Brain-Computer and Brain-Machine Interfaces: Advances in EEG Acquisition, Processing and Machine Learning Technologies towards Better Usability

Dear Colleagues,

Recent developments in data acquisition and processing technology for electroencephalography (EEG), alongside new filtering and machine learning methods, are helping to improve the decoding performance of simplified, low electrode-count EEG systems. These developments have the potential for expanding the scope and potential clinical applicability of EEG-based Brain-Computer and Brian-Machine Interfaces (BCI, BMI). To further accelerate this trend and enhance practical viability and thus social impact, it is necessary to conjointly explore multiple innovation avenues. One example is creating, bolstering, and exploring pathways for incorporating knowledge harvested from laboratory-based experiments using high-density EEG or other neuroimaging methods, alongside machine learning algorithms including deep-learning, into portable and self-contained EEG systems truly suitable for everyday patient use.
Under this overarching aim, the special issue addresses all types of EEG-based neural decoding infrastructure aimed at BCI and BMI, including but not limited to the following:

• Adaptive and non-linear decoding techniques
• Advances in real-time processing technology, including artefact reduction and electrode fault mitigation
• Advances in sensor, front-end and other hardware technologies, including practical circuits
• Advances in all engineering aspects of low-cost, wearable devices, including human-centric design
• Analyses of normative, certification and impact, including health technology assessment-based analyses and innovation models
• Brain activity imaging using EEG or other neuroimaging techniques, focusing on extracting priors for boosting decoding based on low channel numbers
• Forward techniques for optimizing location and number of electrodes
• General applications for portable or wearable EEG data acquisition systems
• Hybrid approaches, for example combining EEG with machine vision to drive a robot
• Multi-modal approaches, for example fusing EEG with electro-oculography or systemic physiological responses
• New patient-friendly paradigms, focusing on their realistic performance in low channel-count scenarios
• Open data-sets and community resources
• Open-source hardware and software, with focus on low-cost, wearable devices
• Patient-based studies of realistic application scenarios including usability evaluations
• Performance comparisons between low- and high- density EEG, including laboratory- vs. field-based context comparisons
• Signal processing techniques and machine learning algorithms, with a focus on deep-learning techniques

Prof. Natsue Yoshimura
Guest Editor

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