Advances in EEG-Based Brain–Computer Interface Systems
A Special Issue of Sensors (ISSN 1424-8220) belonging to the section "Biomedical Sensors".
Deadline for manuscript submissions: 30 June 2027 | Viewed by 68
Editor
Interests: electroencephalography (EEG); brain–computer interfaces (BCIs); signal processing; machine learning; neuroergonomics; neural engineering
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Electroencephalography (EEG)-based brain–computer interfaces (BCIs) have rapidly evolved from laboratory demonstrations into viable systems for clinical rehabilitation, neuro-ergonomics, assistive technology and human–computer interaction. By translating neural dynamics directly into actionable control signals, EEG-BCIs offer non-invasive, high-temporal-resolution pathways for communication, motor recovery, cognitive monitoring and intuitive device control.
Despite substantial progress, several critical challenges remain before EEG-BCIs can achieve widespread real-world deployment. These include low signal-to-noise ratios, non-stationarity of neural signals, high inter/intra-subject variability, lengthy calibration phases and the need for lightweight, wearable sensor hardware. Overcoming these hurdles demands interdisciplinary innovation across ultra-low-power sensing hardware, adaptive signal processing algorithms, deep learning paradigms, cross-subject transfer learning and robust closed-loop systems.
This Special Issue aims to highlight recent technical advancements, innovative sensor designs, novel signal processing methodologies and emerging applications in EEG-based BCIs. We invite authors to submit original research articles, theoretical developments and comprehensive review papers.
Topics of interest include, but are not limited to, the following:
- Next-generation EEG sensors, dry electrodes and wearable acquisition hardware
- Advanced signal processing, denoising and artifact removal techniques
- Feature extraction, deep learning and decoding algorithms for EEG signals
- Adaptive, zero-calibration and transfer learning methods for cross-subject BCIs
- Hybrid BCIs (combining EEG with fNIRS, EMG, EOG or eye-tracking)
- Real-time paradigms (P300, SSVEP, motor imagery, steady-state response)
- Clinical applications (neurorehabilitation, stroke recovery, neuroprosthetics)
- Non-clinical applications (neuroergonomics, automotive safety, gaming, immersive XR)
- Ethics, privacy and user experience in persistent BCI deployment
We look forward to receiving your valuable contributions.
Dr. Rifai Chai
Guest Editor
Manuscript Submission Information
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Keywords
- brain–computer interface (BCI)
- electroencephalography (EEG)
- wearable EEG sensors
- signal processing and artifact removal
- neural decoding and machine learning
- deep learning for EEG
- transfer learning and adaptation
- motor imagery/SSVEP/P300
- neurorehabilitation and assistive tech
- human–computer interaction (HCI)
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