A Fully Integrated 8-Channel Closed-Loop Neural-Prosthetic CMOS SoC for Real-Time Epileptic Seizure Control

Chen, Wei-Ming; Chiueh, Herming; Chen, Tsan-Jieh; Ho, Chia-Lun; Jeng, Chi; Chang, Shun-Ting; Ker, Ming‐Dou; Lin, Chun-Yu; Huang, Ying-Zu; Chou, Cheng‐Chun; Fan, Tsun-Yuan; Cheng, Mu-Huo; Liang, Sheng-Fu; Chien, Tzu-Chieh; Wu, None Sih-Yen; Wang, Yulin; Shaw, Fu-Zen; Huang, Yi; Yang, Chia-Hsiang; Chiou, Jin-Chern; Chang, Chih-Wei; Chou, Lei-Chun; Wu, Chung-Yu

doi:10.1109/jssc.2013.2284346

Cited by 237 publications

(102 citation statements)

References 38 publications

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“…These embedded stimulators would not require any additional invasive risks, and could potentially prevent more drastic treatments such as partial removal of the cortex. An implantable recording and stimulation system can contain a digital signal processor capable of deciding when to stimulate [77]. Other applications of cortical stimulation include closed-loop brain computer interfaces (BCI) which aim to generate functional maps of the brain [78], restore somatosensory feedback [76], restore motor control to tetraplegics [79], aid stroke survivors [80], [81], restore vision [82], reduce pain [73], or even change emotional state [83].…”

Section: Integrated Circuit Interfaces For Stimulationmentioning

confidence: 99%

“…Hence, higher voltage rails of more than AE10 V and/or high-voltage processes are required typically [84]- [86], in turn this results in higher power consumption, larger silicon area, and system complexity to generate and handle high-voltage signals. Instead of maintaining a constant high-voltage power supply, some designs save power by generating a large power rail only when actively stimulating [77], [87], [88].…”

Section: Integrated Circuit Interfaces For Stimulationmentioning

confidence: 99%

“…As one of the treatments for epilepsy, functional neurostimulation in response to detected seizures has been proved effective in reduction of seizures [109], [110]. For real-time closed-loop therapeutics, online automated seizure prediction and/or detection based on ECoG or EEG recordings of epileptic patients is imperative [111]- [114], and their on-chip implementation has been actively investigated and demonstrated utilizing extraction and classification of various signal features such as power spectral densities and wavelet coefficients [47], [77], [115]- [118].…”

Section: E Integrated Electrocortical Online Data Processingmentioning

confidence: 99%

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Silicon-Integrated High-Density Electrocortical Interfaces

Akinin

Park

et al. 2017

Proc. IEEE

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Section: Integrated Circuit Interfaces For Stimulationmentioning

confidence: 99%

Section: Integrated Circuit Interfaces For Stimulationmentioning

confidence: 99%

Section: E Integrated Electrocortical Online Data Processingmentioning

confidence: 99%

See 1 more Smart Citation

Silicon-Integrated High-Density Electrocortical Interfaces

Akinin

Park

et al. 2017

Proc. IEEE

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“…Hence, the solution is not scalable and does not meet the trend of increasing the number of channels for the BMI systems [18]. Another similar work is shown in [19]: an eight-channel closed-loop SoC is developed and tested in vivo to contrast the effect of epileptic seizures. Intracranial EEG (iEEG) signals are acquired with eight energy-efficient AFE and processed by the bio-signal processor, which includes a power-efficient FFT core and an ApEn encoder for feature extraction, and seizures are detected using an Linear Least Squares (LLS) classifier.…”

Section: Related Workmentioning

confidence: 99%

Flexible, Scalable and Energy Efficient Bio-Signals Processing on the PULP Platform: A Case Study on Seizure Detection

Montagna

Benatti

Rossi

2017

JLPEA

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Ultra-low power operation and extreme energy efficiency are strong requirements for a number of high-growth application areas requiring near-sensor processing, including elaboration of biosignals. Parallel near-threshold computing is emerging as an approach to achieve significant improvements in energy efficiency while overcoming the performance degradation typical of low-voltage operations. In this paper, we demonstrate the capabilities of the PULP (Parallel Ultra-Low Power) platform on an algorithm for seizure detection, representative of a wide range of EEG signal processing applications. Starting from the 28-nm FD-SOI (Fully Depleted Silicon On Insulator) technology implementation of the third embodiment of the PULP architecture, we analyze the energy-efficient implementation of the seizure detection algorithm on PULP. The proposed parallel implementation exploits the dynamic voltage and frequency scaling capabilities, as well as the embedded power knobs of the PULP platform, reducing energy consumption for a seizure detection by up to 10× with respect to a sequential implementation at the nominal supply voltage and by 4.2× with respect to a sequential implementation with voltage scaling. Moreover, we analyze the trans-precision optimization of the algorithm on PULP, by means of a hybrid fixed-and floating-point implementation. This approach reduces the energy consumption by up to 43% with respect to the plain fixed-point and floating-point implementations, leveraging the requirements in terms of the precision of the kernels composing the processing chain to improve energy efficiency. Thanks to the proposed architecture and system-level approach for optimization, we demonstrate that PULP reduces energy consumption by up to 140× with respect to commercial low-power microcontrollers, being able to satisfy the real-time constraints typical of bio-medical applications, breaking the barrier of microwatts for a 50-ms complete seizure detection and a few milliwatts for a 5-ms detection latency on a fully-programmable architecture.

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“…An integrated FEA within a small chip area enables neuroscientists and clinicians to simultaneously record and observe larger arrays of neural data, using multiple electrodes and multichannel monitoring systems [5], [6], [7]. Further, acquiring neural activity data via high-density array sensing enables future research in disease and neuro-prosthetic devices [8].…”

Section: Introduction and Current Techniquesmentioning

confidence: 99%