Emotion Detection Through EEG Signals Using FFT and Machine Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

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The effects of applying filters on EEG signals for classifying developers’ code comprehension

Journal of Applied Research and Technology ◽

10.22201/icat.24486736e.2021.19.6.1299 ◽

2021 ◽

Vol 19 (6) ◽

pp. 584-602

Author(s):

Lucian Jose Gonçales ◽

Kleinner Farias ◽

Lucas Kupssinskü ◽

Matheus Segalotto

Keyword(s):

Machine Learning ◽

Software Engineering ◽

Random Forest ◽

Random Forest Classifier ◽

Machine Learning Techniques ◽

Eeg Signals ◽

Machine Learning Technique ◽

Learning Techniques ◽

Learning Technique ◽

F Measure

EEG signals are a relevant indicator for measuring aspects related to human factors in Software Engineering. EEG is used in software engineering to train machine learning techniques for a wide range of applications, including classifying task difficulty, and developers’ level of experience. The EEG signal contains noise such as abnormal readings, electrical interference, and eye movements, which are usually not of interest to the analysis, and therefore contribute to the lack of precision of the machine learning techniques. However, research in software engineering has not evidenced the effectiveness when applying these filters on EEG signals. The objective of this work is to analyze the effectiveness of filters on EEG signals in the software engineering context. As literature did not focus on the classification of developers’ code comprehension, this study focuses on the analysis of the effectiveness of applying EEG filters for training a machine learning technique to classify developers' code comprehension. A Random Forest (RF) machine learning technique was trained with filtered EEG signals to classify the developers' code comprehension. This study also trained another random forest classifier with unfiltered EEG data. Both models were trained using 10-fold cross-validation. This work measures the classifiers' effectiveness using the f-measure metric. This work used the t-test, Wilcoxon, and U Mann Whitney to analyze the difference in the effectiveness measures (f-measure) between the classifier trained with filtered EEG and the classifier trained with unfiltered EEG. The tests pointed out that there is a significant difference after applying EEG filters to classify developers' code comprehension with the random forest classifier. The conclusion is that the use of EEG filters significantly improves the effectivity to classify code comprehension using the random forest technique.

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Machine Learning Methods as a Test Bed for EEG Analysis in BCI Paradigms

Handbook of Research on Applied Cybernetics and Systems Science - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2498-4.ch008 ◽

2017 ◽

pp. 186-206

Author(s):

Kusuma Mohanchandra ◽

Snehanshu Saha

Keyword(s):

Machine Learning ◽

Nearest Neighbor ◽

Machine Learning Techniques ◽

Analytical Models ◽

Support Vector ◽

Test Bed ◽

K Nearest Neighbor ◽

Eeg Signals ◽

Broad Perspective ◽

Learning Techniques

Machine learning techniques, is a crucial tool to build analytical models in EEG data analysis. These models are an excellent choice for analyzing the high variability in EEG signals. The advancement in EEG-based Brain-Computer Interfaces (BCI) demands advanced processing tools and algorithms for exploration of EEG signals. In the context of the EEG-based BCI for speech communication, few classification and clustering techniques is presented in this book chapter. A broad perspective of the techniques and implementation of the weighted k-Nearest Neighbor (k-NN), Support vector machine (SVM), Decision Tree (DT) and Random Forest (RF) is explained and their usage in EEG signal analysis is mentioned. We suggest that these machine learning techniques provides not only potentially valuable control mechanism for BCI but also a deeper understanding of neuropathological mechanisms underlying the brain in ways that are not possible by conventional linear analysis.

Download Full-text