脑电信号光子处理器——基于衍射光子计算单元的癫痫发作检测

Tao Yan; Maoqi Zhang; Hang Chen; Sen Wan; Kaifeng Shang; Haiou Zhang; Xun Cao; Xing Lin; Qionghai Dai

doi:10.1016/j.eng.2024.01.008

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工程（英文） ›› 2024, Vol. 35 ›› Issue (4) : 56-66. DOI: 10.1016/j.eng.2024.01.008

研究论文

Article

脑电信号光子处理器——基于衍射光子计算单元的癫痫发作检测

Tao Yan ^a^,^c ,
Maoqi Zhang ^b^,^d ,
Hang Chen ^b ,
Sen Wan ^a^,^c^,^e ,
Kaifeng Shang ^a^,^c ,
Haiou Zhang ^b ,
Xun Cao ^d ,
Xing Lin ^b^,^c^,^e^,^* ,
Qionghai Dai ^a^,^c^,^e^,^*

作者信息 +

EEG Opto-Processor: Epileptic Seizure Detection Using Diffractive Photonic Computing Units

Tao Yan ^a^,^c ,
Maoqi Zhang ^b^,^d ,
Hang Chen ^b ,
Sen Wan ^a^,^c^,^e ,
Kaifeng Shang ^a^,^c ,
Haiou Zhang ^b ,
Xun Cao ^d ,
Xing Lin ^b^,^c^,^e^,^* ,
Qionghai Dai ^a^,^c^,^e^,^*

Author information +

History +

Highlight

・An EEG opto-processor is proposed to construct photonic neural networks that process analog EEG signals for epileptic seizure detections at high energy efficiency.

・Both free-space and integrated diffractive photonic computing units are designed to process extracranial and intracranial EEG signals efficiently and detect epileptic seizures.

・A channel selection mechanism is used to promote and validate the performance of the opto-processor for supervising clinical diagnosis.

Abstract

Electroencephalography (EEG) analysis extracts critical information from brain signals, enabling brain disease diagnosis and providing fundamental support for brain-computer interfaces. However, performing an artificial intelligence analysis of EEG signals with high energy efficiency poses significant challenges for electronic processors on edge computing devices, especially with large neural network models. Herein, we propose an EEG opto-processor based on diffractive photonic computing units (DPUs) to process extracranial and intracranial EEG signals effectively and to detect epileptic seizures. The signals of the EEG channels within a second-time window are optically encoded as inputs to the constructed diffractive neural networks for classification, which monitors the brain state to identify symptoms of an epileptic seizure. We developed both free-space and integrated DPUs as edge computing systems and demonstrated their applications for real-time epileptic seizure detection using benchmark datasets, that is, the Children’s Hospital Boston (CHB)-Massachusetts Institute of Technology (MIT) extracranial and Epilepsy-iEEG-Multicenter intracranial EEG datasets, with excellent computing performance results. Along with the channel selection mechanism, both numerical evaluations and experimental results validated the sufficiently high classification accuracies of the proposed opto-processors for supervising clinical diagnosis. Our study opens a new research direction for utilizing photonic computing techniques to process large-scale EEG signals and promote broader applications.

Keywords

Epileptic seizure detection / EEG analysis / Diffractive photonic computing unit / Photonic computing

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Tao Yan, Maoqi Zhang, Hang Chen. . Engineering. 2024, 35(4): 56-66 https://doi.org/10.1016/j.eng.2024.01.008

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