Emotion Recognition using EEG and Physiological Data for Robot-Assisted Rehabilitation Systems

2020 ◽  
Author(s):  
Elif Gümüslü ◽  
Duygun Erol Barkana ◽  
Hatice Köse
Keyword(s):  
Emotion Recognition ◽  
Physiological Data ◽  
Robot Assisted

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

Sensors ◽  
2020 ◽  
Vol 20 (17) ◽  
pp. 4723
Author(s):  
Patrícia Bota ◽  
Chen Wang ◽  
Ana Fred ◽  
Hugo Silva
Keyword(s):  
Emotion Recognition ◽  
Classification Accuracy ◽  
Feature Fusion ◽  
State Of The Art ◽  
Electrodermal Activity ◽  
Decision Fusion ◽  
Classification Performance ◽  
Physiological Data ◽  
Systematic Analysis ◽  
Public Datasets

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.

scholarly journals MindLink-Eumpy: An Open-Source Python Toolbox for Multimodal Emotion Recognition

2021 ◽  
Vol 15 ◽  
Author(s):  
Ruixin Li ◽  
Yan Liang ◽  
Xiaojian Liu ◽  
Bingbing Wang ◽  
Wenxin Huang ◽  
...  
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Open Source ◽  
Short Term Memory ◽  
Weight Enumerator ◽  
Physiological Data ◽  
Support Vector ◽  
Decision Level ◽  
Model Based ◽  
The Subject

Emotion recognition plays an important role in intelligent human–computer interaction, but the related research still faces the problems of low accuracy and subject dependence. In this paper, an open-source software toolbox called MindLink-Eumpy is developed to recognize emotions by integrating electroencephalogram (EEG) and facial expression information. MindLink-Eumpy first applies a series of tools to automatically obtain physiological data from subjects and then analyzes the obtained facial expression data and EEG data, respectively, and finally fuses the two different signals at a decision level. In the detection of facial expressions, the algorithm used by MindLink-Eumpy is a multitask convolutional neural network (CNN) based on transfer learning technique. In the detection of EEG, MindLink-Eumpy provides two algorithms, including a subject-dependent model based on support vector machine (SVM) and a subject-independent model based on long short-term memory network (LSTM). In the decision-level fusion, weight enumerator and AdaBoost technique are applied to combine the predictions of SVM and CNN. We conducted two offline experiments on the Database for Emotion Analysis Using Physiological Signals (DEAP) dataset and the Multimodal Database for Affect Recognition and Implicit Tagging (MAHNOB-HCI) dataset, respectively, and conducted an online experiment on 15 healthy subjects. The results show that multimodal methods outperform single-modal methods in both offline and online experiments. In the subject-dependent condition, the multimodal method achieved an accuracy of 71.00% in the valence dimension and an accuracy of 72.14% in the arousal dimension. In the subject-independent condition, the LSTM-based method achieved an accuracy of 78.56% in the valence dimension and an accuracy of 77.22% in the arousal dimension. The feasibility and efficiency of MindLink-Eumpy for emotion recognition is thus demonstrated.

scholarly journals Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data

Sensors ◽  
2021 ◽  
Vol 21 (23) ◽  
pp. 7854
Author(s):  
Luz Santamaria-Granados ◽  
Juan Francisco Mendoza-Moreno ◽  
Angela Chantre-Astaiza ◽  
Mario Munoz-Organero ◽  
Gustavo Ramirez-Gonzalez
Keyword(s):  
Emotion Recognition ◽  
Recommender System ◽  
Mobile Applications ◽  
Short Term Memory ◽  
Physiological Data ◽  
Emotional States ◽  
Final Phase ◽  
Short Term ◽  
Life Context ◽  
Long Short Term Memory

The collection of physiological data from people has been facilitated due to the mass use of cheap wearable devices. Although the accuracy is low compared to specialized healthcare devices, these can be widely applied in other contexts. This study proposes the architecture for a tourist experiences recommender system (TERS) based on the user’s emotional states who wear these devices. The issue lies in detecting emotion from Heart Rate (HR) measurements obtained from these wearables. Unlike most state-of-the-art studies, which have elicited emotions in controlled experiments and with high-accuracy sensors, this research’s challenge consisted of emotion recognition (ER) in the daily life context of users based on the gathering of HR data. Furthermore, an objective was to generate the tourist recommendation considering the emotional state of the device wearer. The method used comprises three main phases: The first was the collection of HR measurements and labeling emotions through mobile applications. The second was emotional detection using deep learning algorithms. The final phase was the design and validation of the TERS-ER. In this way, a dataset of HR measurements labeled with emotions was obtained as results. Among the different algorithms tested for ER, the hybrid model of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks had promising results. Moreover, concerning TERS, Collaborative Filtering (CF) using CNN showed better performance.

Classification of Physiological Data for Emotion Recognition

2017 ◽  
pp. 619-627
Author(s):  
Philip Gouverneur ◽  
Joanna Jaworek-Korjakowska ◽  
Lukas Köping ◽  
Kimiaki Shirahama ◽  
Pawel Kleczek ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Physiological Data

scholarly journals Multimodal Approach for Emotion Recognition Using a Formal Computational Model

2013 ◽  
Vol 4 (3) ◽  
pp. 11-25 ◽  
Author(s):  
Imen Tayari Meftah ◽  
Nhan Le Thanh ◽  
Chokri Ben Amar
Keyword(s):  
Emotion Recognition ◽  
Physiological Data ◽  
Emotional States ◽  
Multimodal Approach ◽  
Sources Of Information ◽  
Recognition Method ◽  
Signal Processing Algorithms ◽  
Multimodal Emotion Recognition ◽  
Many Sources ◽  
Affective Information

Emotions play a crucial role in human-computer interaction. They are generally expressed and perceived through multiple modalities such as speech, facial expressions, physiological signals. Indeed, the complexity of emotions makes the acquisition very difficult and makes unimodal systems (i.e., the observation of only one source of emotion) unreliable and often unfeasible in applications of high complexity. Moreover the lack of a standard in human emotions modeling hinders the sharing of affective information between applications. In this paper, the authors present a multimodal approach for the emotion recognition from many sources of information. This paper aims to provide a multi-modal system for emotion recognition and exchange that will facilitate inter-systems exchanges and improve the credibility of emotional interaction between users and computers. The authors elaborate a multimodal emotion recognition method from Physiological Data based on signal processing algorithms. The authors’ method permits to recognize emotion composed of several aspects like simulated and masked emotions. This method uses a new multidimensional model to represent emotional states based on an algebraic representation. The experimental results show that the proposed multimodal emotion recognition method improves the recognition rates in comparison to the unimodal approach. Compared to the state of art multimodal techniques, the proposed method gives a good results with 72% of correct.

161: Robot-Assisted Laparoscopic Pyeloplasty - 5-Year Follow-Up in a Single Center

2007 ◽  
Vol 177 (4S) ◽  
pp. 55-55
Author(s):  
Christian Schwentner ◽  
Andreas Lunacek ◽  
Alexandre E. Pelzer ◽  
Richard Neururer ◽  
Wolfgang Horninger ◽  
...  
Keyword(s):  
Single Center ◽  
Laparoscopic Pyeloplasty ◽  
Robot Assisted
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