Special Issue "The Challenges in Brain-Computer Interface (BCI) - toward Practical BCI"
Deadline for manuscript submissions: closed (31 July 2020).
Interests: brain–computer Interface (BCI); neuromodulation; myoelectric control; deep learning; machine learning
Special Issues and Collections in MDPI journals
Interests: brain-computer interface (BCI), machine learning, EEG/NIRS hybrid brain signal analysis
For the last several decades, the brain–computer interface (BCI) has been intensively studied to establish a novel method of communication using brain activity for those who are paralyzed but have intact brain functions. However, the performance and reliability of BCI technologies are still limited due to various challenging issues, such as the nonstationarity nature of brain activity, session-to-session transfer, physiological artifacts contained in brain activity, and so on. Consequently, clinically available BCI systems have rarely been introduced to date.
This Special Issue aims to share the current state-of-the-art trends and future directions in the BCI field, thereby encouraging the development of practical solutions to tackle the aforementioned challenging issues. We invite researchers to submit original research articles, clinical studies, and review/survey articles that contribute to the advance of BCI technologies based on non-invasive neuroimaging modalities, i.e., electroencephalography (EEG) and near-infrared spectroscopy (NIRS). This Special Issue will focus in the challenges in practical BCI, including but not limited to:
- Enhanced BCI Performance: Development of new devices, algorithms, and paradigms;
- Reliable BCI: Evaluation of test-retest reliability and session-to-session transfer based on multiday datasets;
- Ambulatory BCI: Development of portable, easy-to-use, and wireless EEG/NIRS recording systems and their related methodologies;
- Neuromodulation-based BCI: Use of electrical and magnetic brain stimulation to improve the performance and reliability of BCI systems;
- Practical BCI: Development of BCI applications, e.g., rehabilitation, entertainment, drowsiness detection, emotion decoding;
- Development of new artifact rejection algorithms for EOG, EMG, ECG, etc.;
- Releasing publicly available BCI datasets;
- Review/survey articles.
Dr. Han-Jeong Hwang
Dr. Chang-Hee Han
Manuscript Submission Information
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