected features. Our experimental results show that features selected by our new structure are more meaningful and helpful to the classifier hence generates better performance than benchmarking models.
Highlights
Electroencephalogram (EEG) as a biomarker plays an important role in the brain-computer interface (BCI) (Wang et al, 2013; Zheng, 2017; Mammone et al, 2019; Nakamura et al, 2020)
The benefits we inherit from the stacked deep structure lie in that the random projections added into the original features can help us to continuously open the manifold structure existing in the original feature space in a stacked way
From our experimental results, we find that features obtained from embedded feature selection models (SDE-JS-Regression and E-JS-Regression) are more inductive to the classifier than filter models and wrapper models (RFE-SVM)
Summary
Electroencephalogram (EEG) as a biomarker plays an important role in the brain-computer interface (BCI) (Wang et al, 2013; Zheng, 2017; Mammone et al, 2019; Nakamura et al, 2020). EEG signals are often used to determine the presence and type of epilepsy in clinical diagnosis (Rieke et al, 2003; Yetik et al, 2005; Adeli et al, 2007; Lopes da Silva, 2008; Coito et al, 2016; Parvez and Paul, 2016; Peker et al, 2016; Panwar et al, 2019). A standard EEG-based AI-assisted diagnosis flowchart is illustrated, which contains signal acquisition, signal processing, feature extraction, feature selection and model training and testing. As we know that original features extracted from EEG signals cannot be directly used for model training because they are often represented in very high-dimensional feature space. We focus on how to selection effective features to guarantee high-efficiency AI-assisted clinical diagnosis
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