The NIRS Cap: Key Part of Emerging Wearable Brain-Device Interfaces

Nowadays, near‐infrared spectroscopy (NIRS) fills a niche in medical imaging due to various reasons including non‐invasiveness and portability. The special characteristics of NIRS imaging make it suitable to handle topics that were only approachable using electroencephalography (EEG) such as imaging infants and children; or studying the human brain activity during actions, like walking and drawing that require a certain amount of freedom that non‐portable devices such as magnetic resonance imaging (MRI) cannot permit. This chapter discusses the unique advantages of NIRS as a functional imaging method and the main obstacles that still prevent this technology from becoming a prominent medical imaging tool. In particular, in this chapter we focus on the design of the brain‐device interface: the NIRS cap and its important role in the imaging process

A self-calibration circuit for a neural spike recording channel

This paper presents a self-calibration circuit for a neural spike recording channel. The proposed design tunes the bandwidth of the signal acquisition Band-Pass Filter (BPF), which suffers from process variations corners. It also performs the adjustment of the Programmable Gain Amplifier (PGA) gain to maximize the input voltage range of the analog-to-digital conversion. The circuit, which consists on a frequency-controlled signal generator and a digital processor, operates in foreground, is completely autonomous and integrable in an estimated area of 0.026mm 2 , with a power consumption around 450nW. The calibration procedure takes less than 250ms to select the configuration whose performance is closest to the required one.Ministerio de Ciencia e Innovación TEC2009-08447Junta de Andalucía TIC-0281

A 97 fJ/Conversion Neuron-ADC with Reconfigurable Sampling and Static Power Reduction

A bio-inspired Neuron-ADC with reconfigurable sampling and static power reduction for biomedical applications is proposed in this work. The Neuron-ADC leverages level-crossing sampling and a bio-inspired refractory circuit to compressively converts bio-signal to digital spikes and information-of-interest. The proposed design can not only avoid dissipating ADC energy on unnecessary data but also achieve reconfigurable sampling, making it appropriate for either low power operation or high accuracy conversion when dealing with various kinds of bio-signals. Moreover, the proposed dynamic comparator can reduce static power up to 41.1% when tested with a 10 kHz sinusoidal input. Simulation results of 40 nm CMOS process show that the Neuron-ADC achieves a maximum ENOB of 6.9 bits with a corresponding FoM of 97 fJ/conversion under 0.6 V supply voltage.Comment: Accepted to 2022 IEEE the 18th Asia Pacific Conference on Circuits and Systems (APCCAS

Cascade Decoders-Based Autoencoders for Image Reconstruction

Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvantages of current autoencoders comprise the following several aspects: the research objective is not data reconstruction but feature representation; the performance evaluation of data recovery is neglected; it is hard to achieve lossless data reconstruction by pure autoencoders, even by pure deep learning. This paper aims for image reconstruction of autoencoders, employs cascade decoders-based autoencoders, perfects the performance of image reconstruction, approaches gradually lossless image recovery, and provides solid theory and application basis for autoencoders-based image compression and compressed sensing. The proposed serial decoders-based autoencoders include the architectures of multi-level decoders and the related optimization algorithms. The cascade decoders consist of general decoders, residual decoders, adversarial decoders and their combinations. It is evaluated by the experimental results that the proposed autoencoders outperform the classical autoencoders in the performance of image reconstruction

CSwin2SR: Circular Swin2SR for Compressed Image Super-Resolution

Closed-loop negative feedback mechanism is extensively utilized in automatic control systems and brings about extraordinary dynamic and static performance. In order to further improve the reconstruction capability of current methods of compressed image super-resolution, a circular Swin2SR (CSwin2SR) approach is proposed. The CSwin2SR contains a serial Swin2SR for initial super-resolution reestablishment and circular Swin2SR for enhanced super-resolution reestablishment. Simulated experimental results show that the proposed CSwin2SR dramatically outperforms the classical Swin2SR in the capacity of super-resolution recovery. On DIV2K test and valid datasets, the average increment of PSNR is greater than 1dB and the related average increment of SSIM is greater than 0.006

The NIRS Cap: Key Part of Emerging Wearable Brain-Device Interfaces

ABSTRACT: Nowadays, near‐infrared spectroscopy (NIRS) fills a niche in medical imaging due to various reasons including non‐invasiveness and portability. The special characteristics of NIRS imaging make it suitable to handle topics that were only approachable using electroencephalography (EEG) such as imaging infants and children; or studying the human brain activity during actions, like walking and drawing that require a certain amount of freedom that non‐portable devices such as magnetic resonance imaging (MRI) cannot permit. This chapter discusses the unique advantages of NIRS as a functional imaging method and the main obstacles that still prevent this technology from becoming a prominent medical imaging tool. In particular, in this chapter we focus on the design of the brain‐device interface: the NIRS cap and its important role in the imaging process

CMISR: Circular Medical Image Super-Resolution

Classical methods of medical image super-resolution (MISR) utilize open-loop architecture with implicit under-resolution (UR) unit and explicit super-resolution (SR) unit. The UR unit can always be given, assumed, or estimated, while the SR unit is elaborately designed according to various SR algorithms. The closed-loop feedback mechanism is widely employed in current MISR approaches and can efficiently improve their performance. The feedback mechanism may be divided into two categories: local and global feedback. Therefore, this paper proposes a global feedback-based closed-cycle framework, circular MISR (CMISR), with unambiguous UR and SR elements. Mathematical model and closed-loop equation of CMISR are built. Mathematical proof with Taylor-series approximation indicates that CMISR has zero recovery error in steady-state. In addition, CMISR holds plug-and-play characteristic which can be established on any existing MISR algorithms. Five CMISR algorithms are respectively proposed based on the state-of-the-art open-loop MISR algorithms. Experimental results with three scale factors and on three open medical image datasets show that CMISR is superior to MISR in reconstruction performance and is particularly suited to medical images with strong edges or intense contrast

Supercapacitors: Fabrication Challenges and Trends

Supercapacitors have shown great potential as important complements to batteries. We first describe the principle of supercapacitors, including the categories and the main components of supercapacitors. In the second part, we compare the advantages of supercapacitors with other energy storage devices, and then the power densities of active materials are compared with each other. In the third part, we show how various technologies are used to fabricate electrodes and supercapacitors. In the last part, several applications are presented, showing the high value of supercapacitors, including hybrid vehicles, solar cells, and wearable and portable devices

READS-V: Real-time Automated Detection of Epileptic Seizures from Surveillance Videos via Skeleton-based Spatiotemporal ViG

An accurate and efficient epileptic seizure onset detection system can significantly benefit patients. Traditional diagnostic methods, primarily relying on electroencephalograms (EEGs), often result in cumbersome and non-portable solutions, making continuous patient monitoring challenging. The video-based seizure detection system is expected to free patients from the constraints of scalp or implanted EEG devices and enable remote monitoring in residential settings. Previous video-based methods neither enable all-day monitoring nor provide short detection latency due to insufficient resources and ineffective patient action recognition techniques. Additionally, skeleton-based action recognition approaches remain limitations in identifying subtle seizure-related actions. To address these challenges, we propose a novel skeleton-based spatiotemporal vision graph neural network (STViG) for efficient, accurate, and timely REal-time Automated Detection of epileptic Seizures from surveillance Videos (READS-V). Our experimental results indicate STViG outperforms previous state-of-the-art action recognition models on our collected patients' video data with higher accuracy (5.9% error) and lower FLOPs (0.4G). Furthermore, by integrating a decision-making rule that combines output probabilities and an accumulative function, our READS-V system achieves a 5.1 s EEG onset detection latency, a 13.1 s advance in clinical onset detection, and zero false detection rate.Comment: 12 pages, 8 figures, 8 table