scholarly journals EEG-Based Emotion Classification for Verifying the Korean Emotional Movie Clips with Support Vector Machine (SVM)

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Guiyoung Son ◽  
Yaeri Kim
Keyword(s):  
Support Vector Machine ◽  
Emotional Processing ◽  
Spectral Power ◽  
Brain Activity ◽  
Brain Area ◽  
Interdisciplinary Studies ◽  
Support Vector ◽  
Emotion Classification ◽  
Brain Topography ◽  
Electroencephalogram Eeg

Emotion plays a crucial role in understanding each other under natural communication in daily life. Electroencephalogram (EEG), based on emotion classification, has been widely utilized in the fields of interdisciplinary studies because of emotion representation’s objectiveness. In this paper, it aimed to introduce the Korean continuous emotional database and investigate brain activity during emotional processing. Moreover, we selected emotion-related channels for verifying the generated database using the Support Vector Machine (SVM). First, we recorded EEG signals, collected from 28 subjects, to investigate the brain activity across brain areas while watching movie clips by five emotions (anger, excitement, fear, sadness, and happiness) and a neutral state. We analyzed EEG raw signals to investigate the emotion-related brain area and select suitable emotion-related channels using spectral power across frequency bands, i.e., alpha and beta bands. As a result, we select the eight-channel set, namely, AF3-AF4, F3-F4, F7-F8, and P7-P8, from statistical and brain topography analysis. We perform the classification using SVM and achieve the best accuracy of 94.27% when utilizing the selected channels set with five emotions. In conclusion, we provide a fundamental emotional database reflecting Korean feelings and the evidence of different emotions for application to broaden area.

scholarly journals Framing Twitter Public Sentiment on Nigerian Government COVID-19 Palliatives Distribution Using Machine Learning

2021 ◽  
Vol 13 (6) ◽  
pp. 3497
Author(s):  
Hassan Adamu ◽  
Syaheerah Lebai Lutfi ◽  
Nurul Hashimah Ahamed Hassain Malim ◽  
Rohail Hassan ◽  
Assunta Di Vaio ◽  
...  
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Nearest Neighbor ◽  
Primary Objective ◽  
Support Vector ◽  
Standard English ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
The Public ◽  
The Government

Sustainable development plays a vital role in information and communication technology. In times of pandemics such as COVID-19, vulnerable people need help to survive. This help includes the distribution of relief packages and materials by the government with the primary objective of lessening the economic and psychological effects on the citizens affected by disasters such as the COVID-19 pandemic. However, there has not been an efficient way to monitor public funds’ accountability and transparency, especially in developing countries such as Nigeria. The understanding of public emotions by the government on distributed palliatives is important as it would indicate the reach and impact of the distribution exercise. Although several studies on English emotion classification have been conducted, these studies are not portable to a wider inclusive Nigerian case. This is because Informal Nigerian English (Pidgin), which Nigerians widely speak, has quite a different vocabulary from Standard English, thus limiting the applicability of the emotion classification of Standard English machine learning models. An Informal Nigerian English (Pidgin English) emotions dataset is constructed, pre-processed, and annotated. The dataset is then used to classify five emotion classes (anger, sadness, joy, fear, and disgust) on the COVID-19 palliatives and relief aid distribution in Nigeria using standard machine learning (ML) algorithms. Six ML algorithms are used in this study, and a comparative analysis of their performance is conducted. The algorithms are Multinomial Naïve Bayes (MNB), Support Vector Machine (SVM), Random Forest (RF), Logistics Regression (LR), K-Nearest Neighbor (KNN), and Decision Tree (DT). The conducted experiments reveal that Support Vector Machine outperforms the remaining classifiers with the highest accuracy of 88%. The “disgust” emotion class surpassed other emotion classes, i.e., sadness, joy, fear, and anger, with the highest number of counts from the classification conducted on the constructed dataset. Additionally, the conducted correlation analysis shows a significant relationship between the emotion classes of “Joy” and “Fear”, which implies that the public is excited about the palliatives’ distribution but afraid of inequality and transparency in the distribution process due to reasons such as corruption. Conclusively, the results from this experiment clearly show that the public emotions on COVID-19 support and relief aid packages’ distribution in Nigeria were not satisfactory, considering that the negative emotions from the public outnumbered the public happiness.

Practical Artifact Removal Brain-Computer Interface System

2014 ◽  
pp. 265-277
Author(s):  
Wei-Yen Hsu
Keyword(s):  
Support Vector Machine ◽  
Classification Accuracy ◽  
Brain Computer Interface ◽  
Computer Interface ◽  
Support Vector ◽  
Artifact Removal ◽  
Eeg Data ◽  
Interface System ◽  
Electroencephalogram Eeg ◽  
Bci System

In this chapter, a practical artifact removal Brain-Computer Interface (BCI) system for single-trial Electroencephalogram (EEG) data is proposed for applications in neuroprosthetics. Independent Component Analysis (ICA) combined with the use of a correlation coefficient is proposed to remove the EOG artifacts automatically, which can further improve classification accuracy. The features are then extracted from wavelet transform data by means of the proposed modified fractal dimension. Finally, Support Vector Machine (SVM) is used for the classification. When compared with the results obtained without using the EOG signal elimination, the proposed BCI system achieves promising results that will be effectively applied in neuroprosthetics.

scholarly journals Automatic Emotion-Based Music Classification for Supporting Intelligent IoT Applications

Electronics ◽  
2019 ◽  
Vol 8 (2) ◽  
pp. 164 ◽  
Author(s):  
Yeong-Seok Seo ◽  
Jun-Ho Huh
Keyword(s):  
Support Vector Machine ◽  
Industrial Revolution ◽  
Nearest Neighbor ◽  
New Technologies ◽  
Support Vector ◽  
Emotion Classification ◽  
K Nearest Neighbor ◽  
Music Classification ◽  
Iot Applications ◽  
Emotional Model

With the arrival of the fourth industrial revolution, new technologies that integrate emotional intelligence into existing IoT applications are being studied. Of these technologies, emotional analysis research for providing various music services has received increasing attention in recent years. In this paper, we propose an emotion-based automatic music classification method to classify music with high accuracy according to the emotional range of people. In particular, when the new (unlearned) songs are added to a music-related IoT application, it is necessary to build mechanisms to classify them automatically based on the emotion of humans. This point is one of the practical issues for developing the applications. A survey for collecting emotional data is conducted based on the emotional model. In addition, music features are derived by discussing with the working group in a small and medium-sized enterprise. Emotion classification is carried out using multiple regression analysis and support vector machine. The experimental results show that the proposed method identifies most of induced emotions felt by music listeners and accordingly classifies music successfully. In addition, comparative analysis is performed with different classification algorithms, such as random forest, deep neural network and K-nearest neighbor, as well as support vector machine.

135 One night of mild sleep restriction affects EEG and behavioural measures of vigilance

SLEEP ◽  
2021 ◽  
Vol 44 (Supplement_2) ◽  
pp. A55-A55
Author(s):  
Aaron Gibbings ◽  
Laura Ray ◽  
Nareg Berberian ◽  
Ali Shahidi Zandi ◽  
Adrian Owen ◽  
...  
Keyword(s):  
Emotional Processing ◽  
Spectral Power ◽  
Brain Activity ◽  
Sleep Onset ◽  
Vigilance Task ◽  
Sleep Loss ◽  
Sleep Restriction ◽  
Beta Activity ◽  
Life Threatening ◽  
Single Night

Abstract Introduction Much is known about the behavioural and cognitive consequences of chronic sleep loss but relatively little is known about the changes in brain activity associated with reduced vigilance after mild and acute sleep loss. Mild and acute sleep loss is generally thought to be innocuous despite research showing emotional processing, visual attention and behavioural responding are all negatively impacted by even small amounts of sleep loss. The current study investigated behavioural, cognitive, and electrophysiological consequences of mild (i.e., a couple of hours) and acute (i.e., a single night) sleep loss via simultaneous behavioural and physiological measures of vigilance. Methods Participants (N = 23; 18 females, Mage = 22 ± 3 years) came into the lab (from ~12 pm to 3 pm) for two testing days after sleeping from 1 am to 6 am (Sleep Restriction), or from 12 am to 9 am (Normally Rested). Brain activity was recorded using electroencephalography (EEG) from 15 scalp derivations, while vigilance was assessed simultaneously using the psychomotor vigilance task (PVT). Results Vigilance was reduced in the Sleep Restricted vs. Normally Rested condition, (F(1,22)=9.02, p=0.007). This was exacerbated over the course of performing the PVT, (F(5,110)=8.12, p<0.001). Sleep Restriction also resulted in increased intensity of alpha burst activity compared to the Normally Rested condition (F(1,20)=6.19, p=0.022). Lastly, EEG spectral power differed between restriction sleep conditions across deepening stages of sleep onset, particularly for frequencies that reflect arousal e.g., delta, alpha and beta activity (F(1,20)>5.52, p<0.029). Conclusion These results suggest that even a small amount of sleep loss, occurring on only one night significantly reduces vigilance and impacts the physiology of the brain in ways that reflect reduced arousal. Understanding the neural correlates and cognitive processes associated with sleep loss may lead to important advancements in identifying and preventing potentially deleterious or dangerous, sleep-related lapses in vigilance (e.g., in the classroom, workplace), and when lapses in vigilance can be life-threatening (e.g., while driving). Support (if any):

scholarly journals EEG signal classification for drowsiness detection using wavelet transform and support vector machine

2021 ◽  
Vol 10 (2) ◽  
pp. 501
Author(s):  
Novie Theresia Br. Pasaribu ◽  
Timotius Halim ◽  
Ratnadewi Ratnadewi ◽  
Agus Prijono
Keyword(s):  
Support Vector Machine ◽  
Wavelet Transform ◽  
Support Vector ◽  
Eeg Signal ◽  
Drowsiness Detection ◽  
Driver Drowsiness ◽  
Electroencephalogram Eeg ◽  
Behavioral Methods ◽  
Physiological Method ◽  
Fold Cross Validation

<span id="docs-internal-guid-ed628156-7fff-8934-2369-94f011b043ca"><span>There are several categories to detect and measure driver drowsiness such as physiological methods, subjective methods and behavioral methods. The most objective method for drowsiness detection is the physiological method. One of the physiological methods used is an electroencephalogram (EEG). In this research wavelet transform is used as a feature extraction and using support vector machine (SVM) as a classifier. We proposed an experiment of retrieval data which is designed by using modified-EAR and EEG signal. From the SVM training process, with the 5-fold cross validation, Quadratic kernel has the highest accuracy 84.5% then others. In testing Driving-2 process 7 respondents were detected as drowsiness class, and 3 respondents were detected as awake class. In the testing of Driving-3 process, 6 respondents were detected as drowsiness class, and 4 respondents were detected as awake class. </span></span>

scholarly journals Single channel electroencephalogram (EEG) brain computer interface (BCI) feature extraction and quantization method for support vector machine classification

2018 ◽  
Vol 7 (4) ◽  
pp. 2095 ◽  
Author(s):  
Tarmizi Ahmad Izzuddin ◽  
Norlaili Mat Safri ◽  
Fauzal Naim Zohedi ◽  
Mohamad Afzan Othman ◽  
Muhammad Shaufil Adha Shawkany Hazim
Keyword(s):  
Support Vector Machine ◽  
Single Channel ◽  
Brain Computer Interface ◽  
Target Object ◽  
International Standards ◽  
Assistive Robotics ◽  
Computer Interface ◽  
Support Vector ◽  
Electroencephalogram Eeg ◽  
Bci System

Over the recent years, there has been a huge interest towards Electroencephalogram (EEG) based brain computer interface (BCI) system. BCI system enables the extraction of meaningful information directly from the human brain via suitable signal processing and machine learning method and thus, many researches have applied this technology towards rehabilitation and assistive robotics. Such application is important towards improving the lives of people with motor diseases such as Amytrophic Lateral Scelorosis (ALS) disease or people with quadriplegia/tetraplegia. This paper introduces features extraction method based on the Fast Fourier Transform (FFT) with logarithmic bin-ning for rapid classification using Support Vector Machine (SVM) algorithm, with an application towards a BCI system with a shared con-trol scheme. In general, subjects wearing a single channel EEG electrode located at F8 (10-20 international standards) were required to syn-chronously imagine a star rotating and mind relaxation at specific time and direction. The imagination of a star would trigger a mobile robot suggesting that there exists a target object at certain direction. Based on the proposed algorithm, we showed that our algorithm can distin-guish between mind relaxation and mental star rotation with up to 80% accuracy from the single channel EEG signals.  

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