scholarly journals Hyperscanning EEG and Classification Based on Riemannian Geometry for Festive and Violent Mental State Discrimination

2020 ◽  
Vol 14 ◽  
Author(s):  
Cédric Simar ◽  
Ana-Maria Cebolla ◽  
Gaëlle Chartier ◽  
Mathieu Petieau ◽  
Gianluca Bontempi ◽  
...  
Keyword(s):  
Riemannian Geometry ◽  
Mental States ◽  
Machine Learning Techniques ◽  
Eeg Signals ◽  
Action Execution ◽  
Distinctive Features ◽  
Non Invasive ◽  
State Discrimination ◽  
Learning Techniques ◽  
Eeg Recordings

Interactions between two brains constitute the essence of social communication. Daily movements are commonly executed during social interactions and are determined by different mental states that may express different positive or negative behavioral intent. In this context, the effective recognition of festive or violent intent before the action execution remains crucial for survival. Here, we hypothesize that the EEG signals contain the distinctive features characterizing movement intent already expressed before movement execution and that such distinctive information can be identified by state-of-the-art classification algorithms based on Riemannian geometry. We demonstrated for the first time that a classifier based on covariance matrices and Riemannian geometry can effectively discriminate between neutral, festive, and violent mental states only on the basis of non-invasive EEG signals in both the actor and observer participants. These results pave the way for new electrophysiological discrimination of mental states based on non-invasive EEG recordings and cutting-edge machine learning techniques.

Borderline and Depression: A Thin EEG Line

2021 ◽  
pp. 155005942110608
Author(s):  
Jakša Vukojević ◽  
Damir Mulc ◽  
Ivana Kinder ◽  
Eda Jovičić ◽  
Krešimir Friganović ◽  
...  
Keyword(s):  
Machine Learning ◽  
Borderline Personality ◽  
Machine Learning Techniques ◽  
Major Depressive ◽  
Everyday Clinical Practice ◽  
Data Set ◽  
Learning Techniques ◽  
Eeg Recordings ◽  
The Given ◽  
Close Interrelationship

In everyday clinical practice, there is an ongoing debate about the nature of major depressive disorder (MDD) in patients with borderline personality disorder (BPD). The underlying research does not give us a clear distinction between those 2 entities, although depression is among the most frequent comorbid diagnosis in borderline personality patients. The notion that depression can be a distinct disorder but also a symptom in other psychopathologies led our team to try and delineate those 2 entities using 146 EEG recordings and machine learning. The utilized algorithms, developed solely for this purpose, could not differentiate those 2 entities, meaning that patients suffering from MDD did not have significantly different EEG in terms of patients diagnosed with MDD and BPD respecting the given data and methods used. By increasing the data set and the spatiotemporal specificity, one could have a more sensitive diagnostic approach when using EEG recordings. To our knowledge, this is the first study that used EEG recordings and advanced machine learning techniques and further confirmed the close interrelationship between those 2 entities.

Non-invasive Deception Detection in Videos Using Machine Learning Techniques

2021 ◽  
Author(s):  
Siam Islam ◽  
Popin Saha ◽  
Touhidul Chowdhury ◽  
Asif Sorowar ◽  
Raqeebir Rab
Keyword(s):  
Machine Learning ◽  
Deception Detection ◽  
Machine Learning Techniques ◽  
Non Invasive ◽  
Learning Techniques

Soft Computing-Based Early Detection of Parkinson's Disease Using Non-Invasive Method Based on Speech Analysis

2021 ◽  
pp. 329-340
Author(s):  
Chandrasekar Ravi
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Speech Analysis ◽  
Machine Learning Techniques ◽  
Speech Signals ◽  
Fuzzy Classifier ◽  
Non Invasive ◽  
Specificity And Sensitivity ◽  
Learning Techniques ◽  
Invasive Method

This chapter aims to use the speech signals that are a behavioral bio-marker for Parkinson's disease. The victim's vocabulary is mostly lost, or big gaps are observed when they are talking or the conversation is abruptly stopped. Therefore, speech analysis could help to identify the complications in conversation from the inception of the symptoms of Parkinson's disease in initial phases itself. Speech can be regularly logged in an unobstructed approach and machine learning techniques can be applied and analyzed. Fuzzy logic-based classifier is proposed for learning from the training speech signals and classifying the test speech signals. Brainstorm optimization algorithm is proposed for extracting the fuzzy rules from the speech data, which is used by fuzzy classifier for learning and classification. The performance of the proposed classifier is evaluated using metrics like accuracy, specificity, and sensitivity, and compared with benchmark classifiers like SVM, naïve Bayes, k-means, and decision tree. It is observed that the proposed classifier outperforms the benchmark classifiers.

scholarly journals Non-Intrusive Load Monitoring of Residential Water-Heating Circuit Using Ensemble Machine Learning Techniques

Inventions ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 57
Author(s):  
Attique Ur Rehman ◽  
Tek Tjing Lie ◽  
Brice Vallès ◽  
Shafiqur Rahman Tito
Keyword(s):  
Machine Learning ◽  
Feature Selection ◽  
Machine Learning Techniques ◽  
Learning Models ◽  
Water Heating ◽  
Energy Monitoring ◽  
Non Invasive ◽  
Ensemble Machine Learning ◽  
Learning Techniques ◽  
Load Monitoring

The recent advancement in computational capabilities and deployment of smart meters have caused non-intrusive load monitoring to revive itself as one of the promising techniques of energy monitoring. Toward effective energy monitoring, this paper presents a non-invasive load inference approach assisted by feature selection and ensemble machine learning techniques. For evaluation and validation purposes of the proposed approach, one of the major residential load elements having solid potential toward energy efficiency applications, i.e., water heating, is considered. Moreover, to realize the real-life deployment, digital simulations are carried out on low-sampling real-world load measurements: New Zealand GREEN Grid Database. For said purposes, MATLAB and Python (Scikit-Learn) are used as simulation tools. The employed learning models, i.e., standalone and ensemble, are trained on a single household’s load data and later tested rigorously on a set of diverse households’ load data, to validate the generalization capability of the employed models. This paper presents a comprehensive performance evaluation of the presented approach in the context of event detection, feature selection, and learning models. Based on the presented study and corresponding analysis of the results, it is concluded that the proposed approach generalizes well to the unseen testing data and yields promising results in terms of non-invasive load inference.

scholarly journals Study of Machine Learning Techniques for EEG Eye State Detection

Proceedings ◽  
2020 ◽  
Vol 54 (1) ◽  
pp. 53
Author(s):  
Francisco Laport ◽  
Paula M. Castro ◽  
Adriana Dapena ◽  
Francisco J. Vazquez-Araujo ◽  
Daniel Iglesia
Keyword(s):  
Machine Learning ◽  
Mental States ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Discrete Wavelet ◽  
Linear Discriminant ◽  
State Identification ◽  
Brain Signals ◽  
State Classification ◽  
Learning Techniques

A comparison of different machine learning techniques for eye state identification through Electroencephalography (EEG) signals is presented in this paper. (1) Background: We extend our previous work by studying several techniques for the extraction of the features corresponding to the mental states of open and closed eyes and their subsequent classification; (2) Methods: A prototype developed by the authors is used to capture the brain signals. We consider the Discrete Fourier Transform (DFT) and the Discrete Wavelet Transform (DWT) for feature extraction; Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) for state classification; and Independent Component Analysis (ICA) for preprocessing the data; (3) Results: The results obtained from some subjects show the good performance of the proposed methods; and (4) Conclusion: The combination of several techniques allows us to obtain a high accuracy of eye identification.

scholarly journals Assessment of Non-Invasive Blood Pressure Prediction from PPG and rPPG Signals Using Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (18) ◽  
pp. 6022
Author(s):  
Fabian Schrumpf ◽  
Patrick Frenzel ◽  
Christoph Aust ◽  
Georg Osterhoff ◽  
Mirco Fuchs
Keyword(s):  
Blood Pressure ◽  
Prediction Error ◽  
Machine Learning Techniques ◽  
Systematic Evaluation ◽  
Average Error ◽  
Prediction Errors ◽  
Invasive Blood Pressure ◽  
Specific Data ◽  
Non Invasive ◽  
Learning Techniques

Exploiting photoplethysmography signals (PPG) for non-invasive blood pressure (BP) measurement is interesting for various reasons. First, PPG can easily be measured using fingerclip sensors. Second, camera based approaches allow to derive remote PPG (rPPG) signals similar to PPG and therefore provide the opportunity for non-invasive measurements of BP. Various methods relying on machine learning techniques have recently been published. Performances are often reported as the mean average error (MAE) on the data which is problematic. This work aims to analyze the PPG- and rPPG based BP prediction error with respect to the underlying data distribution. First, we train established neural network (NN) architectures and derive an appropriate parameterization of input segments drawn from continuous PPG signals. Second, we use this parameterization to train NNs with a larger PPG dataset and carry out a systematic evaluation of the predicted blood pressure. The analysis revealed a strong systematic increase of the prediction error towards less frequent BP values across NN architectures. Moreover, we tested different train/test set split configurations which underpin the importance of a careful subject-aware dataset assignment to prevent overly optimistic results. Third, we use transfer learning to train the NNs for rPPG based BP prediction. The resulting performances are similar to the PPG-only case. Finally, we apply different personalization techniques and retrain our NNs with subject-specific data for both the PPG-only and rPPG case. Whilst the particular technique is less important, personalization reduces the prediction errors significantly.

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