Review of EEG Affective Recognition with a Neuroscience Perspective

RY Lim, WCL Lew, KK Ang - Brain Sciences, 2024 - mdpi.com
Emotions are a series of subconscious, fleeting, and sometimes elusive manifestations of
the human innate system. They play crucial roles in everyday life—influencing the way we …

Eeg-based seizure detection using variable-frequency complex demodulation and convolutional neural networks

YR Veeranki, R McNaboe, HF Posada-Quintero - Signals, 2023 - mdpi.com
Epilepsy is a complex neurological disorder characterized by recurrent and unpredictable
seizures that affect millions of people around the world. Early and accurate epilepsy …

Non-Linear Signal Processing Methods for Automatic Emotion Recognition using Electrodermal Activity

YR Veeranki, LRM Diaz, R Swaminathan… - IEEE Sensors …, 2024 - ieeexplore.ieee.org
Detection of emotional states plays a prominent role in affective computing, decision-
making, and healthcare. Physiological signals are an ideal target for continuous and …

Neuronal Correlates of Empathy: A Systematic Review of Event-Related Potentials Studies in Perceptual Tasks

R Almeida, C Prata, MR Pereira, F Barbosa… - Brain Sciences, 2024 - mdpi.com
Empathy is a crucial component to infer and understand others' emotions. However, a
synthesis of studies regarding empathy and its neuronal correlates in perceptual tasks using …

A novel optimized recurrent network-based automatic system for speech emotion identification

N Koppula, KS Rao, SA Nabi, A Balaram - Wireless Personal …, 2023 - Springer
Speech is a unique characteristic of humans that expresses one's emotional viewpoint to
others. Speech emotion recognition (SER) identifies the speaker's emotion from the speech …

Emotion recognition from physiological channels using graph neural network

T Wierciński, M Rock, R Zwierzycki, T Zawadzka… - Sensors, 2022 - mdpi.com
In recent years, a number of new research papers have emerged on the application of
neural networks in affective computing. One of the newest trends observed is the utilization …

Electrodermal activity based emotion recognition using time-frequency methods and machine learning algorithms

Y Rao Veeranki, N Ganapathy… - Current Directions in …, 2021 - degruyter.com
In this work, the feasibility of time-frequency methods, namely short-time Fourier transform,
Choi Williams distribution, and smoothed pseudo-Wigner-Ville distribution in the …

Comparison of Electrodermal Activity Signal Decomposition Techniques for Emotion Recognition

YR Veeranki, N Ganapathy, R Swaminathan… - IEEE …, 2024 - ieeexplore.ieee.org
Emotions play an essential role in human life as they are linked to well-being and markers of
various diseases. Physiological signals can be used to assess emotions objectively and …

Mining inconsistent emotion recognition results with the multidimensional model

A Landowska, T Zawadzka, M Zawadzki - IEEE Access, 2021 - ieeexplore.ieee.org
The paper deals with the challenge of inconsistency in multichannel emotion recognition.
The focus of the paper is to explore factors that might influence the inconsistency. The paper …

Approaches to studying emotion using physiological responses to spoken narratives: A scoping review

MA Savard, R Merlo, A Samithamby, A Paas… - …, 2024 - Wiley Online Library
Narratives are effective tools for evoking emotions, and physiological measurements provide
a means of objectively assessing emotional reactions–making them a potentially powerful …