Decoding the neural signatures of valence and arousal from portable EEG headset

Abstract

Emotion classification using electroencephalography (EEG) data and machine learning techniques have been on the rise in the recent past. However, past studies use data from medical-grade EEG setups with long set-up times and environment constraints. This paper focuses on classifying emotions on the valence-arousal plane using various feature extraction, feature selection, and machine learning techniques. We evaluate different feature extraction and selection techniques and propose the optimal set of features and electrodes for emotion recognition. The images from the OASIS image dataset were used to elicit valence and arousal emotions, and the EEG data was recorded using the Emotiv Epoc X mobile EEG headset. The analysis is carried out on publicly available datasets: DEAP and DREAMER for benchmarking. We propose a novel feature ranking technique and incremental learning approach to analyze performance dependence on the number of participants. Leave-one-subject-out cross-validation was carried out to identify subject bias in emotion elicitation patterns. The importance of different electrode locations was calculated, which could be used for designing a headset for emotion recognition. The collected dataset and pipeline are also published. Our study achieved a root mean square score (RMSE) of 0.905 on DREAMER, 1.902 on DEAP, and 2.728 on our dataset for valence label and a score of 0.749 on DREAMER, 1.769 on DEAP, and 2.3 on our proposed dataset for arousal label.

Estimating Systemic Cognitive States from a Mixture of Physiological and Brain Signals

“…Following the seminal work in Wang, Nie, and Lu (2014b), Garg et al. (2021), Zheng, Zhu, Peng, and Lu (2014), Zheng and Lu (2015), we decide to use a periodogram having a 1 s nonoverlapping rectangular window to estimate the PSD using the MATLAB Signal Processing Toolbox. Particularly, the periodogram PSD estimator produces the average spectral power over each frequency via discrete Fourier transform (Al‐Nafjan et al, 2017; Hamzah et al, 2016).…”

Emotion Classification Based on CWT of ECG and GSR Signals Using Various CNN Models

“…They plotted scalograms and converted them into images, used the pre-trained GoogLeNet deep learning model to classify emotions, and achieved 93.31% accuracy [36]. Garg et al classified emotions based on EEG data using the SelectKBest feature selection technique and traditional machine learning techniques [37]. Alsubai et.al.classified emotions using a deep normalized attention-based neural network for feature extraction using EEG data [38].…”