A 1.15μW 5.54mm<sup>3</sup> Implant with a Bidirectional Neural Sensor and Stimulator SoC utilizing Bi-Phasic Quasi-static Brain Communication achieving 6kbps-10Mbps Uplink with Compressive Sensing and RO-PUF based Collision Avoidance

Chatterjee, Baibhab; K, Gaurav Kumar; Nath, Mayukh; Xiao, Shulan; Modak, Nirmoy; Das, Debayan; Jayant, Krishna; Sen, Shreyas

doi:10.23919/vlsicircuits52068.2021.9492445

Cited by 16 publications

(16 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…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

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%

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

Sen,

Sheehan,

Raman

et al. 2023

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…In Table I the presented impulse-based galvanic coupled BCC is benchmarked with stateof-the-art high-speed (>200 Mbps) and miniature implantable transmitters (Tx), including inductive coupling, optical and Impulse-radio UWB (IR-UWB). The proposed GC-BCC demonstrates a data rate up to 250 Mbps with a small electronic area of 26 mm 2 , while most state-of-the-art GC-BCC communication [34], [35], [49] has limited bandwidth up to 10's MHz. Inductive coupled communication can achieve a higher data rate with a "de-Q" technique [23], but it suffers from high path loss and still requires a relatively large coil area.…”

Section: Discussionmentioning

confidence: 99%

Galvanic-Coupled Trans-Dural Data Transfer for High-Bandwidth Intracortical Neural Sensing

Shi

Song

Gao

et al. 2022

IEEE Trans. Microwave Theory Techn.

View full text Add to dashboard Cite

A digital-impulse galvanic coupling as a new high-speed trans-dural (from cortex to the skull) data transmission method has been presented in this paper. The proposed wireless telemetry replaces the tethered wires connected in between implants on the cortex and above the skull, allowing the brain implant to be "free-floating" for minimizing brain tissue damage. Such trans-dural wireless telemetry must have a wide channel bandwidth for high-speed data transfer and a small form factor for minimum invasiveness. To investigate the propagation property of the channel, a finite element model is developed and a channel characterization based on a liquid phantom and porcine tissue is performed. The results show that the trans-dural channel has a wide frequency response of up to 250 MHz. Propagation loss due to micro-motion and misalignments is also investigated in this work. The result indicates that the proposed transmission method is relatively insensitive to misalignment. It has approximately 1 dB extra loss when there is a horizontal misalignment of 1mm. A pulse-based transmitter ASIC and a miniature PCB module are designed and validated ex-vivo with a 10-mm thick porcine tissue. This work demonstrates a high-speed and miniature in-body galvanic-coupled pulse-based communication with a data rate up to 250 Mbps with an energy efficiency of 2 pJ/bit, and has a small module area of only 26 mm 2 .

show abstract

“…2d, part of the electric fields going from the signal electrode to the reference electrode in the TX is received at the RX electrodes. However, the human tissue (brain, in this scenario) presents itself as a low-resistance load (~1 kΩ or less) between the signal and reference electrodes of the TX 44 . If the TX signal is not DC balanced (which is usually the case for traditional G-HBC for wearable devices), this will result in a significant amount of DC power for the implant.…”

Section: Pros and Cons Of Galvanic Eqs Hbc (G-hbc) For Brain Implantsmentioning

confidence: 99%

Biphasic quasistatic brain communication for energy-efficient wireless neural implants

Chatterjee,

Nath,

Kumar K

et al. 2023

Nat Electron

Self Cite

View full text Add to dashboard Cite

Wireless communication using electro-magnetic (EM) fields acts as the backbone for information exchange among wearable devices around the human body. However, for Implanted devices, EM fields incur high amount of absorption in the tissue, while alternative modes of transmission including ultrasound, optical and magnetoelectric methods result in large amount of transduction losses due to conversion of one form of energy to another, thereby increasing the overall end-to-end energy loss. To solve the challenge of wireless powering and communication in a brain implant with low end-end channel loss, we present Bi-Phasic Quasistatic Brain Communication (BP-QBC), achieving < 60dB worst-case end-to-end channel loss at a channel length of ~55mm, by using Electro-quasistatic (EQS) Signaling that avoids transduction losses due to no field-modality conversion. BP-QBC utilizes dipole coupling based signal transmission within the brain tissue using differential excitation in the transmitter (TX) and differential signal pick-up at the receiver (RX), while offering ~41X lower power w.r.t. traditional Galvanic Human Body Communication (G-HBC) at a carrier frequency of 1MHz, by blocking any DC current paths through the brain tissue. Since the electrical signal transfer through the human tissue is electroquasistatic up to several 10's of MHz range, BP-QBC allows a scalable (bps-10Mbps) duty-cycled uplink (UL) from the implant to an external wearable. The power consumption in the BP-QBC TX is only 0.52 μW at 1Mbps (with 1% duty cycling), which is within the range of harvested power in the downlink (DL) from a wearable hub to an implant through the EQS brain channel, with externally applied electric currents < 1/5th of ICNIRP safety limits. Furthermore, BP-QBC eliminates the need for sub-cranial interrogators/repeaters, as it offers better signal strength due to no field transduction. Such low end-to-end channel loss with high data rates enabled by a completely new modality of brain communication and powering has deep societal and scientific impact in the fields of neurobiological research, brain-machine interfaces, electroceuticals and connected healthcare. Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

show abstract

A 1.15μW 5.54mm³ Implant with a Bidirectional Neural Sensor and Stimulator SoC utilizing Bi-Phasic Quasi-static Brain Communication achieving 6kbps-10Mbps Uplink with Compressive Sensing and RO-PUF based Collision Avoidance

Cited by 16 publications

References 0 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

Galvanic-Coupled Trans-Dural Data Transfer for High-Bandwidth Intracortical Neural Sensing

Biphasic quasistatic brain communication for energy-efficient wireless neural implants

Contact Info

Product

Resources

About

A 1.15μW 5.54mm3 Implant with a Bidirectional Neural Sensor and Stimulator SoC utilizing Bi-Phasic Quasi-static Brain Communication achieving 6kbps-10Mbps Uplink with Compressive Sensing and RO-PUF based Collision Avoidance

Cited by 16 publications

References 0 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

Galvanic-Coupled Trans-Dural Data Transfer for High-Bandwidth Intracortical Neural Sensing

Biphasic quasistatic brain communication for energy-efficient wireless neural implants

Contact Info

Product

Resources

About

A 1.15μW 5.54mm³ Implant with a Bidirectional Neural Sensor and Stimulator SoC utilizing Bi-Phasic Quasi-static Brain Communication achieving 6kbps-10Mbps Uplink with Compressive Sensing and RO-PUF based Collision Avoidance