EEG Signal Processing Techniques and Applications—3rd Edition
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biomedical Sensors".
Deadline for manuscript submissions: 31 March 2025 | Viewed by 117
Special Issue Editors
Interests: computing, simulation and modelling; human factors; industrial automation; instrumentation, sensors and measurement science; systems engineering; through-life engineering services
Special Issues, Collections and Topics in MDPI journals
Interests: nonlinear signal processing; system identification; statistical machine learning; frequency-domain analysis; causality analysis; computational neuroscience
Special Issues, Collections and Topics in MDPI journals
Interests: brain dynamics and brain activities; brain–computer interfaces; AI for clinical disease diagnosis; neurorehabilitation; hybrid-augmented intelligence
Special Issues, Collections and Topics in MDPI journals
Interests: bioscience signal processing; data modeling
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Electroencephalography (EEG) is a well-established, non-invasive tool to record brain electrophysiological activity. It is economical, portable, easy to administer, and widely available in most hospitals. Compared with other neuroimaging techniques that provide information about the anatomical structure (e.g., MRI, CT, and fMRI), EEG offers ultra-high time resolution, which is critical in understanding brain function. The empirical interpretation of EEG is largely based on recognizing abnormal frequencies in specific biological states, the spatial-temporal and morphological characteristics of paroxysmal or persistent discharges, reactivity to external stimuli, and activation procedures or intermittent photic stimulation. Despite being useful in many instances, these practical approaches to interpreting EEGs can leave important dynamic and nonlinear interactions between various brain network anatomical constituents undetected within its recordings; as such, interactions are far beyond the observational capabilities of any specially trained physician in this field.
This Special Issue will provide a forum for original high-quality research on EEG signal pre-processing, modeling, analysis, and its applications in the time, space, frequency, or time–frequency domains. The applications of artificial intelligence and machine learning approaches in this topic are particularly welcomed. The types of applications to consider include but are not limited to the following:
- Clinical studies.
- Human factors.
- Brain–machine interfaces.
- Psychology and neuroscience.
- Social interactions.
Prof. Dr. Yifan Zhao
Dr. Fei He
Dr. Yuzhu Guo
Dr. Hua-Liang Wei
Guest Editors
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- electroencephalography
- EEG signal processing
- artificial intelligence in EEG data analysis
- brain connectivity
- time–frequency analysis
- deep learning in EEG data analysis
- machine learning techniques in EEG data analysis
- computer-aided diagnosis systems
Benefits of Publishing in a Special Issue
- Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
- Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
- Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
- External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
- e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.
Further information on MDPI's Special Issue polices can be found here.
Related Special Issue
- EEG Signal Processing Techniques and Applications—2nd Edition in Sensors (18 articles)
