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Neural signal recording and optical stimulation using implantable devices have become a ubiquitous method to treat brain disorders, yet there lie some shortcomings, such as size, weight, and functionalities of the implants. This work presents a commercial off-the-shelf (COTS) component-based miniaturized wireless optogenetic headstage with simultaneous optical stimulation and electrophysiological recording for freely moving rats. The system includes a battery-based neural stimulator consisting of a low-dropout (LDO) regulator, an oscillator, and a μ LED. The electrophysiological signal recording system includes an intracortical neural probe implemented on a shape memory polymer (SMP) substrate, an array of neural amplifiers with an integrated analog-to-digital converter (ADC), a transceiver IC, and a ceramic antenna. A digital sub-1-GHz transceiver integrated with a low-power microcontroller (MCU) is used to transmit the acquired neural data to a remote receiver unit, followed by offline spike detection and sorting in LabVIEW. The front-end recording amplifiers provide a gain of 45.7 dB with the input-referred noise of 2.4μVrms . The integrated multiplexer (MUX) with the ADC allows sampling of the amplified voltage at a configurable sampling rate of 160–480 kSamples/s. The total power consumption of the stimulation and the recording system is 23 mW. The dimension of the headstage device is 13.5×21.3 mm, weighing 4 g without the battery. The system is experimentally validated in an in vivo setting by placing the headstage on the head of a male rat and recording the neural signals from the ventral tegmental area (VTA) of the brain. This integrative neural signal recording and spike sorting approach would be useful for the development of a closed-loop neuromodulation system. 

Egocentric temporal action segmentation in videos is a crucial task in computer vision with applications in various fields such as mixed reality, human behavior analysis, and robotics. Although recent research has utilized advanced visual-language frameworks, transformers remain the backbone of action segmentation models. Therefore, it is necessary to improve transformers to enhance the robustness of action segmentation models. In this work, we propose two novel ideas to enhance the state-of-the-art transformer for action segmentation. First, we introduce a dual dilated attention mechanism to adaptively capture hierarchical representations in both local-to-global and global-to-local contexts. Second, we incorporate cross-connections between the encoder and decoder blocks to prevent the loss of local context by the decoder. We also utilize state-of-the-art visual-language representation learning techniques to extract richer and more compact features for our transformer. Our proposed approach outperforms other state-of-the-art methods on the Georgia Tech Egocentric Activities (GTEA) and HOI4D Office Tools datasets, and we validate our introduced components with ablation studies. The source code and supplementary materials are publicly available on https://www.sail-nu.com/dxformer. 

The next evolutionary step in biological signal monitoring will be enabled by wireless communication. Low power and cost-efficient wireless transceivers are currently being employed for implantable medical devices (IMDs), in addition to military and civilian applications such as monitoring, surveillance, and home automation. The major goal of this paper is to do a thorough and realistic link budget analysis for an implantable wireless transceiver operating in the 3–5 GHz ultrawideband frequency with a link distance of 2 m (which includes 10 mm of brain tissue layer and 1.99 m of air medium), data rate of 100 Mbps with On-Off keying (OOK) modulation, and a minimum receiver sensitivity of −58.01 dBm. The proposed power budget analysis is particularly well suited for distributed brain implant applications as it models the path loss including the tissue layer without compromising the spectrum regulation imposed by the Federal Communications Commission (FCC) for UWB communication.