A GRU–CNN model for auditory attention detection using microstate and recurrence quantification analysis

Abstract

Attention as a cognition ability plays a crucial role in perception which helps humans to concentrate on specific objects of the environment while discarding others. In this paper, auditory attention detection (AAD) is investigated using different dynamic features extracted from multichannel electroencephalography (EEG) signals when listeners attend to a target speaker in the presence of a competing talker. To this aim, microstate and recurrence quantification analysis are utilized to extract different types of features that reflect changes in the brain state during cognitive tasks. Then, an optimized feature set is determined by employing the processes of significant feature selection based on classification performance. The classifier model is developed by hybrid sequential learning that employs Gated Recurrent Units (GRU) and Convolutional Neural Network (CNN) into a unified framework for accurate attention detection. The proposed AAD method shows that the selected feature set achieves the most discriminative features for the classification process. Also, it yields the best performance as compared with state-of-the-art AAD approaches from the literature in terms of various measures. The current study is the first to validate the use of microstate and recurrence quantification parameters to differentiate auditory attention using reinforcement learning without access to stimuli.

An Automatic Lie Detection Model Using EEG Signals Based on the Combination of Type 2 Fuzzy Sets and Deep Graph Convolutional Networks

“…However, the highest values of this coefficient for the [ 6 ] and [ 12 ] studies are 96 and 96.45%, respectively. The highest accuracy achieved among the studies is related to [ 14 ], which is around 98%. However, this study uses feature selection/extraction and manual classification.…”

“…The Method Used ACC (%) Abootalebi et al [9] P300 Waves 86 Amir et al [10] Classical Features 80 Mohammad et al [11] Brain Waves 79 Gao et al [12] SVM 96 Simbolon et al [13] ERP 83 Saini et al [14] SVM 98 Yohan et al [15] ANN 86 Bagel et al [16] CNN 84 Dodia et al [17] FFT-Hand Crafted Features 88 Kang et al [4] ICA + FCN 88.5 Boddu et al [6] PSO + SVM 96.45…”

“…The study’s second advantage is the use of minimal signal-recording electrodes. Saini and colleagues [ 14 ] investigated the classification of EEG signals using various features for lie detection. This paper described a novel approach to extracting and integrating domain features with an SVM classifier.…”

Automatic Detection of Acute Leukemia (ALL and AML) Utilizing Customized Deep Graph Convolutional Neural Networks

“…Building models that are capable of data analysis, optimization [22,23], spatial encoding [24], spatial ability [25][26][27], learning content management systems [28,29], prediction, and other tasks is the aim of machine learning [30,31] and its subsets, including federated learning [32][33][34], recurrent neural networks [35], deep learning networks, etc. In this regard, Michael Deferard and his colleagues first introduced the fundamental notion of the graph convolutional network.…”

“…In this regard, Michael Deferard and his colleagues first introduced the fundamental notion of the graph convolutional network. The researchers utilized signal processing techniques in graph spectral theory for the first time [35]. This enabled the development of convolutional functions and the application of convolutional networks in graph theory.…”