On closed-loop brain stimulation systems for improving the quality of life of patients with neurological disorders

Abstract

Emerging brain technologies have significantly transformed human life in recent decades. For instance, the closed-loop brain-computer interface (BCI) is an advanced software-hardware system that interprets electrical signals from neurons, allowing communication with and control of the environment. The system then transmits these signals as controlled commands and provides feedback to the brain to execute specific tasks. This paper analyzes and presents the latest research on closed-loop BCI that utilizes electric/magnetic stimulation, optogenetic, and sonogenetic techniques. These techniques have demonstrated great potential in improving the quality of life for patients suffering from neurodegenerative or psychiatric diseases. We provide a comprehensive and systematic review of research on the modalities of closed-loop BCI in recent decades. To achieve this, the authors used a set of defined criteria to shortlist studies from well-known research databases into categories of brain stimulation techniques. These categories include deep brain stimulation, transcranial magnetic stimulation, transcranial direct-current stimulation, transcranial alternating-current stimulation, and optogenetics. These techniques have been useful in treating a wide range of disorders, such as Alzheimer's and Parkinson's disease, dementia, and depression. In total, 76 studies were shortlisted and analyzed to illustrate how closed-loop BCI can considerably improve, enhance, and restore specific brain functions. The analysis revealed that literature in the area has not adequately covered closed-loop BCI in the context of cognitive neural prosthetics and implanted neural devices. However, the authors demonstrate that the applications of closed-loop BCI are highly beneficial, and the technology is continually evolving to improve the lives of individuals with various ailments, including those with sensory-motor issues or cognitive deficiencies. By utilizing emerging techniques of stimulation, closed-loop BCI can safely improve patients' cognitive and affective skills, resulting in better healthcare outcomes.

Optimization of Deep Learning Models to Predict Lung Cancer Using Chest X-Ray Images

“…• Developing the Devices Used: through developing central processing units and accelerating simulation and analysis processes (Belkacem et al, 2023). • Adapting to Different Environments: By developing continuous learning models without forgetting previous information.…”

The evolution of neuromodulation for chronic stroke: From neuroplasticity mechanisms to brain-computer interfaces

“…Similarly, invasive modalities such as vagus nerve stimulation (VNS) and deep brain stimulation (DBS) show great promise in improving rehabilitation in stroke patients suffering from disabling symptoms. Moreover, there has been rapid development of therapeutic neurostimulation in the form of brain-computer interfaces (BCIs), which utilizes real-time analysis of brain states to enable automatic adjustment of stimulation parameters [ 7 ]. In this paper, we will discuss the landmark trials, current applications, and future directions of the various modalities of invasive neuromodulation for stroke rehabilitation with an emphasis on VNS, DBS and BCI.…”

Generative Neural Spike Prediction from Upstream Neural Activity via Behavioral Reinforcement

“…For instance, one can build a biomimetic model that receives neural signals from upstream regions and predict neural activity in downstream regions. By delivering electrical stimulations to the downstream based on model predictions, such an approach can partially "rewire" the disconnected brain areas and induce similar functionalities 2,3 . In fact, this has been applied in the memory prosthesis 4,5 , which used point process models to predict spikes of the CA1 area as stimulation patterns from spike recordings of the CA3 area in the hippocampus.…”