Advance Research of Neurodynamics to Enhance Noninvasive Brain-Computer Interface and Its Applications

Dear Colleagues,

Brain–computer Interfaces (BCIs) are systems that establish a direct communication pathway between users’ brain activity and external effectors. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. In recent years, BCI has also proven useful for rehabilitation after stroke and for other disorders. Despite the advancement obtained in the last decade, we still are far from a clear control of the machinery by means of brain intention. To achieve this goal, we need to progress on signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Moreover, we need to increase efficiency in detecting brain signal intention and translate it into efficient commands for human–machine applications. In this respect, linear methods that are predominantly used in characterizing brain neurodynamics in both healthy and pathological conditions may not be suitable to describe the irregular and non-periodic patterns recorded by electrophysiological and neuroimaging techniques. Brain neurodynamics contain “hidden information” that might be captured by nonlinear methods, and thus, they may provide crucial and so far overlooked physiological information that may push BCI systems forward. 

The aim of the present Special Issue is to provide a general overview of recent linear and nonlinear signal processing and human–machine interaction supported by brain–computer interfaces. In particular, BCI systems benefit users if the decoded actions reflect the users’ intentions with an accuracy that enables them to efficiently interact with their environment. This interaction can also be related to advanced systems in an ambient assisted living scenario, when the human–system interaction requests user supervision to control autonomous systems.

Cutting-edge research topics can include: i) advanced linear and nonlinear signal processing; ii) deep neural networks; and iii) human–machine interaction or cooperation through BCI application.

This Special Issue will cover recent advances in –computer interface (BCI) and brain neurodynamics data analysis by linear and nonlinear methods and its application in human–machine interactions. We encourage submissions of original research and reviews with a focus on new approaches able to effectively enhance the quality of life of BCI users with both neurophysiological recordings (e.g., EEG and MEG) and neuroimaging techniques (e.g., fMRI and fNIRS).

Potential topics include but are not limited to the following:

• EEG pattern data analysis;
• EEG and evoked potential recognition;
• Feature selection for EEG classification;
• Linear and nonlinear classifier for EEG signals;
• Brain–computer interface
• Hybrid brain–computer interface;
• Human–machine interaction;
• BCI control applications.

Dr. Camillo Porcaro
Dr. Sabrina Iarlori
Dr. Francesco Ferracuti
Dr. Andrea Monteriù
Guest Editors

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