迈向无校准的运动图像大脑计算机接口:基于VGG的卷积神经网络和WGAN方法
A G Habashi1, Ahmed M Azab2, Seif Eldawlatly1,3
1Computer and Systems Engineering Department, Faculty of Engineering, Ain Shams University, Cairo, Egypt.
Journal of neural engineering
|July 19, 2024
概括
这项研究介绍了一种新的,无校准的大脑计算机接口 (BCI) 方法,使用深度学习和数据增强来执行运动图像 (MI) 任务. 该方法提高了跨学科分类的准确性,而不需要学科特定的培训数据.
科学领域:
- 神经科学是一个神经科学.
- 机器学习 机器学习
- 生物医学工程 生物医学工程
背景情况:
- 运动图像 (MI) 是使用电脑图 (EEG) 信号的关键脑电脑接口 (BCI) 范式.
- 脑电图的学科间变异性需要对MI BCI培训的学科依赖数据,这阻碍了广泛采用.
- 目前的MI BCI面临着校准挑战,因为需要个性化培训数据.
研究的目的:
- 为了提高跨主题 (CS) MI EEG分类性能.
- 开发适合现实应用的无校准MI BCI方法.
- 通过克服主体间的变异性来提高MIBCI的准确性和稳定性.
主要方法:
- 使用EEG频谱图像进行MI分类.
- 采用深度学习技术,特别是修改后的VGG-CNN分类器.
- 实施了Wasserstein生成对抗网络 (WGAN) 进行合成数据增强,以扩展培训数据集.
- 对基准数据集 (BCI竞争IV-2B,IV-2A,IV-1) 进行了实验,使用离开一个主体的验证.
主要成果:
- 拟议的方法证明了CSMI EEG分类准确度的提高.
- 结合WGAN生成的数据和修改后的VGG-CNN分类器,超越了最先进的方法.
- 在不需要目标学科校准数据的情况下,在跨学科分类中取得了显著的改进.
结论:
- 开发的框架代表了向无校准BCI系统的重大进步.
- 这种方法有可能通过简化其实施来扩大MI BCI的应用.
- 这项研究强调了深度学习和数据增强对于强大的跨主题MI分类的有效性.
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