In classifying valence, the best subject-dependent and subject-independent accuracy are 84.44% and 75.00% respectively. In classifying arousal, the best subject-dependent and subject-independent accuracy are 79
Classification performance of calm, fear, happiness and sadness using Evolving Fuzzy Neural Network (EFuNN) classifiers are compared based on subject-dependent and subject-independent validations. It is observed that the proposed technique is able to yield accuracy of above 50% to above 90% for ...
Subject-Independent Brain-Computer Interfaces Based on Deep Convolutional Neural Networks 2020, IEEE Transactions on Neural Networks and Learning Systems Brain-Controlled Robotic Arm System Based on Multi-Directional CNN-BiLSTM Network Using EEG Signals 2020, IEEE Transactions on Neural Systems and Rehabili...
This research introduces a new CNN, i.e., the EEG-ConvNet, to overcome these limitations and challenges. The proposed EEG-ConvNet comprises five convolutional layers with batch normalization and max pooling. In addition, fine-tuning techniques improve the validation of pre-trained models. The ...
(2019) presented 1D and 2D CNN LSTM networks. Raw audio recordings and log-Mel spectrograms were used to investigate how local similarities and global contextual knowledge may be gleaned from them. On the Berlin EmoDB of speaker-dependent and speaker-independent experiments, the 2D CNN LSTM ...
Fig. 1: Movement variability across subjects and trial conditions. Summary of behavior during motor task shows variability within and between subjects that is independent of trial conditions yet correlates with their session performance.aA detailed timeline of epochs shown to subjects on a computer scre...
Touch is a fundamental aspect of social, parental and sexual behavior. In contrast to our detailed knowledge about cortical processing of non-social touch, we still know little about how social touch impacts cortical circuits. We investigated neural acti
How do I damp a membrane subject to a... Learn more about dpe toolbox, driven damped oscillation Partial Differential Equation Toolbox
The distinction between subject-dependent and subject-independent performance is ubiquitous in the human activity recognition (HAR) literature. We assess whether HAR models really do achieve better subject-dependent performance than subject-independent performance, whether a model trained with data from many...
When modeling survival data, it is common to assume that the (log‐transformed) survival time (T) is conditionally independent of the (log‐transformed) censoring time (C) given a set of covariates. There are numerous situations in which this assumption is not realistic, and a number of ...