A $1.8\mu\mathrm{W}\ 5.5$ mm<sup>3</sup> ADC-less Neural Implant SoC utilizing 13.2pJ/Sample Time-domain Bi-phasic Quasi-static Brain Communication with Direct Analog to Time Conversion

Chatterjee, Baibhab; Kumar, K Gaurav; Xiao, Shulan; Barik, Gourab; Jayant, Krishna; Sen, Shreyas

doi:10.1109/esscirc55480.2022.9911420

Cited by 5 publications

(3 citation statements)

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“…Moving forward, efforts need to be focused by the new researchers in this field on refining signal processing techniques, exploring novel approaches to recording neural activity and advancing machine learning algorithms [ 105 , 106 ]. One other direction that the current research is focused on is the collection of signals using distributed implants [ 107 , 108 , 109 , 110 , 111 ], which can provide simultaneous recording from multiple sites scattered throughout the brain. Such technologies hold immense promise in terms of providing more information from various regions which potentially produces correlated neural activity during the generation of speech and handwriting.…”

Section: Discussionmentioning

confidence: 99%

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Sen,

Sheehan,

Raman

et al. 2023

Sensors

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Brain–Computer Interfaces (BCIs) have become increasingly popular in recent years due to their potential applications in diverse fields, ranging from the medical sector (people with motor and/or communication disabilities), cognitive training, gaming, and Augmented Reality/Virtual Reality (AR/VR), among other areas. BCI which can decode and recognize neural signals involved in speech and handwriting has the potential to greatly assist individuals with severe motor impairments in their communication and interaction needs. Innovative and cutting-edge advancements in this field have the potential to develop a highly accessible and interactive communication platform for these people. The purpose of this review paper is to analyze the existing research on handwriting and speech recognition from neural signals. So that the new researchers who are interested in this field can gain thorough knowledge in this research area. The current research on neural signal-based recognition of handwriting and speech has been categorized into two main types: invasive and non-invasive studies. We have examined the latest papers on converting speech-activity-based neural signals and handwriting-activity-based neural signals into text data. The methods of extracting data from the brain have also been discussed in this review. Additionally, this review includes a brief summary of the datasets, preprocessing techniques, and methods used in these studies, which were published between 2014 and 2022. This review aims to provide a comprehensive summary of the methodologies used in the current literature on neural signal-based recognition of handwriting and speech. In essence, this article is intended to serve as a valuable resource for future researchers who wish to investigate neural signal-based machine-learning methods in their work.

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Section: Discussionmentioning

confidence: 99%

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Sen,

Sheehan,

Raman

et al. 2023

Sensors

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“…This process encodes the analog information into the analog pulse width of the signal, which can still be communicated using a digitalfriendly transmitter, since the amplitude of the pulse width-modulated signal remains rail to rail. This method was proposed by Naderiparizi et al (69), while Chatterjee et al (70) reported the first integrated circuit implementation for resource-constrained wireless neural implants, obviating the need for power-hungry ADC and digital compression modules.…”

Section: Adc-less Sensingmentioning

confidence: 99%

Bioelectronic Sensor Nodes for the Internet of Bodies

Chatterjee

Mohseni

Sen

2023

Annu. Rev. Biomed. Eng.

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Energy-efficient sensing with physically secure communication for biosensors on, around, and within the human body is a major area of research for the development of low-cost health care devices, enabling continuous monitoring and/or secure perpetual operation. When used as a network of nodes, these devices form the Internet of Bodies, which poses challenges including stringent resource constraints, simultaneous sensing and communication, and security vulnerabilities. Another major challenge is to find an efficient on-body energy-harvesting method to support the sensing, communication, and security submodules. Due to limitations in the amount of energy harvested, we require a reduction in energy consumed per unit information, making the use of in-sensor analytics and processing imperative. In this article, we review the challenges and opportunities of low-power sensing, processing, and communication with possible powering modalities for future biosensor nodes. Specifically, we analyze, compare, and contrast ( a) different sensing mechanisms such as voltage/current domain versus time domain, ( b) low-power, secure communication modalities including wireless techniques and human body communication, and ( c) different powering techniques for wearable devices and implants. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 25 is June 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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“…2. In [16], [20], and [21], the BCP downlink, BCC downlink, and BCC uplink are incorporated at the base station (BS) and sensor node (SN), but these three paths are enabled in a time-domain multiplexing (TDM) fashion as they share the body channel. Switches dedicated to the path selection are required, where the synchronization and switch control remain a challenge.…”

mentioning

confidence: 99%

Concurrent Body-Coupled Powering and Communication ICs With a Single Electrode

Li,

Dong,

Lin

et al. 2024

IEEE J. Solid-State Circuits

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Body-coupled powering (BCP) and body-coupled communication (BCC) utilize the human body channel as the wireless transmission medium, which shows less path loss around the body area. However, integrating both BCP and BCC requires multiple electrodes or alternating the uplink and downlink in the time domain, due to signal interferences and backflow between different paths. To address this issue, we propose a base station (BS) IC and a sensor node (SN) IC with BCP and BCC concurrency. At the BS, the adaptive self-interference cancellation (SI-C) structure suppresses the output signals that are coupled at the data receiver, enabling the concurrent uplink data recovery and downlink power delivery. At the SN, the ground domain of the uplink data path is separated from that of the downlink power/data path to suppress leakage between the paths. For regulated power supply in different ground domains, the cross-ground-domain power converter is designed with 89.1% efficiency. The ICs are implemented in a 40-nm 1P8M standard CMOS process, and BCC + BCP concurrent operations are successfully validated.Index Terms-Body-coupled communication (BCC), bodycoupled powering (BCP), power-data co-transfer, power-data concurrency, wireless body-area network (BAN). I. INTRODUCTIONB ODY-AREA network (BAN) empowers the cooperated sensing, recognition, and stimulation of bio-signals distributed around the body area [1]. However, on the one hand, the increasing need for downlink (e.g., for remote control and coordination) and uplink communication (e.g., for multisite sensing) incurs additional power overhead. On the other hand, the need for miniaturized form factor (e.g., for user comfort) further limits the on-chip energy, demanding wireless Manuscript

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A $1.8\mu\mathrm{W}\ 5.5$ mm³ ADC-less Neural Implant SoC utilizing 13.2pJ/Sample Time-domain Bi-phasic Quasi-static Brain Communication with Direct Analog to Time Conversion

Cited by 5 publications

References 5 publications

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Bioelectronic Sensor Nodes for the Internet of Bodies

Concurrent Body-Coupled Powering and Communication ICs With a Single Electrode

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A $1.8\mu\mathrm{W}\ 5.5$ mm3 ADC-less Neural Implant SoC utilizing 13.2pJ/Sample Time-domain Bi-phasic Quasi-static Brain Communication with Direct Analog to Time Conversion

Cited by 5 publications

References 5 publications

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Machine-Learning Methods for Speech and Handwriting Detection Using Neural Signals: A Review

Bioelectronic Sensor Nodes for the Internet of Bodies

Concurrent Body-Coupled Powering and Communication ICs With a Single Electrode

Contact Info

Product

Resources

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A $1.8\mu\mathrm{W}\ 5.5$ mm³ ADC-less Neural Implant SoC utilizing 13.2pJ/Sample Time-domain Bi-phasic Quasi-static Brain Communication with Direct Analog to Time Conversion