Afrah Ramin, Amini Zahra, Kafieh Rahele
School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
Medical Image and Signal Processing Research Center, School of Advanced Technologies in Medicine, Isfahan University of Medical Sciences, Isfahan, Iran.
J Biomed Phys Eng. 2024 Dec 1;14(6):579-592. doi: 10.31661/jbpe.v0i0.2207-1521. eCollection 2024 Dec.
The P300 signal, an endogenous component of event-related potentials, is extracted from an electroencephalography signal and employed in Brain-computer Interface (BCI) devices.
The current study aimed to address challenges in extracting useful features from P300 components and detecting P300 through a hybrid unsupervised manner based on Convolutional Neural Network (CNN) and Long Short-term Memory (LSTM).
In this cross-sectional study, CNN as a useful method for the P300 classification task emphasizes spatial characteristics of data. However, CNN and LSTM networks are combined to modify the classification system by extracting both spatial and temporal features. Then, the CNN-LSTM network was trained in an unsupervised learning method based on an autoencoder to improve Signal-to-noise Ratio (SNR) by extracting main components from latent space. To deal with imbalanced data, an Adaptive Synthetic Sampling Approach (ADASYN) is used and augmented without any duplication.
The trained model, tested on the BCI competition III dataset, including two normal subjects, with an accuracy of 95% and 94% for subjects A and B in P300 detection, respectively.
CNN-LSTM, was embedded into an autoencoder and introduced to simultaneously extract spatial and temporal features and manage the computational complexity of the method. Further, ADASYN as an augmentation method was proposed to deal with the imbalanced nature of data, which not only maintained feature space as before but also preserved anatomical features of P300. High-quality results highlight the suitable efficiency of the proposed method.
P300信号是事件相关电位的内源性成分，从脑电图信号中提取并应用于脑机接口（BCI）设备。
本研究旨在解决从P300成分中提取有用特征以及通过基于卷积神经网络（CNN）和长短期记忆网络（LSTM）的混合无监督方式检测P300的挑战。
在这项横断面研究中，CNN作为一种用于P300分类任务的有用方法，强调数据的空间特征。然而，将CNN和LSTM网络相结合，通过提取空间和时间特征来改进分类系统。然后，基于自动编码器，以无监督学习方法训练CNN-LSTM网络，通过从潜在空间中提取主要成分来提高信噪比（SNR）。为处理不平衡数据，使用自适应合成采样方法（ADASYN）进行扩充且无任何重复。
在BCI竞赛III数据集上对训练好的模型进行测试，该数据集包括两名正常受试者，受试者A和受试者B在P300检测中的准确率分别为95%和94%。
将CNN-LSTM嵌入自动编码器，并引入该模型以同时提取空间和时间特征并管理该方法的计算复杂性。此外，提出ADASYN作为一种扩充方法来处理数据的不平衡特性，其不仅如以前一样保持特征空间，还保留了P300的解剖特征。高质量结果突出了所提方法的适宜效率。
