scholarly journals Single Trial Classification of EEG and Peripheral Physiological Signals for Recognition of Emotions Induced by Music Videos

2010 ◽  
pp. 89-100 ◽  
Author(s):  
Sander Koelstra ◽  
Ashkan Yazdani ◽  
Mohammad Soleymani ◽  
Christian Mühl ◽  
Jong-Seok Lee ◽  
...  
Keyword(s):  
Music Videos ◽  
Physiological Signals ◽  
Single Trial ◽  
Recognition Of Emotions

scholarly journals Time–frequency time–space LSTM for robust classification of physiological signals

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tuan D. Pham
Keyword(s):  
Time Series ◽  
Network Architecture ◽  
Time Series Data ◽  
Short Term Memory ◽  
Physiological Signals ◽  
Series Data ◽  
Time Frequency ◽  
Time Space ◽  
Deep Recurrent Neural Network

AbstractAutomated analysis of physiological time series is utilized for many clinical applications in medicine and life sciences. Long short-term memory (LSTM) is a deep recurrent neural network architecture used for classification of time-series data. Here time–frequency and time–space properties of time series are introduced as a robust tool for LSTM processing of long sequential data in physiology. Based on classification results obtained from two databases of sensor-induced physiological signals, the proposed approach has the potential for (1) achieving very high classification accuracy, (2) saving tremendous time for data learning, and (3) being cost-effective and user-comfortable for clinical trials by reducing multiple wearable sensors for data recording.

Recognition of emotions in autistic children using physiological signals

2016 ◽  
Vol 6 (2) ◽  
pp. 137-147 ◽  
Author(s):  
Niranjana Krupa ◽  
Karthik Anantharam ◽  
Manoj Sanker ◽  
Sameer Datta ◽  
John Vijay Sagar
Keyword(s):  
Physiological Signals ◽  
Autistic Children ◽  
Recognition Of Emotions

Single-Trial Classification of MEG Recordings

2007 ◽  
Vol 54 (3) ◽  
pp. 436-443 ◽  
Author(s):  
Marcos Perreau Guimaraes ◽  
Dik Kin Wong ◽  
E. Timothy Uy ◽  
Logan Grosenick ◽  
Patrick Suppes
Keyword(s):  
Single Trial

scholarly journals Framework for the Classification of Emotions in People With Visual Disabilities Through Brain Signals

2021 ◽  
Vol 15 ◽  
Author(s):  
Jesús Leonardo López-Hernández ◽  
Israel González-Carrasco ◽  
José Luis López-Cuadrado ◽  
Belén Ruiz-Mezcua
Keyword(s):  
Machine Learning ◽  
Positive Emotions ◽  
Machine Learning Techniques ◽  
Visual Disabilities ◽  
Brain Signals ◽  
Learning Techniques ◽  
Sensory Disabilities ◽  
Recognition Of Emotions

Nowadays, the recognition of emotions in people with sensory disabilities still represents a challenge due to the difficulty of generalizing and modeling the set of brain signals. In recent years, the technology that has been used to study a person’s behavior and emotions based on brain signals is the brain–computer interface (BCI). Although previous works have already proposed the classification of emotions in people with sensory disabilities using machine learning techniques, a model of recognition of emotions in people with visual disabilities has not yet been evaluated. Consequently, in this work, the authors present a twofold framework focused on people with visual disabilities. Firstly, auditory stimuli have been used, and a component of acquisition and extraction of brain signals has been defined. Secondly, analysis techniques for the modeling of emotions have been developed, and machine learning models for the classification of emotions have been defined. Based on the results, the algorithm with the best performance in the validation is random forest (RF), with an accuracy of 85 and 88% in the classification for negative and positive emotions, respectively. According to the results, the framework is able to classify positive and negative emotions, but the experimentation performed also shows that the framework performance depends on the number of features in the dataset and the quality of the Electroencephalogram (EEG) signals is a determining factor.

scholarly journals A Novel Hybrid Machine Learning Classification for the Detection of Bruxism Patients Using Physiological Signals

2020 ◽  
Vol 10 (21) ◽  
pp. 7410
Author(s):  
Md Belal Bin Heyat ◽  
Faijan Akhtar ◽  
Asif Khan ◽  
Alam Noor ◽  
Bilel Benjdira ◽  
...  
Keyword(s):  
Home Monitoring ◽  
Physiological Signals ◽  
Sleep Stages ◽  
Monitoring Systems ◽  
The Novel ◽  
Hybrid Classifier ◽  
Machine Learning Classification ◽  
Power Spectral ◽  
Automatic Tool

Bruxism is a sleep disorder in which the patient clinches and gnashes their teeth. Bruxism detection using traditional methods is time-consuming, cumbersome, and expensive. Therefore, an automatic tool to detect this disorder will alleviate the doctor workload and give valuable help to patients. In this paper, we targeted this goal and designed an automatic method to detect bruxism from the physiological signals using a novel hybrid classifier. We began with data collection. Then, we performed the analysis of the physiological signals and the estimation of the power spectral density. After that, we designed the novel hybrid classifier to enable the detection of bruxism based on these data. The classification of the subjects into “healthy” or “bruxism” from the electroencephalogram channel (C4-A1) obtained a maximum specificity of 92% and an accuracy of 94%. Besides, the classification of the sleep stages such as the wake (w) stage and rapid eye movement (REM) stage from the electrocardiogram channel (ECG1-ECG2) obtained a maximum specificity of 86% and an accuracy of 95%. The combined bruxism classification and the sleep stages classification from the electroencephalogram channel (C4-P4) obtained a maximum specificity of 90% and an accuracy of 97%. The results show that more accurate bruxism detection is achieved by exploiting the electroencephalogram signal (C4-P4). The present work can be applied for home monitoring systems for bruxism detection.

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