Unlike traditional methods that implant passive optical components like fibers and rod lenses, optoelectronic semiconductor-based devices directly implant active optoelectronic semiconductors into the brain. This approach offers several advantages—the devices are compact and lightweight, enabling measurement and control without hindering the movement of small animals like mice. Additionally, it allows for simultaneous implantation of multiple devices, and integration with other functions. However, potential temperature increment and biocompatibility due to the active nature of these devices are major drawbacks. This paper reviews novel optoelectronic semiconductor-based devices for measuring and controlling brain nerve function. The advantages of brain-implantable optoelectronic semiconductor devices for fluorescence imaging and photostimulation are highlighted. We address potential limitations and propose future improvements, demonstrating their significant potential to advance neuroscience and pharmacology.

Accurately classifying electroencephalogram (EEG) signals, especially for individuals with neurodegenerative conditions such as myotrophic lateral sclerosis (ALS), poses a significant challenge due to high inter-subject and inter-session changes in signal. This study introduces a novel three-layer graph attention network (GAT) model for motor imagery (MI) classification, utilizing phase locking value (PLV) as the graph input. The GAT model outperforms state-of-the-art deep learning methods, demonstrating notable improvements with a two-class accuracy of 74.06% on an ALS dataset (approximately 320 trials collected over 1-2 months), and 71.89% on the BCI Comp IV 2a Dataset. This improvement demonstrates the effectiveness of graph-based representations to enhance classification performance for neurodegenerative conditions. There are statistically significant reductions in variance compared to state-of-the-art, due to subject-specific attention given by the model during testing. These results support the hypothesis that phase-locking value-based graph representations can enhance neural representations in BCIs, offering promising avenues for more personalized approaches in MI classification. This study highlights the potential for further optimizing GAT architectures and feature sets, pointing to future research directions that could improve performance and efficiency in MI classification tasks whilst establishing a lightweight methodology.

An ultrasound (US) phased array with electronic steering and focusing capability can enable high-resolution, large-scale US interventions in various medical research and clinical experiments. For such applications involving different animal subjects and humans, the phased array system must provide flexibility in generating waveforms with different patterns (including experimental parameters), precise delay resolution between channels, and high voltage across US transducers to produce high US pressure output over extended durations. This paper presents a 16-channel high-voltage phased array system designed for therapeutic medical applications, capable of driving US transducers with pulses up to 100 V and a fine delay resolution of 5 ns, while providing a wide range of sonication waveforms. The modular 16-channel electronics are integrated with a custom-built, 2 MHz, 16-element US transducer array with dimensions of 4.3×11.7×0.7 mm³. In measurements, the phased array system achieved a peak-to-peak US pressure output of up to 6 MPa at a focal depth of 10 mm, with lateral and axial resolution of 0.6 mm and 4.67 mm, respectively. Additionally, the beam focusing and steering capability of the system in measurements and the theoretical analysis of the power consumption of the high-voltage driver (along with measured results) are provided. Finally, the phased array system’s ability to steer and focus the ultrasound beam for blood-brain barrier (BBB) opening in different brain regions is successfully demonstrated in vivo.

Electrophysiological recording and neural stimulation in freely moving laboratory mice offer significant potential for advancing in neuroscience research, enabling the study of neural activities and brain functions in natural surroundings. Using wireless technologies and miniaturized devices, researchers can monitor and manipulate the electrical activity of neurons in real time while the animals engage in complex behaviors. However, depending on its size and weight, the autonomy of a wireless system is limited to a few minutes or a few hours at most. To address this, a wireless link for continuous power transmission is essential to run practical experiments. Working with mice is challenging due to their small size and limited volume available, necessitating the use of very small coils. It is also crucial to maintain the Specific Absorption Rate (SAR) within safe limits to prevent heating and temperature rises that could interfere with physiological conditions and measurements. This paper introduces a methodology to design an optimized overlapping multi-coil array integrated within a standard homecage, featuring a high-quality factor design that effectively couples with a small, lightweight receiver coil for in-vivo measurements with laboratory mice. Using trace adjustment, the transmitter design enhances the self-resonance frequency (SRF) of the coils, resulting in an improved quality factor, with measurements indicating a value of 173 at 6.78 MHz. Using a 0.46-gram, 14-mm receiver (RX) coil, the measurement results reveal a maximum power transfer efficiency (PTE) of 7.5% and a maximum power delivered to the load (PDL) of 23.8 dBm (240 mW) at a 4-cm distance. Additionally, continuous in-vivo recording sessions demonstrate the delivery of approximately 46 mW on average wirelessly to the battery using a 0.4-gram, 16-mm RX coil installed on the head of a laboratory mouse. The system also prevents thermal effects in mice tissues, with a peak spatial-average SAR (psSAR) of 1.75 W/kg, which is well below the standard regulatory limits.

Neural interfaces have played an increasingly significant part in people’s lives. A mm-scale fully-implanted neural system-on-a-chip is required for long-term bio-compatible recording in applications such as fundamental neuroscience research, neural prosthesis, and neurological disease diagnosis. This paper aims to survey and discuss the current wireless intrabody communication methods used in neural implants, including far-field radio frequency, nearfield inductive coupling, ultrasonic, near-infrared, capacitive body coupling, and galvanic body coupling communication. Starting with the discussion of communication requirements, the performance of each approach is evaluated in terms of mechanism, trade-offs, characteristics, and tissue safety. From the viewpoint of wireless communication, we present a detailed analysis and comparison of neural implants that employ different data telemetry technologies. After identifying the challenges of neural implants, several optimizations are summarized.