arXiv:2210.00864
quEEGNet: Quantum AI for Biosignal Processing
원문 초록
In this paper, we introduce an emerging quantum machine learning (QML) framework to assist classical deep learning methods for biosignal processing applications. Specifically, we propose a hybrid quantum-classical neural network model that integrates a variational quantum circuit (VQC) into a deep neural network (DNN) for electroencephalogram (EEG), electromyogram (EMG), and electrocorticogram (ECoG) analysis. We demonstrate that the proposed quantum neural network (QNN) achieves state-of-the-art performance while the number of trainable parameters is kept small for VQC.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Dataset combining EEG, eye-tracking, and high-speed video for ocular activity analysis across BCI paradigms
A Statistical Approach for Synthetic EEG Data Generation
Decoding Covert Speech from EEG Using a Functional Areas Spatio-Temporal Transformer
EEG-CLIP : Learning EEG representations from natural language descriptions
Spatial Distillation based Distribution Alignment (SDDA) for Cross-Headset EEG Classification
Toward Foundational Model for Sleep Analysis Using a Multimodal Hybrid Self-Supervised Learning Framework
토론
이 논문과 연결된 포럼 글이 아직 없습니다.