MindCeive:利用 CNN-GRU 和 GAN 感知人类的想象力

IF 4.9 2区 医学 Q1 ENGINEERING, BIOMEDICAL Biomedical Signal Processing and Control Pub Date : 2024-11-01 DOI:10.1016/j.bspc.2024.107110
Ritik Naik, Kunal Chaudhari, Ketaki Jadhav, Amit Joshi
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引用次数: 0

摘要

在脑机接口的帮助下,神经科学在了解人类大脑方面取得了惊人的进步。最近,不同研究人员在人工智能领域做出的贡献使得通过解码大脑信号来感知人类的想象力成为可能。生成人类感知到的视觉刺激将有助于分析人脑是如何工作的以及在不同的感知体验中是如何表现的。脑电图、脑磁图、功能磁共振成像等不同技术被用来捕捉大脑信号。脑电信号无创、成本低、时间分辨率高,因此是首选。机器学习模型用于从这些信号中提取重要特征。然后,生成对抗网络利用这些提取的特征生成代表人类想象力的图像。这项工作使用脑电信号生成逼真的图像。从脑电信号中提取重要特征的任务是通过基于卷积神经网络和门控递归单元的特征提取器来完成的。与现有模型相比,所提出的特征提取器的分类准确性更高。通过将这些提取的特征与所提出的生成对抗网络新架构相结合,可以生成人类所想象物体的逼真图像。所提出的 MindCeive 方法在分类准确率、起始得分和类别多样性得分等各种性能指标上都有所改进,因而优于之前的研究成果。
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MindCeive: Perceiving human imagination using CNN-GRU and GANs
Neuroscience has made astonishing advancements in understanding the human brain with the help of Brain-Computer Interface. Recent contributions in the field of Artificial Intelligence by different researchers made it possible to perceive human imagination by decoding brain signals. Generating visual stimuli perceived by humans will help in analyzing how the human brain works and behaves to different perceptual experiences. Different techniques like Electroencephalography, Magnetoencephalography, functional Magnetic Resonance Imaging, etc. are used to capture brain signals. Electroencephalography signals are non-invasive, low cost, and also have high temporal resolution, therefore they are preferred. Machine learning models are used to extract important features from these signals. These extracted features are then used by Generative Adversarial Network to generate images representing human imagination. This work uses Electroencephalography signals to generate realistic images. The task of extracting important features from Electroencephalography signals is achieved using Convolutional Neural Network and Gated Recurrent Unit based feature extractor. The proposed feature extractor accomplishes better classification accuracy than existing models. By using these extracted features in combination with proposed novel architecture of Generative Adversarial Network, realistic images of objects imagined by humans are generated. The proposed MindCeive approach outperforms previous works by showing improvement in various performance metrics such as Classification Accuracy, Inception Score, and Class Diversity Score.
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来源期刊
Biomedical Signal Processing and Control
Biomedical Signal Processing and Control 工程技术-工程:生物医学
CiteScore
9.80
自引率
13.70%
发文量
822
审稿时长
4 months
期刊介绍: Biomedical Signal Processing and Control aims to provide a cross-disciplinary international forum for the interchange of information on research in the measurement and analysis of signals and images in clinical medicine and the biological sciences. Emphasis is placed on contributions dealing with the practical, applications-led research on the use of methods and devices in clinical diagnosis, patient monitoring and management. Biomedical Signal Processing and Control reflects the main areas in which these methods are being used and developed at the interface of both engineering and clinical science. The scope of the journal is defined to include relevant review papers, technical notes, short communications and letters. Tutorial papers and special issues will also be published.
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