Task-State EEG Signal Classification for Spatial Cognitive Evaluation Based on Multiscale High-Density Convolutional Neural Network

Abstract

In this study, a multi-scale high-density convolutional neural network (MHCNN) classification method for spatial cognitive ability assessment was proposed, aiming at achieving the binary classification of task-state EEG signals before and after spatial cognitive training. Besides, the multi-dimensional conditional mutual information method was used to extract the frequency band features of the EEG data. And the coupling features under the combination of multi-frequency bands were transformed into multi-spectral images. At the same time, the idea of Densenet was introduced to improve the multi-scale convolutional neural network. Firstly, according to the discreteness of multispectral EEG image features, two-scale convolution kernels were used to calculate and learn useful channel and frequency band feature information in multispectral image data. Secondly, to enhance feature propagation and reduce the number of parameters, the dense network was connected after the multi-scale convolutional network, and the learning rate change function of the stochastic gradient descent algorithm was optimized to objectively evaluate the training effect. The experimental results showed that compared with the classical convolution neural network (CNN) and multi-scale convolution neural network, the proposed MHCNN had better classification Manuscript received November 12, 2021.

A deep learning approach to detect the electroencephalogram-based cognitive task states

“…A multiscale high-density convolutional neural network (MHCNN) has been proposed to classify EEG task states (before and after cognitive training), and the classification results were compared with CNN. The results show that MHCNN outperforms with an accuracy of 98% [62]. A CNNbased automatic sleep stage detection has been done by Cui et al [63].…”

A Detailed Analysis & Parametric Comparison Of Eeg Processing Models

“…In order to categorize task-state EEG data before and after spatial cognitive training, researchers in [10] proposed a multiscale high-density convolutional neural network (MHCNN) classification approach for assessing spatial cognitive abilities. The properties of the EEG frequency band were discovered utilizing multidimensional conditional mutual information.…”

“…The extraction of EEG characteristics may be accomplished via the use of a wide variety of methodologies, such as time-domain, frequencydomain, and time-frequency analyses, in addition to chaotic features [5][6][7]. In addition, a number of research have integrated or reconstructed these approaches in order to get additional characteristics, which has ultimately provided remarkable classification findings [8][9][10]. The accuracy of medical EEG collection equipment has seen significant improvements as a result of the expansion of scientific knowledge and the increased power of technology.…”