An Elegant Experimental Setup for Accurate Presentation of Dichoptic Stimuli

EEG-based investigation of effects of mindfulness meditation training on state and trait by deep learning and traditional machine learning

“…However, the meditation state classification accuracy for our MBSR novices using deep and shallow ConvNets does not improve over the traditional FBCSP + SVM method but instead drops to around chance levels of 48.34 and 50.60%, respectively. Though in this study we use a CNN architecture ConvNet as the deep learning architecture, which is different from the RNN architecture LSTM in Panachakel et al (2021a), the main reason accounting for the failure of ConvNet in the inter-subject classification scenario should be that ConvNet uses an end-to-end training architecture whereas in Panachakel et al (2021a) the LSTM architecture does not work directly on raw EEG data but instead on EEG features extracted by CSP + LDA.…”

“…To overcome this disadvantage of end-to-end ConvNets on inter-subject classification, we may either enlarge the dataset by collecting data from a large number of subjects for better covering broad individual features so as to reduce the problem of individual difference, or try not to use end-to-end learning for the case where only a small number of subjects are available, as demonstrated in Panachakel et al (2021a).…”

“…The classification techniques used for meditation state recognition include linear discriminant analysis (LDA) (Panachakel et al, 2021b), support vector machine (SVM) (Shaw and Routray, 2016;Han et al, 2020), random forest (RF) (Huang et al, 2021), and artificial neural network (ANN). Recently, deep learning techniques including long short-term memory (LSTM) framework (Panachakel et al, 2021a) as well as various deep convolutional neural networks (CNN) such as VGG16, ResNet50, and MobileNet , have also been exploited for meditation state recognition.…”