Manuscript Topics

Nowadays, neurostimulation is considered an established therapy in the clinical practice; it finds application in the treatment of neurologic and psychiatric disorders, such as Tourette syndrome, neuropathic pain, and Parkinson’s disease. With the increasing need of personalized treatments, open-loop neurostimulation systems represent a limitation for this purpose. Indeed, they provide stimuli to the patient in a programmed manner, not adapting to the psychophysical state of the subject. On the other hand, close-loop systems overcome most of the limits of the previous technology, automatically adapting the stimulation to the condition of the patient (e.g., stress or anxiety), through a feedback mechanism. Different types of signals can be acquired for this purpose, such as electrocardiogram (ECG), photoplethysmogram (PPG), breathing rhythm, electroencephalogram (EEG), and pupillogram. However, the implementation of a close-loop system is not trivial, especially from a signal processing point of view, as a reliable and efficient method is required.

This Special Issue is focused on new methods for neurostimulation adapted to the user by using physiological parameters.

Topics of interest include, but are not limited to:
• Real-time and offline neurostimulation solutions;
• Algorithms integrating information from more physiological signals;
• Adaptive neurostimulation using signals recorded by portable devices;
• Machine Learning approaches;
• Therapeutical applications of neurostimulation.

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