22.7 A Programmable Wireless EEG Monitoring SoC with Open/Closed-Loop Optogenetic and Electrical Stimulation for Epilepsy Control

Lee, Shuenn-Yuh; Tsou, Chieh; Huang, Peng-Wei; Cheng, Po-Hao; Liao, Chi-Chung; Liao, Zhan-Xien; Lee, Hao-Yun; Lin, Chou-Ching K.; Hsieh, Chia-Hsiang

doi:10.1109/isscc.2019.8662385

Cited by 18 publications

(5 citation statements)

References 2 publications

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“…In the experiment, the delay time of the feature extraction is close to 0.16 s, and the time from epilepsy attack to detection is approximately 0.5 s. Fig. 9 shows the proposed RF transceiver for wireless communication [42]. The transmitter is composed of a bias-stimulating circuit (BSC), a current-reused self-mixing voltage control oscillator (CRSMVCO), and a quadrupletransconductance power amplifier (QTPA) with a matching circuit on the output stage.…”

Section: B Digital Circuitsmentioning

confidence: 91%

A Programmable EEG Monitoring SoC with Optical and Electrical Stimulation for Epilepsy Control

et al. 2020

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In this paper, a cross-domain integration system composed of a gene transfer technique with a pre-clinical trial, an on-chip circuit design, an off-chip hardware with peripheral unit integration, and a custom software is presented. Opsin protein-gene transfer is successfully conducted to demonstrate that gene transfer treatment with optical stimulation has several benefits compared with electrical treatment. The proposed wireless programmable stimulating system on chip (WPSSoC) is composed of two sensing channels combined with a decimation filter to acquire intracranial electroencephalography (iEEG), an epilepsy detection unit (EDU), a stimulator with a waveform controller, and an ultra-low-power embedded transceiver. The equivalent sample rate of sensed data is 375 samples/s with 16-bit output data. The performance of intermodulation distortion with a third order (IMD 3) is approximately 68 dB. The EDU extracts approximate entropy (ApEn) and spectrum bins for the classifier. The stimulator with controller features on the waveform-adjustable function to provide 0-510 µA of electrical stimulation and 0-50 mW of optical stimulation. The proposed transceiver is implemented with a 2.45 GHz on-off keying (OOK) carrier frequency. Transmitter and receiver front-ends perform at 50.6 and 12.7 pJ/bit of energy per bit, respectively. Power consumption and area of WPSSoC are about 1 mW and 13.67 mm 2 in a 0.18 µm process, respectively. Circuits of each part are integrated in a 4.3 cm × 2 cm printed circuit board. The shrunk device is verified with Thy1-ChR2-YFP gene transfer in C57BL/6 mice by using a custom software which is used to provide commands and monitor iEEG. INDEX TERMS ChR2 gene transferring, electrical stimulation, epilepsy, implantable device, in vivo testing, open-/closed-loop control, optical stimulation, system-on-chip, wireless monitoring and control.

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Section: B Digital Circuitsmentioning

confidence: 91%

A Programmable EEG Monitoring SoC with Optical and Electrical Stimulation for Epilepsy Control

et al. 2020

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“…e common data communication schemes are implemented using di erent shi keying (amplitude, phase, on-o ) [8][9][10][11][12]. ey are the most power-e cient solutions for implants; however, their Bit Rates (BR), i.e.…”

Section: Communication Powermentioning

confidence: 99%

“…Therefore, there has been significant interest in lowering the communication energy per bit to keep the implant within heat flux limits. The most common data communication schemes for WI-BMIs are implemented using different shift keying (amplitude, phase, on-off) [14][15][16][17][18]. They are the most power-efficient solutions for implants; however, their BRs are below 20 Mbps.…”

Section: Communication Powermentioning

confidence: 99%

Hardware-Efficient Compression of Neural Multi-Unit Activity

Savolainen

Zhang

Feng

et al. 2022

Preprint

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Objective: Recent advances in intracortical brain machine interfaces (iBMIs) have demonstrated the feasibility of using our thoughts; by sensing and decoding neural activity, for communication and cursor control tasks. It is essential that any invasive device is completely wireless so as to remove percutaneous connections and the associated infection risks. However, wireless communication consumes significant power and there are strict heating limits in cortical tissue. Most iBMIs use Multi Unit Activity (MUA) processing, however the required bandwidth can be excessive for large channel counts in mm or sub-mm scale implants. As such, some form of data compression for MUA iBMIs is desirable. Approach: We used a Machine Learning approach to select static Huffman encoders that worked together, and investigated a broad range of resulting compression systems. They were implemented in reconfigurable hardware and their power consumption, resource utilization and compression performance measured. Main Results: Our design results identified a specific system that provided top performance. We tested it on data from 3 datasets, and found that, with less than 1% behavioural decoding performance reduction from peak, the communication bandwidth was reduced from 1 kb/s/channel to approximately 27 bits/s/channel, using only a Look-Up Table and a 50 ms temporal resolution for threshold crossings. Relative to raw broadband data, this is a compression ratio of 1700-15,000 × and is over an order of magnitude higher than has achieved before. Assuming 20 nJ per communicated bit, the total compression and communication power was between 1.37 and 1.52 μW/channel, occupying 246 logic cells and 4 kbit RAM supporting up to 512-channels. Significance: We show that MUA data can be significantly compressed in a hardware efficient manner, `out of the box' with no calibration necessary. This can significantly reduce on-implant power consumption and enable much larger channel counts in WI-BMIs. All results, code and hardware designs have been made publicly available.

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“…Since the motor function for chronic studies (> 2 months) is the target, the stability of the device during free movement, its reliability and usability for extended testing of the animals are essential. Although the optogenetic interfaces in [18], [41] show powerful recording functions, the recording electrodes need to be percutaneously connected to an external system to provide the power supply and data telemetry. The design challenges of size constraints to the power supply and data telemetry modules were not fully addressed.…”

Section: Comparisonmentioning

confidence: 99%

A Fully Implantable Opto-Electro Closed-Loop Neural Interface for Motor Neuron Disease Studies

Liu

Almarri

et al. 2022

IEEE Trans. Biomed. Circuits Syst.

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This paper presents a fully implantable closed-loop device for use in freely moving rodents to investigate new treatments for motor neuron disease. The 0.18 µm CMOS integrated circuit comprises 4 stimulators, each featuring 16 channels for optical and electrical stimulation using arbitrary current waveforms at frequencies from 1.5 Hz to 50 kHz, and a bandwidth programmable front-end for neural recording. The implant uses a Qi wireless inductive link which can deliver >100 mW power at a maximum distance of 2 cm for a freely moving rodent. A backup rechargeable battery can support 10 mA continuous stimulation currents for 2.5 hours in the absence of an inductive power link. The implant is controlled by a graphic user interface with broad programmable parameters via a Bluetooth low energy bidirectional data telemetry link. The encapsulated implant is 40 mm × 20 mm × 10 mm. Measured results are presented showing the electrical performance of the electronics and the packaging method.

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22.7 A Programmable Wireless EEG Monitoring SoC with Open/Closed-Loop Optogenetic and Electrical Stimulation for Epilepsy Control

Cited by 18 publications

References 2 publications

A Programmable EEG Monitoring SoC with Optical and Electrical Stimulation for Epilepsy Control

A Programmable EEG Monitoring SoC with Optical and Electrical Stimulation for Epilepsy Control

Hardware-Efficient Compression of Neural Multi-Unit Activity

A Fully Implantable Opto-Electro Closed-Loop Neural Interface for Motor Neuron Disease Studies

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