Correction to: Innovative deep learning models for EEG-based vigilance detection

Khessiba, Souhir; Blaiech, Ahmed Ghazi; Khalifa, Khaled Ben; Abdallah, Asma Ben; Bedoui, Mohamed Hédi

doi:10.1007/s00521-021-06187-0

Cited by 6 publications

(1 citation statement)

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“…The function of the convolution layer is to extract signal features, and the convolution operation mainly adopts Sparse Connectivity and Shared Weights, which can greatly reduce the number of CNN parameters and accelerate the training speed [60][61][62]. Pooling layer can reduce the data dimension and the complexity of network computing, accelerating the computing speed [63,64]. After feature extraction, FC layer is used to perceive global information and complete classification.…”

Section: Cnn Classificationmentioning

confidence: 99%

A Combined Virtual Electrode-Based ESA and CNN Method for MI-EEG Signal Feature Extraction and Classification

Lun,

Zhang,

Zhu

et al. 2023

Sensors

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A Brain–Computer Interface (BCI) is a medium for communication between the human brain and computers, which does not rely on other human neural tissues, but only decodes Electroencephalography (EEG) signals and converts them into commands to control external devices. Motor Imagery (MI) is an important BCI paradigm that generates a spontaneous EEG signal without external stimulation by imagining limb movements to strengthen the brain’s compensatory function, and it has a promising future in the field of computer-aided diagnosis and rehabilitation technology for brain diseases. However, there are a series of technical difficulties in the research of motor imagery-based brain–computer interface (MI-BCI) systems, such as: large individual differences in subjects and poor performance of the cross-subject classification model; a low signal-to-noise ratio of EEG signals and poor classification accuracy; and the poor online performance of the MI-BCI system. To address the above problems, this paper proposed a combined virtual electrode-based EEG Source Analysis (ESA) and Convolutional Neural Network (CNN) method for MI-EEG signal feature extraction and classification. The outcomes reveal that the online MI-BCI system developed based on this method can improve the decoding ability of multi-task MI-EEG after training, it can learn generalized features from multiple subjects in cross-subject experiments and has some adaptability to the individual differences of new subjects, and it can decode the EEG intent online and realize the brain control function of the intelligent cart, which provides a new idea for the research of an online MI-BCI system.

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