Emotional State Recognition Using Advanced Machine Learning Techniques on EEG Data

Giannakaki, Katerina; Giannakakis, Giorgos; Farmaki, Christina; Sakkalis, Vangelis

doi:10.1109/cbms.2017.156

Cited by 28 publications

(16 citation statements)

References 33 publications

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“…It is independent of human peripheral nerves and muscle tissues, and can realize the communications and controls between the human brain and computer or other peripheral devices. After the researchers extracted the signal characteristics of the acquired EEG signals, they usually used the machine learning methods for classification and recognition [9][10][11][12][13]. The ultimate goal is to send instructions and control external devices.…”

Section: Introductionmentioning

confidence: 99%

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Novel joint algorithm based on EEG in complex scenarios

Chen

Yang

Miao

et al. 2019

Computer Assisted Surgery

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At present, in the field of electroencephalogram (EEG) signal recognition, the classification and recognition in complex scenarios with more categories of EEG signals have gained more attention. Based on the joint fast Fourier transform (FFT) and support vector machine (SVM) methods, this study proposed a novel EEG signal-processing joint method for the complex scenarios with 10 classifications of EEG signals. Moreover, a comprehensive efficiency formula was put forward. The formula considered the accuracy and time consumption of the joint method. This new joint method could improve the accuracy and comprehensive efficiency of multiclass EEG signal recognition. The new joint approach used standardization for data preprocessing. Feature extraction was performed by combining FFT and principal component analysis methods. EEG signals were classified using the weighted k-nearest nenighbour method. In this study, experiments were conducted using public datasets of brainwave 0-9 digits classification. The result demonstrated that the accuracy and comprehensive efficiency of the novel joint method were 84% and 87%, respectively, which were better than those of the existing methods. The precision rate, recall rate, and F1 score of the novel joint method were 89%, 85%, and 0.85, respectively. In conclusion, the proposed joint method was effective in a complex scenario for multiclass EEG signal recognition.

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Section: Introductionmentioning

confidence: 99%

“…Several studies have reported on the classification of EEG data. For example, Giannakaki et al [9] collected EEG signals by inducing different emotions and obtained three different types of EEG signals representing calm, positive, and negative emotions. They used wavelet transform combined with SVM method to classify the signals.…”

Section: Introductionmentioning

confidence: 99%

Novel joint algorithm based on EEG in complex scenarios

Chen

Yang

Miao

et al. 2019

Computer Assisted Surgery

View full text Add to dashboard Cite

show abstract

“…The growing use of machine learning in EEG research is at least partially attributable to the increased availability of code and software packages, as well as the ever-increasing computational capacity of modern computers, without the need to purchase a machine that can fill a small room. The purposes of studies of this nature have varied widely, and include attempts to detect disease (Orrù, Pettersson-Yeo, Marquand, Sartori, & Mechelli, 2012), recognize emotional states (Giannakaki, Giannakakis, Farmaki, & Sakkalis 2017), and even classify individuals by learning style (Jawed, Amin, Malik, & Faye, 2019). In the case of brain-computer interface (BCI) applications, motor cortical patterns have been classified and used to control virtual and even physical objects (Abiri, Borhani, Sellers, Jiang, & Zhao, 2018).…”

Section: Eeg and Machine Learningmentioning

confidence: 99%

Classifying Creativity: Applying Machine Learning Techniques to Divergent Thinking EEG Data

Stevens¹,

Zabelina²

2020

Preprint

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Prior research has shown that greater EEG alpha power (8-13 Hz) is characteristic of more creative individuals, and more creative task conditions. The present study investigated the potential for machine learning to classify more and less creative brain states. Participants completed an Alternate Uses Task, in which they thought of Normal or Uncommon (more creative) uses for everyday objects (e.g., brick). We hypothesized that alpha power would be greater for Uncommon (vs. Common) uses, and that a machine learning (ML) approach would enable the reliable classification data from the two conditions. Further, we expected that ML would be successful at classifying more (vs. less) creative individuals. As expected, alpha power was significantly greater for the Uncommon than for the Normal condition. Using spectrally weighted common spatial patterns to extract EEG features, and quadratic discriminant analysis, we found that classification accuracy for the two conditions varied widely among individuals, with a mean of 63.9%. For more vs. less creative individuals, 82.3% classification accuracy was attained. These findings indicate the potential for broader adoption of machine learning in creativity research.

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“…is study employs images to forecast the features that are extracted from images and calculates probabilities of the final movement. Electroencephalography (EEG) devices are also utilized in some of studies in the literature [21][22][23][24][25][26][27][28][29]. In [21], it is shown that measuring EEG signals gives an opinion about human emotional conditions.…”

Section: Related Workmentioning

confidence: 99%

“…Electroencephalography (EEG) devices are also utilized in some of studies in the literature [21][22][23][24][25][26][27][28][29]. In [21], it is shown that measuring EEG signals gives an opinion about human emotional conditions. Discrimination between calm, exciting positive, and exciting negative emotional states is observed when EEG is used.…”

Section: Related Workmentioning

confidence: 99%

Mood Detection from Physical and Neurophysical Data Using Deep Learning Models

Kilimci

Guven²,

Uysal

et al. 2019

Complexity

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Nowadays, smart devices as a part of daily life collect data about their users with the help of sensors placed on them. Sensor data are usually physical data but mobile applications collect more than physical data like device usage habits and personal interests. Collected data are usually classified as personal, but they contain valuable information about their users when it is analyzed and interpreted. One of the main purposes of personal data analysis is to make predictions about users. Collected data can be divided into two major categories: physical and behavioral data. Behavioral data are also named as neurophysical data. Physical and neurophysical parameters are collected as a part of this study. Physical data contains measurements of the users like heartbeats, sleep quality, energy, movement/mobility parameters. Neurophysical data contain keystroke patterns like typing speed and typing errors. Users’ emotional/mood statuses are also investigated by asking daily questions. Six questions are asked to the users daily in order to determine the mood of them. These questions are emotion-attached questions, and depending on the answers, users’ emotional states are graded. Our aim is to show that there is a connection between users’ physical/neurophysical parameters and mood/emotional conditions. To prove our hypothesis, we collect and measure physical and neurophysical parameters of 15 users for 1 year. The novelty of this work to the literature is the usage of both combinations of physical and neurophysical parameters. Another novelty is that the emotion classification task is performed by both conventional machine learning algorithms and deep learning models. For this purpose, Feedforward Neural Network (FFNN), Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural network are employed as deep learning methodologies. Multinomial Naïve Bayes (MNB), Support Vector Regression (SVR), Decision Tree (DT), Random Forest (RF), and Decision Integration Strategy (DIS) are evaluated as conventional machine learning algorithms. To the best of our knowledge, this is the very first attempt to analyze the neurophysical conditions of the users by evaluating deep learning models for mood analysis and enriching physical characteristics with neurophysical parameters. Experiment results demonstrate that the utilization of deep learning methodologies and the combination of both physical and neurophysical parameters enhances the classification success of the system to interpret the mood of the users. A wide range of comparative and extensive experiments shows that the proposed model exhibits noteworthy results compared to the state-of-art studies.

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Emotional State Recognition Using Advanced Machine Learning Techniques on EEG Data

Cited by 28 publications

References 33 publications

Novel joint algorithm based on EEG in complex scenarios

Novel joint algorithm based on EEG in complex scenarios

Classifying Creativity: Applying Machine Learning Techniques to Divergent Thinking EEG Data

Mood Detection from Physical and Neurophysical Data Using Deep Learning Models

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