The research on non-invasive BCI is nowadays hitting the bottleneck due to the humble quality of scalp EEG signals. Whereas invasive solutions that offer higher signal quality in contrast are suffocated in their spreading because of the potential surgical complication and health risks caused by electrode implantation. Therefore, it puts forward a necessity to explore a scheme that could both collect high-quality EEG signals and guarantee high-level operation safety.This study proposed a Minimally Invasive Local-skull Electrophysiological Modification method to improve scalp EEG signals qualities at specific brain regions. Six eight-month-old SD rats were used for in vivo verification experiment. A hole with a diameter of about 500 micrometers was drilled in the skull above the visual cortex of rats. Significant changes in rsEEG and SSVEP signals before and after modification were observed. After modification, the skull impedance of rats decreases by about 84 % , the average maximum bandwidth of rsEEG increase by 57 %, and the broadband SNR of SSVEP is increased by 5.13 dB. The time of piezoelectric drilling operation is strictly controlled under 30 seconds for each rat to prevent possible brain damage from overheating. Compared with traditional invasive procedures such as ECoG, Minimally Invasive Local-skull Electrophysiological Modification operation time is shorter and no electrode implantation is needed while it remarkably boosts the scalp EEG signal quality. This technical solution has the potential to replace the use of ECoG in certain application scenarios and further invigorate studies in the field of scalp EEG in the future.

Hypertension is one of the most prevalent chronic diseases that affects more than 20% of the adult population worldwide, but fortunately, most of their blood pressure can be effectively controlled via drug treatment. However, there still remains 5–30% of patients clinically who do not respond well to conventional medication, while the non-drug treatments currently existing are struggling with major drawbacks like irreversible nerve damage, huge side effects, and even non-effectiveness. In this study, based on the physiological regulation mechanism of blood pressure and state-of-the-art neuromodulation technique, we worked along with the vagus nerve stimulation scheme, developed, and explored whether and how a real-time neural recording and stimulation system could provide an insight into self-adaptive modulation in the blood pressure, in the hope to crack a crevice in the closed-loop treatment for resistant hypertension. Unlike traditional neuromodulation devices, additional signal recording and real-time wireless transmission functions are added to the same device to realize the features of a dynamic monitor and modulator. The system is tested both in vitro and in vivo, showing decent electrical performance of 8 kHz sampling rate and flexible stimulation outputs which sufficiently covers our needs in manipulating neural activities of interest. A relatively stable drop in the blood pressure resulting from stimulation was observed and specific patterns in the vagus nerve signals relating to blood pressure could also be primarily identified. This laid a solid foundation for further studies on the final realization of closed-loop automatic adjustment for resistive hypertension treatment.

One critical manifestation of neurological deterioration is the sign of cognitive decline. Causes of cognitive decline include but are not limited to: aging, cerebrovascular disease, Alzheimer's disease, and trauma. Currently, the primary tool used to examine cognitive decline is scale. However, scale examination has drawbacks such as its clinician subjectivity and inconsistent results. This study attempted to use resting-state EEG to construct a cognitive assessment model that is capable of providing a more scientific and robust evaluation on cognition levels. In this study, 75 healthy subjects, 99 patients with Mild Cognitive Impairment (MCI), and 78 patients with dementia were involved. Their resting-state EEG signlas were collected twice, and the recording devices varied. By matching these EEG and traditional scale results, the proposed cognition assessment model was trained based on Adaptive Boosting (AdaBoost) and Support Vector Machines (SVM) methods, mapping subjects' cognitive levels to a 0-100 test score with a mean error of 4.82 (< 5%). This study is the first to establish a continuous evaluation model of cognitive decline on a large sample dataset. Its cross-device usability also suggests universality and robustness of this EEG model, offering a more reliable and affordable way to assess cognitive decline for clinical diagnosis and treatment as well. Furthermore, the interpretability of features involved may further contribute to the early diagnosis and superior treatment evaluation of Alzheimer's disease.