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Applied Sciences
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New Insights into AI-Based EEG and Biosignals
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New Insights into AI-Based EEG and Biosignals

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (30 October 2023) | Viewed by 2749

Special Issue Editors

Dr. Paula M. Castro

E-Mail Website
Guest Editor
CITIC Research Centre, University of A Coruña, 15071 A Coruña, Spain
Interests: wireless communications; signal processing
Dr. Adriana Dapena

E-Mail Website
Guest Editor
CITIC Research Centre, University of A Coruña, 15071 A Coruña, Spain
Interests: complex networks; blind source separation; EEG signal processing

Special Issue Information

Dear Colleagues,

In recent years, new human–machine interfaces (HMIs) have been developed to facilitate communication and interactions between people with severe motor difficulties and their environment, or even with damaged parts of their own body. By using technologies such as the Internet of Things (IoT) and artificial intelligence techniques, they can satisfy their daily life needs by remotely accessing, controlling and monitoring devices through different biological signals, also known as biosignals, that can come from different parts of their body. A wide variety of these biosignals can be used to interact with HMIs. The use of each will depend on the purpose of the interface and the motor capabilities of the user. The three most common types of biological signals are: muscular, ocular and cerebral. This Special Issue focuses on the analysis and development of interfaces based not only on brain signals that capture the biosignals produced by the user's neural activity through noninvasive techniques such as electroencephalography (EEG), but also on other biosignals, or a combination of several of them, in order to improve the interaction with the environment and the patient's progress in performing activities such as daily living, a rehabilitation program or their usual therapy based on the joint data extracted.

Dr. Paula M. Castro
Dr. Adriana Dapena
Guest Editors

Manuscript Submission Information

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Keywords

  • artificial intelligence
  • data acquisition
  • data analysis
  • biosignals
  • electroencephalography (EEG)
  • electromyography (EMG)
  • electrooculography (EOG)
  • Internet of Things
  • signal processing

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Published Papers (1 paper)

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Research

16 pages, 8644 KiB  
Article
Deep Representation of EEG Signals Using Spatio-Spectral Feature Images
by Nikesh Bajaj and Jesús Requena Carrión
Appl. Sci. 2023, 13(17), 9825; https://doi.org/10.3390/app13179825 - 30 Aug 2023
Cited by 2 | Viewed by 2167
Abstract
Modern deep neural networks (DNNs) have shown promising results in brain studies involving multi-channel electroencephalogram (EEG) signals. The representations produced by the layers of a DNN trained on EEG signals remain, however, poorly understood. In this paper, we propose an approach to interpret [...] Read more.
Modern deep neural networks (DNNs) have shown promising results in brain studies involving multi-channel electroencephalogram (EEG) signals. The representations produced by the layers of a DNN trained on EEG signals remain, however, poorly understood. In this paper, we propose an approach to interpret deep representations of EEG signals. Our approach produces spatio-spectral feature images (SSFIs) that encode the EEG input patterns that activate the neurons in each layer of a DNN. We evaluate our approach using the PhyAAt dataset of multi-channel EEG signals for auditory attention. First, we train the same convolutional neural network (CNN) architecture on 25 separate sets of EEG signals from 25 subjects and conduct individual model analysis and inter-subject dependency analysis. Then we generate the SSFI input patterns that activate the layers of each trained CNN. The generated SSFI patterns can identify the main brain regions involved in a given auditory task. Our results show that low-level CNN features focus on larger regions and high-level features focus on smaller regions. In addition, our approach allows us to discern patterns in different frequency bands. Further SSFI saliency analysis reveals common brain regions associated with a specific activity for each subject. Our approach to investigate deep representations using SSFI can be used to enhance our understanding of the brain activity and effectively realize transfer learning. Full article
(This article belongs to the Special Issue New Insights into AI-Based EEG and Biosignals)
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