scholarly journals Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 173
Author(s):  
Marco Bilucaglia ◽  
Gian Marco Duma ◽  
Giovanni Mento ◽  
Luca Semenzato ◽  
Patrizio E. Tressoldi
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Signal Classification ◽  
Human Cognition ◽  
Classifier Combination ◽  
Activity Measurement ◽  
Dynamic Feature ◽  
Spectral Features ◽  
Emotion Identification ◽  
Temporal Features

Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the performance of static and dynamic (time evolving) approaches. The best static feature-classifier combination was the SVM with spectral features (51.8%), followed by LDA with spectral features (51.4%) and kNN with temporal features (51%). The best dynamic feature‑classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and LDA with temporal features (63.68%). The results show a clear increase in classification accuracy with temporal dynamic features.

scholarly journals Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity

F1000Research ◽  
2021 ◽  
Vol 9 ◽  
pp. 173
Author(s):  
Marco Bilucaglia ◽  
Gian Marco Duma ◽  
Giovanni Mento ◽  
Luca Semenzato ◽  
Patrizio E. Tressoldi
Keyword(s):  
Machine Learning ◽  
Brain Activity ◽  
Signal Classification ◽  
Human Cognition ◽  
Classifier Combination ◽  
Activity Measurement ◽  
Dynamic Feature ◽  
Spectral Features ◽  
Emotion Identification ◽  
Temporal Features

Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the performance of static and dynamic (time evolving) approaches. The best static feature-classifier combination was the SVM with spectral features (51.8%), followed by LDA with spectral features (51.4%) and kNN with temporal features (51%). The best dynamic feature‑classifier combination was the SVM with temporal features (63.8%), followed by kNN with temporal features (63.70%) and LDA with temporal features (63.68%). The results show a clear increase in classification accuracy with temporal dynamic features.

scholarly journals Applying machine learning EEG signal classification to emotion‑related brain anticipatory activity

F1000Research ◽  
2020 ◽  
Vol 9 ◽  
pp. 173
Author(s):  
Marco Bilucaglia ◽  
Gian Marco Duma ◽  
Giovanni Mento ◽  
Luca Semenzato ◽  
Patrizio Tressoldi
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Brain Activity ◽  
Signal Classification ◽  
Human Cognition ◽  
Support Vector ◽  
Activity Measurement ◽  
Emotion Identification ◽  
Linear Discriminant ◽  
Temporal Features

Machine learning approaches have been fruitfully applied to several neurophysiological signal classification problems. Considering the relevance of emotion in human cognition and behaviour, an important application of machine learning has been found in the field of emotion identification based on neurophysiological activity. Nonetheless, there is high variability in results in the literature depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight into machine learning applied to emotion identification based on electrophysiological brain activity. For this reason, we analysed previously recorded EEG activity measured while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (linear discriminant analysis, support vector machine and k-nearest neighbour) was compared using both spectral and temporal features. Furthermore, we also contrasted the classifiers’ performance with static and dynamic (time evolving) features. The results show a clear increase in classification accuracy with temporal dynamic features. In particular, the support vector machine classifiers with temporal features showed the best accuracy (63.8 %) in classifying high vs low arousal auditory stimuli.

scholarly journals EEG Signal Classification: An Application to the Emotion Related Brain Anticipatory Activity

2019 ◽  
Author(s):  
Marco Bilucaglia ◽  
Gian Marco Duma ◽  
Giovanni Mento ◽  
Luca Semenzato ◽  
Patrizio Tressoldi
Keyword(s):  
Brain Activity ◽  
Classification Performance ◽  
Stimulus Onset ◽  
Human Cognition ◽  
Activity Measurement ◽  
Classification Problems ◽  
Dynamic Features ◽  
Emotion Identification ◽  
Temporal Features ◽  
Neurophysiological Activity

Machine Learning (ML) approaches have been fruitfully applied to several classification problems of neurophysiological activity. Considering the relevance of emotion in human cognition and behaviour, ML found an important application field in emotion identification based on neurophysiological activity. Nonetheless, the literature results present a high variability depending on the neuronal activity measurement, the signal features and the classifier type. The present work aims to provide new methodological insight on ML applied to emotion identification based on electrophysiological brain activity. For this reason, we recorded EEG activity while emotional stimuli, high and low arousal (auditory and visual) were provided to a group of healthy participants. Our target signal to classify was the pre-stimulus onset brain activity. Classification performance of three different classifiers (LDA, SVM and kNN) was compared using both spectral and temporal features. Furthermore, we also contrasted the classifiers performance with static and dynamic (time evolving) features. The results show a clear increased in classification accuracy with temporal dynamic features. In particular, the SVM classifiers with temporal features showed the best accuracy (63.8 %) in classifying high vs. low arousal auditory stimuli.

scholarly journals Decoding EEG Brain Activity for Multi-Modal Natural Language Processing

2021 ◽  
Vol 15 ◽  
Author(s):  
Nora Hollenstein ◽  
Cedric Renggli ◽  
Benjamin Glaus ◽  
Maria Barrett ◽  
Marius Troendle ◽  
...  
Keyword(s):  
Machine Learning ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Large Scale ◽  
Brain Activity ◽  
Training Data ◽  
Human Cognition ◽  
Special Focus ◽  
Eeg Data

Until recently, human behavioral data from reading has mainly been of interest to researchers to understand human cognition. However, these human language processing signals can also be beneficial in machine learning-based natural language processing tasks. Using EEG brain activity for this purpose is largely unexplored as of yet. In this paper, we present the first large-scale study of systematically analyzing the potential of EEG brain activity data for improving natural language processing tasks, with a special focus on which features of the signal are most beneficial. We present a multi-modal machine learning architecture that learns jointly from textual input as well as from EEG features. We find that filtering the EEG signals into frequency bands is more beneficial than using the broadband signal. Moreover, for a range of word embedding types, EEG data improves binary and ternary sentiment classification and outperforms multiple baselines. For more complex tasks such as relation detection, only the contextualized BERT embeddings outperform the baselines in our experiments, which raises the need for further research. Finally, EEG data shows to be particularly promising when limited training data is available.

scholarly journals Machine Learning Techniques for Brain Signal Analysis

2020 ◽  
Author(s):  
Charlie Dondapati ◽  
Arakkal Fahad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Signal Analysis ◽  
Brain Activity ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Human Cognition ◽  
Emotion Classification ◽  
Learning Models ◽  
Human Emotions ◽  
High Level

<p>Brain signal analysis has revolutionized the research on human-computer interaction. Analyzing brain activity of the human emotions opens greater avenues to advance the research on Brain signal analysis. Human emotions play a significant role in social intercourse, human cognition, and decision making.[1] In this project, Differential Entropy (DE) features of EEG are used to perform emotion classification. The DE features are more suited for emotion recognition than Energy spectrum (ES) features which are used traditionally [2]. We have applied machine learning algorithms to discriminate three categories of human emotion: 1) positive 2) neutral and 3) negative. Feature extraction and dimensionality reduction are performed on the EEG dataset to obtain high-level features which helped to increase the accuracy and efficiency of the classification models. We have performed numerous machine learning models on the EEG data and compared the results of deep learning models and shallow models. .</p><br>

scholarly journals Machine Learning Techniques for Brain Signal Analysis

2020 ◽  
Author(s):  
Charlie Dondapati ◽  
Arakkal Fahad ◽  
Jinan Fiaidhi
Keyword(s):  
Machine Learning ◽  
Signal Analysis ◽  
Brain Activity ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Human Cognition ◽  
Emotion Classification ◽  
Learning Models ◽  
Human Emotions ◽  
High Level

<p>Brain signal analysis has revolutionized the research on human-computer interaction. Analyzing brain activity of the human emotions opens greater avenues to advance the research on Brain signal analysis. Human emotions play a significant role in social intercourse, human cognition, and decision making.[1] In this project, Differential Entropy (DE) features of EEG are used to perform emotion classification. The DE features are more suited for emotion recognition than Energy spectrum (ES) features which are used traditionally [2]. We have applied machine learning algorithms to discriminate three categories of human emotion: 1) positive 2) neutral and 3) negative. Feature extraction and dimensionality reduction are performed on the EEG dataset to obtain high-level features which helped to increase the accuracy and efficiency of the classification models. We have performed numerous machine learning models on the EEG data and compared the results of deep learning models and shallow models. .</p><br>

scholarly journals Brain Activity-Based Metrics for Assessing Learning States in VR under Stress among Firefighters: An Explorative Machine Learning Approach in Neuroergonomics

2021 ◽  
Vol 11 (7) ◽  
pp. 885
Author(s):  
Maher Abujelala ◽  
Rohith Karthikeyan ◽  
Oshin Tyagi ◽  
Jing Du ◽  
Ranjana K. Mehta
Keyword(s):  
Machine Learning ◽  
Environmental Conditions ◽  
Brain Activity ◽  
Memory Task ◽  
Classification Problem ◽  
Brain Regions ◽  
Training Data ◽  
Information Encoding ◽  
Machine Learning Approach ◽  
Encoding And Retrieval

The nature of firefighters` duties requires them to work for long periods under unfavorable conditions. To perform their jobs effectively, they are required to endure long hours of extensive, stressful training. Creating such training environments is very expensive and it is difficult to guarantee trainees’ safety. In this study, firefighters are trained in a virtual environment that includes virtual perturbations such as fires, alarms, and smoke. The objective of this paper is to use machine learning methods to discern encoding and retrieval states in firefighters during a visuospatial episodic memory task and explore which regions of the brain provide suitable signals to solve this classification problem. Our results show that the Random Forest algorithm could be used to distinguish between information encoding and retrieval using features extracted from fNIRS data. Our algorithm achieved an F-1 score of 0.844 and an accuracy of 79.10% if the training and testing data are obtained at similar environmental conditions. However, the algorithm’s performance dropped to an F-1 score of 0.723 and accuracy of 60.61% when evaluated on data collected under different environmental conditions than the training data. We also found that if the training and evaluation data were recorded under the same environmental conditions, the RPM, LDLPFC, RDLPFC were the most relevant brain regions under non-stressful, stressful, and a mix of stressful and non-stressful conditions, respectively.

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