Minimally Invasive Local-Skull Electrophysiological Modification With Piezoelectric Drill

Precise prediction of cerebrospinal fluid amyloid beta protein for early Alzheimer's disease detection using multimodal data

Alzheimer's disease (AD) constitutes a neurodegenerative disorder marked by a progressive decline in cognitive function and memory capacity. The accurate diagnosis of this condition predominantly relies on cerebrospinal fluid (CSF) markers, notwithstanding the associated burdens of pain and substantial financial costs endured by patients. This study encompasses subjects exhibiting varying degrees of cognitive impairment, encompassing individuals with subjective cognitive decline, mild cognitive impairment, and dementia, constituting a total sample size of 82 participants. The primary objective of this investigation is to explore the relationships among brain atrophy measurements derived from magnetic resonance imaging, atypical electroencephalography (EEG) patterns, behavioral assessment scales, and amyloid β-protein (Aβ) indicators. The findings of this research reveal that individuals displaying reduced Aβ1-42/Aβ-40 levels exhibit significant atrophy in the frontotemporal lobe, alongside irregularities in various parameters related to EEG frequency characteristics, signal complexity, inter-regional information exchange, and microstates. The study additionally endeavors to estimate Aβ1-42/Aβ-40 content through the application of a random forest algorithm, amalgamating structural data, electrophysiological features, and clinical scales, achieving a remarkable predictive precision of 91.6%. In summary, this study proposes a cost-effective methodology for acquiring CSF markers, thereby offering a valuable tool for the early detection of AD.

A LFP/AP Mode Reconfigurable Analog Front-End Combining an Electrical EEEG-iEEG Model for the Closed-Loop VNS

This article presents a local field potential (LFP)/action potential (AP) mode reconfigurable analog front-end (AFE) dedicated for the closed-loop vagus nerve stimulator (VNS). It combines an inverse electrical model of the intracranial electroencephalogram (iEEG) conducting in the brain tissues and been recorded at scalp as the extended electroencephalogram (EEEG). The AFE contains a LFP/AP mode reconfigurable EEEG preamplifier, a tunable integrator to compensate the effect of either the recording electrodes or head tissues, and an adder. The LFP/AP mode reconfigurable EEEG preamplifier consists of a tunable chopper-stabilized amplifier (CSA) and a 2nd-order tunable low pass filter (LPF). For better separation of LFP and AP signals, a high-order DC servo loop (DSL) characterized as a 2nd-order DSL in parallel with a 1st-order DSL is exploited in the tunable CSA to achieve a tunable high-pass frequency with a stopband attenuation slope (SAS) of +40 dB/dec. In addition, the tunable LPF can obtain a tunable low-pass frequency with a SAS of -40 dB/dec and provide additional 20 dB gain for AP signals. Fabricated in a SMIC 180 nm CMOS technology, and in the LFP band (0.5 Hz-200 Hz) and AP band (300 Hz-5 kHz), the measured mid-band gains of the LFP/AP mode reconfigurable EEEG preamplifier are 39.6 dB and 59.5 dB, the input-referred noises (IRNs) are 2.2 μVrms and 6.3 μVrms, the DC/in-band input impedances are 1.27/1.26 GΩ and 0.3/0.22 GΩ, respectively. The power consumption per channel AFE is 6.3 μW, and the die area is 1.4 mm × 0.25 mm.

Brain-computer interface paradigms and neural coding

Brain signal patterns generated in the central nervous system of brain-computer interface (BCI) users are closely related to BCI paradigms and neural coding. In BCI systems, BCI paradigms and neural coding are critical elements for BCI research. However, so far there have been few references that clearly and systematically elaborated on the definition and design principles of the BCI paradigm as well as the definition and modeling principles of BCI neural coding. Therefore, these contents are expounded and the existing main BCI paradigms and neural coding are introduced in the review. Finally, the challenges and future research directions of BCI paradigm and neural coding were discussed, including user-centered design and evaluation for BCI paradigms and neural coding, revolutionizing the traditional BCI paradigms, breaking through the existing techniques for collecting brain signals and combining BCI technology with advanced AI technology to improve brain signal decoding performance. It is expected that the review will inspire innovative research and development of the BCI paradigm and neural coding.

An ensemble learning model for continuous cognition assessment based on resting-state EEG

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 signals 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.

A Real-Time Non-Implantation Bi-Directional Brain-Computer Interface Solution Without Stimulation Artifacts

The non-implantation bi-directional brain-computer interface (BCI) is a neural interface technology that enables direct two-way communication between the brain and the external world by both "reading" neural signals and "writing" stimulation patterns to the brain. This technology has vast potential applications, such as improving the quality of life for individuals with neurological and mental illnesses and even expanding the boundaries of human capabilities. Nonetheless, non-implantation bi-directional BCIs face challenges in generating real-time feedback and achieving compatibility between stimulation and recording. These issues arise due to the considerable overlap between electrical stimulation frequencies and electrophysiological recording frequencies, as well as the impediment caused by the skull to the interaction of external and internal currents. To address those challenges, this work proposes a novel solution that combines the temporal interference stimulation paradigm and minimally invasive skull modification. A longitudinal animal experiment has preliminarily validated the feasibility of the proposed method. In signal recording experiments, the average impedance of our scheme decreased by 4.59 kΩ , about 67%, compared to the conventional technique at 18 points. The peak-to-peak value of the Somatosensory Evoked Potential increased by 8%. Meanwhile, the signal-to-noise ratio of Steady-State Visual Evoked Potential increased by 5.13 dB, and its classification accuracy increased by 44%. The maximum bandwidth of the resting state rose by 63%. In electrical stimulation experiments, the signal-to-noise ratio of the low-frequency response evoked by our scheme rose by 8.04 dB, and no stimulation artifacts were generated. The experimental results show that signal quality in acquisition has significantly improved, and frequency-band isolation eliminates stimulation artifacts at the source. The acquisition and stimulation pathways are real-time compatible in this non-implantation bi-directional BCI solution, which can provide technical support and theoretical guidance for creating closed-loop adaptive systems coupled with particular application scenarios in the future.