An Approach to EEG Based Emotion Recognition and Classification Using Kernel Density Estimation

Lahane, Prashant; Sangaiah, Arun Kumar

doi:10.1016/j.procs.2015.04.138

Cited by 34 publications

(10 citation statements)

References 7 publications

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“…In the machine learning algorithm researchers believe that suitable feature extraction is the key to make an efficient predictive model. There are several types of features based on frequency domain, Time domain and frequency time domain for the EEG signal diagnosis and analysis [23]. Feature Classification-Machine learning is a subject of artificial intelligence.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

“…Using these technologies diagnosis of neuropsychiatric and neurological disorder such as Alzheimer, epilepsy and depressive disorder will be done [31]. Nowadays, researcher has shows extensive interest to make human emotion interaction with machine by proper brain computer interface [23].Literature survey covered recent methods used in physiological signal based emotion recognition; it will help for researcher working in this field. The remaining portion of this paper is organized as follows: Emotion is described in Section II.…”

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confidence: 99%

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EEG based Emotion Recognition and Classification: a Review

Kumawat¹,

Jain²

2021

IRJASH

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Emotion plays a vital role in medical research and interpersonal communication. Essentially feeling can be communicated verbally like discourse or non-verbally like outward appearance and physiological signals. A human emotion is complex physiological state which involves a physiological response, a person's experience and behavioral change. EEG measures electric current that are generated due to neuronal activities in the human brain. This paper provides an overview of comparative study of various techniques of emotion recognition from EEG signals. Our analysis is based on extracted features and classification methods of emotion recognition.We intended that, this study will be useful for newly researchers those entering in the field of emotion recognition.

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Section: Feature Extraction and Classificationmentioning

confidence: 99%

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confidence: 99%

EEG based Emotion Recognition and Classification: a Review

Kumawat¹,

Jain²

2021

IRJASH

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“…Many physiological modalities and features have been evaluated for ER, namely Electroencephalography (EEG) [ 28 , 29 , 30 ], Electrocardiography (ECG) [ 31 , 32 , 33 ], Electrodermal Activity (EDA) [ 34 , 35 , 36 ], Respiration (RESP) [ 26 ], Blood Volume Pulse (BVP) [ 26 , 35 ] and Temperature (TEMP) [ 26 ]. Multi-modal approaches have prevailed; however, there is still no clear evidence of which feature combinations and physiological signals are the most relevant.…”

Section: State Of the Artmentioning

confidence: 99%

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Bota

Wang²,

Fred

et al. 2020

Sensors

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Emotion recognition based on physiological data classification has been a topic of increasingly growing interest for more than a decade. However, there is a lack of systematic analysis in literature regarding the selection of classifiers to use, sensor modalities, features and range of expected accuracy, just to name a few limitations. In this work, we evaluate emotion in terms of low/high arousal and valence classification through Supervised Learning (SL), Decision Fusion (DF) and Feature Fusion (FF) techniques using multimodal physiological data, namely, Electrocardiography (ECG), Electrodermal Activity (EDA), Respiration (RESP), or Blood Volume Pulse (BVP). The main contribution of our work is a systematic study across five public datasets commonly used in the Emotion Recognition (ER) state-of-the-art, namely: (1) Classification performance analysis of ER benchmarking datasets in the arousal/valence space; (2) Summarising the ranges of the classification accuracy reported across the existing literature; (3) Characterising the results for diverse classifiers, sensor modalities and feature set combinations for ER using accuracy and F1-score; (4) Exploration of an extended feature set for each modality; (5) Systematic analysis of multimodal classification in DF and FF approaches. The experimental results showed that FF is the most competitive technique in terms of classification accuracy and computational complexity. We obtain superior or comparable results to those reported in the state-of-the-art for the selected datasets.

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“…Several methods and techniques can be applied to perform emotion recognition through the use of a couple of hardware devices and software such as: analysis of emotional properties based on two physiological data such as, ECG and EEG [3]; unified system for efficient discrimination of positive and negative emotions based on EEG data [4]; automatic recognizer of the facial expression around the eyes and forehead based on Electrooculography (EOG) data giving support to emotion recognition task [5]; use of GSR and ECG data to develop a study to examine the effectiveness of Matching Pursuit (MP) algorithm in emotion recognition, using mainly PCA to reduce the features dimensionality and Probabilistic Neural Network (PNN) as the recognition technique [6]; emotion recognition system based on physiological data using ECG and respiration (RSP) data, recorded simultaneously by a physiological monitoring device based on wearable sensors [7]; emotions recognition using EEG data and also performed an analyze about the impact of positive and negative emotions using SVM and RBF as the recognition methods [8]; new approach to emotion recognition based on EEG and classification method using Artificial Neural Networks (ANN) with features analysis based on Kernel Density Estimation (KDE) [9]; an application that stores several psychophysiological data based on HR, ECG, SpO2 and GSR, that were acquired while the users watched advertisements about smoking campaigns [10]; experiments based on flight simulator to developed a multimodal sensing architecture to recognize emotions using three different techniques for biosignal acquisitions [11]; multimodal sensing system to identify emotions using different acquisition techniques, based on photo presentation methodology [12]; real-time user interface with emotion recognition that depends on the need for skill development to support a change in the interface paradigm to one that is more human centered [13]; recognize emotions through psychophysiological sensing using a multiple-fusion-layer based on ensemble classifier of stacked auto encoder (MESAE) [14].…”

Section: Introductionmentioning

confidence: 99%

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

Roza

Postolache

2019

Sensors

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The present work tries to fill part of the gap regarding the pilots' emotions and their bio-reactions during some flight procedures such as, takeoff, climbing, cruising, descent, initial approach, final approach and landing. A sensing architecture and a set of experiments were developed, associating it to several simulated flights (N f lights = 13) using the Microsoft Flight Simulator Steam Edition (FSX-SE). The approach was carried out with eight beginner users on the flight simulator (N pilots = 8). It is shown that it is possible to recognize emotions from different pilots in flight, combining their present and previous emotions. The cardiac system based on Heart Rate (HR), Galvanic Skin Response (GSR) and Electroencephalography (EEG), were used to extract emotions, as well as the intensities of emotions detected from the pilot face. We also considered five main emotions: happy, sad, angry, surprise and scared. The emotion recognition is based on Artificial Neural Networks and Deep Learning techniques. The Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) were the main methods used to measure the quality of the regression output models. The tests of the produced output models showed that the lowest recognition errors were reached when all data were considered or when the GSR datasets were omitted from the model training. It also showed that the emotion surprised was the easiest to recognize, having a mean RMSE of 0.13 and mean MAE of 0.01; while the emotion sad was the hardest to recognize, having a mean RMSE of 0.82 and mean MAE of 0.08. When we considered only the higher emotion intensities by time, the most matches accuracies were between 55% and 100%.

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An Approach to EEG Based Emotion Recognition and Classification Using Kernel Density Estimation

Cited by 34 publications

References 7 publications

EEG based Emotion Recognition and Classification: a Review

EEG based Emotion Recognition and Classification: a Review

Emotion Assessment Using Feature Fusion and Decision Fusion Classification Based on Physiological Data: Are We There Yet?

Multimodal Approach for Emotion Recognition Based on Simulated Flight Experiments

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