Improved EEG-based emotion recognition through information enhancement in connectivity feature map

Abstract

Electroencephalography (EEG), despite its inherited complexity, is a preferable brain signal for automatic human emotion recognition (ER), which is a challenging machine learning task with emerging applications. In any automatic ER, machine learning (ML) models classify emotions using the extracted features from the EEG signals, and therefore, such feature extraction is a crucial part of ER process. Recently, EEG channel connectivity features have been widely used in ER, where Pearson correlation coefficient (PCC), mutual information (MI), phase-locking value (PLV), and transfer entropy (TE) are well-known methods for connectivity feature map (CFM) construction. CFMs are typically formed in a two-dimensional configuration using the signals from two EEG channels, and such two-dimensional CFMs are usually symmetric and hold redundant information. This study proposes the construction of a more informative CFM that can lead to better ER. Specifically, the proposed innovative technique intelligently combines CFMs’ measures of two different individual methods, and its outcomes are more informative as a fused CFM. Such CFM fusion does not incur additional computational costs in training the ML model. In this study, fused CFMs are constructed by combining every pair of methods from PCC, PLV, MI, and TE; and the resulting fused CFMs PCC + PLV, PCC + MI, PCC + TE, PLV + MI, PLV + TE, and MI + TE are used to classify emotion by convolutional neural network. Rigorous experiments on the DEAP benchmark EEG dataset show that the proposed CFMs deliver better ER performances than CFM with a single connectivity method (e.g., PCC). At a glance, PLV + MI-based ER is shown to be the most promising one as it outperforms the other methods.

Epileptic Seizure Detection from Decomposed EEG Signal through 1D and 2D Feature Representation and Convolutional Neural Network

“…CNN is a well-studied DL model to classify image or image-like 2D representations of features ( [7,8]) due to its unique ability to process and analyze 2D structured data. Convolution operation with a 2D kernel makes CNN (hereafter called 2D CNN) particularly effective in recognizing patterns, shapes, and structures in images or 2D input.…”

“…Epilepsy has been a subject of extensive research in the computational intelligence domain over the last few decades ( [2][3][4][5][6]) for automated ES detection and diagnosis. In recent years, machine learning (ML) and deep learning (DL) techniques have emerged as powerful tools for analyzing EEG for the diagnosis of neurological disorders such as autism and emotion [7,8]. EEG signals are the most promising brain signals for ES analysis and recognition, particularly for recognizing the abnormality of the brain due to its painless experiment and inexpensive nature [9].…”

Information-Theoretical Analysis of the Cycle of Creation of Knowledge and Meaning in Brains under Multiple Cognitive Modalities

“…Several challenges in different areas are reviewed in [ 12 ] that included safety and health, for example, via measuring physiological signals such as EEG. Other authors have explored the challenges associated with brain computer interface for emotion recognition [ 13 , 14 ] associated with machine learning and other models [ 15 ], as for example, in planning and rational decision making [ 16 ], something that requires robust classification methods for EEG signals in emotion recognition, as discussed by [ 17 ]. Another relevant field of study concerning various physiological signals concerns the characterization of mental stress and fatigue, as described in studies like [ 18 ] where electrodermal activity is measured to classify calm from distress conditions.…”

EEG-based recognition of happiness

“…EEG-based recognition of happiness holds significant promise for enhancing human-machine interaction and improving well-being. By leveraging advanced signal processing techniques and machine learning algorithms, researchers can extract valuable insights from EEG data and develop practical applications in various domains [6]. Addressing challenges and exploring new research directions will further propel the field of EEG-based emotion recognition towards impactful realworld implementations.…”