NURIP: Neural Interface Processor for Brain-State Classification and Programmable-Waveform Neurostimulation
Autor: | Taufik A. Valiante, David M. Groppe, Naveen Verma, Gerard O'Leary, Roman Genov |
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Rok vydání: | 2018 |
Předmět: |
Computer science
medicine.medical_treatment Feature extraction 02 engineering and technology Electroencephalography Intracranial Electroencephalography 03 medical and health sciences Epilepsy 0302 clinical medicine Neuromodulation 0202 electrical engineering electronic engineering information engineering medicine Waveform Electrical and Electronic Engineering Neurostimulation Brain–computer interface Signal processing Artificial neural network medicine.diagnostic_test business.industry Dimensionality reduction 020208 electrical & electronic engineering medicine.disease Autoencoder medicine.anatomical_structure business 030217 neurology & neurosurgery Computer hardware |
Zdroj: | IEEE Journal of Solid-State Circuits. 53:3150-3162 |
ISSN: | 1558-173X 0018-9200 |
Popis: | The advancement of implantable medical devices for the treatment of neurological disorders demands energy-efficient, low-latency processors for responsive, safe, and personalized neuromodulation. A 130-nm CMOS neural interface processor is presented to perform the brain-state classification and closed-loop control using programmable-waveform electrical stimulation. The architecture features an autoencoder neural network for both spatial filtering and dimensionality reduction. Dedicated feature extraction blocks are implemented for univariate (signal-band energy) and multivariate (phase locking value, and cross-frequency coupling) neural signal processing. The proceeding exponentially decaying memory support vector machine (EDM-SVM) accelerator employs these features for hardware-efficient brain-state classification with a high temporal resolution. An integrated digitally charge-balanced waveform generator enables flexibility in finding optimal neuromodulation paradigms for pathological symptom suppression. The system on chip (SoC) is validated using the EU human intracranial electroencephalography epilepsy data set, achieving a seizure sensitivity of 97.7% and a false detection rate of 0.185/h while consuming 169 $\mu \text{J}$ per classification. |
Databáze: | OpenAIRE |
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