PET Mapping for Brain-Computer Interface Stimulation of the Ventroposterior Medial Nucleus of the Thalamus in Rats with Implanted Electrodes

Cybersecurity in neural interfaces: Survey and future trends

With the joint advancement in areas such as pervasive neural data sensing, neural computing, neuromodulation and artificial intelligence, neural interface has become a promising technology facilitating both the closed-loop neurorehabilitation for neurologically impaired patients and the intelligent man-machine interactions for general application purposes. However, although neural interface has been widely studied, few previous studies focused on the cybersecurity issues in related applications. In this survey, we systematically investigated possible cybersecurity risks in neural interfaces, together with potential solutions to these problems. Importantly, our survey considers interfacing techniques on both central nervous systems (i.e., brain-computer interfaces) and peripheral nervous systems (i.e., general neural interfaces), covering diverse neural modalities such as electroencephalography, electromyography and more. Moreover, our survey is organized on three different levels: (1) the data level, which mainly focuses on the privacy leakage issue via attacking and analyzing neural database of users; (2) the permission level, which mainly focuses on the prospects and risks to directly use real time neural signals as biometrics for continuous and unobtrusive user identity verification; and (3) the model level, which mainly focuses on adversarial attacks and defenses on both the forward neural decoding models (e.g. via machine learning) and the backward feedback implementation models (e.g. via neuromodulation and stimulation). This is the first study to systematically investigate cybersecurity risks and possible solutions in neural interfaces which covers both central and peripheral nervous systems, and considers multiple different levels to provide a complete picture of this issue.

Using Brain-Computer Interface to Control a Virtual Drone Using Non-Invasive Motor Imagery and Machine Learning

In recent years, the control of devices “by the power of the mind” has become a very controversial topic but has also been very well researched in the field of state-of-the-art gadgets, such as smartphones, laptops, tablets and even smart TVs, and also in medicine, to be used by people with disabilities for whom these technologies may be the only way to communicate with the outside world. It is well known that BCI control is a skill and can be improved through practice and training. This paper aims to improve and diversify signal processing methods for the implementation of a brain-computer interface (BCI) based on neurological phenomena recorded during motor tasks using motor imagery (MI). The aim of the research is to extract, select and classify the characteristics of electroencephalogram (EEG) signals, which are based on sensorimotor rhythms, for the implementation of BCI systems. This article investigates systems based on brain-computer interfaces, especially those that use the electroencephalogram as a method of acquisition of MI tasks. The purpose of this article is to allow users to manipulate quadcopter virtual structures (external, robotic objects) simply through brain activity, correlated with certain mental tasks using undecimal transformation (UWT) to reduce noise, Independent Component Analysis (ICA) together with determination coefficient (r2) and, for classification, a hybrid neural network consisting of Radial Basis Functions (RBF) and a multilayer perceptron–recurrent network (MLP–RNN), obtaining a classification accuracy of 95.5%. Following the tests performed, it can be stated that the use of biopotentials in human–computer interfaces is a viable method for applications in the field of BCI. The results presented show that BCI training can produce a rapid change in behavioral performance and cognitive properties. If more than one training session is used, the results may be beneficial for increasing poor cognitive performance. To achieve this goal, three steps were taken: understanding the functioning of BCI systems and the neurological phenomena involved; acquiring EEG signals based on sensorimotor rhythms recorded during MI tasks; applying and optimizing extraction methods, selecting and classifying characteristics using neuronal networks.

Brain-Machine Interface-Based Rat-Robot Behavior Control

Brain-machine interface (BMI) provides a bidirectional pathway between the brain and external facilities. The machine-to-brain pathway makes it possible to send artificial information back into the biological brain, interfering neural activities and generating sensations. The idea of the BMI-assisted bio-robotic animal system is accomplished by stimulations on specific sites of the nervous system. With the technology of BMI, animals' locomotion behavior can be precisely controlled as robots, which made the animal turning into bio-robot. In this chapter, we reviewed our lab works focused on rat-robot navigation. The principles of rat-robot system have been briefly described first, including the target brain sites chosen for locomotion control and the design of remote control system. Some methodological advances made by optogenetic technologies for better modulation control have then been introduced. Besides, we also introduced our implementation of "mind-controlled" rat navigation system. Moreover, we have presented our efforts made on combining biological intelligence with artificial intelligence, with developments of automatic control and training system assisted with images or voices inputs. We concluded this chapter by discussing further developments to acquire environmental information as well as promising applications with write-in BMIs.

PET imaging on neurofunctional changes after optogenetic stimulation in a rat model of panic disorder

Panic disorder (PD) is an acute paroxysmal anxiety disorder with poorly understood pathophysiology. The dorsal periaqueductal gray (dPAG) is involved in the genesis of PD. However, the downstream neurofunctional changes of the dPAG during panic attacks have yet to be evaluated in vivo. In this study, optogenetic stimulation to the dPAG was performed to induce panic-like behaviors, and in vivo positron emission tomography (PET) imaging with ¹⁸F-flurodeoxyglucose (¹⁸F-FDG) was conducted to evaluate neurofunctional changes before and after the optogenetic stimulation. Compared with the baseline, post-optogenetic stimulation PET imaging demonstrated that the glucose metabolism significantly increased (P < 0.001) in dPAG, the cuneiform nucleus, the cerebellar lobule, the cingulate cortex, the alveus of the hippocampus, the primary visual cortex, the septohypothalamic nucleus, and the retrosplenial granular cortex but significantly decreased (P < 0.001) in the basal ganglia, the frontal cortex, the forceps minor corpus callosum, the primary somatosensory cortex, the primary motor cortex, the secondary visual cortex, and the dorsal lateral geniculate nucleus. Taken together, these data indicated that in vivo PET imaging can successfully detect downstream neurofunctional changes involved in the panic attacks after optogenetic stimulation to the dPAG.

Intrinsic optical imaging study on cortical responses to electrical stimulation in ventral posterior medial nucleus of thalamus

Intracortical electrical micro-stimulation has been applied widely for the attempts on reconstruction of sensory functions. More recently, thalamic electrical stimulation has been proposed as a promising target for somatosensory stimulation. However, the cortical activations and mechanisms evoked by VPM stimulation remained unclear. In this report, the cortical neural responses to electrical stimulations were recorded by optical imaging of intrinsic signals. The impact of stimulation parameters was characterized to illustrate how the VPM stimulation alter cortical activities. Significant increases were found in cortical responses with increased stimulation amplitude or pulse width. However, frequency modulation exhibited significant inhibition with higher frequency stimulation. Our results suggest that optical imaging of intrinsic signals is sensitive and reliable to deep brain stimulations. These results may not only help to understand the modulation effects through thalamocortical pathway, but also show the possibility to use VPM stimulation to evoke frequency-tuned tactile sensations in rats.