Improving the accuracy of EEG emotion recognition by combining valence lateralization and ensemble learning with tuning parameters

Pane, Evi Septiana; Wibawa, Adhi Dharma; Purnomo, Mauridhi Hery

doi:10.1007/s10339-019-00924-z

Cited by 55 publications

(26 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, since there is no precise regulation on the effective frequency band in the EEG signal, the bandpass filters used in different studies had different cutoff frequencies. Generally, the purpose of setting the low cutoff frequency at about 4 Hz ( Özerdem and Polat, 2017 ; Chao et al, 2018 ; Pane et al, 2019 ; Yin et al, 2020 ) was to remove electrooculography (EOG) artifacts (0–4 Hz) and potential artifacts of respiration and body movements within 0–3 Hz. While some documents set the low cutoff frequency at about 1 Hz ( Yuvaraj et al, 2014 ; Bhatti et al, 2016 ; Liang et al, 2019 ; Hou et al, 2020 ; Keelawat et al, 2021 ), the purpose of which was to remove the baseline drift (DC component) in the EEG signal and the 1/f noise introduced by the acquire equipment.…”

Section: Preprocessing Methods Of Electroencephalography Signalmentioning

confidence: 99%

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Cai

Xiao

Cui

et al. 2021

Front. Syst. Neurosci.

View full text Add to dashboard Cite

Emotion recognition has become increasingly prominent in the medical field and human-computer interaction. When people’s emotions change under external stimuli, various physiological signals of the human body will fluctuate. Electroencephalography (EEG) is closely related to brain activity, making it possible to judge the subject’s emotional changes through EEG signals. Meanwhile, machine learning algorithms, which are good at digging out data features from a statistical perspective and making judgments, have developed by leaps and bounds. Therefore, using machine learning to extract feature vectors related to emotional states from EEG signals and constructing a classifier to separate emotions into discrete states to realize emotion recognition has a broad development prospect. This paper introduces the acquisition, preprocessing, feature extraction, and classification of EEG signals in sequence following the progress of EEG-based machine learning algorithms for emotion recognition. And it may help beginners who will use EEG-based machine learning algorithms for emotion recognition to understand the development status of this field. The journals we selected are all retrieved from the Web of Science retrieval platform. And the publication dates of most of the selected articles are concentrated in 2016–2021.

show abstract

Section: Preprocessing Methods Of Electroencephalography Signalmentioning

confidence: 99%

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Cai

Xiao

Cui

et al. 2021

Front. Syst. Neurosci.

View full text Add to dashboard Cite

show abstract

“…There are, however, two approaches to the classification of emotion through EEG and these include the machine learning and neural network approaches. a) Machine learning approach: Some of the methods usually applied include decision tree (DT) [71], naïve bayes (NB) [72], quadratic discriminant analysis (QDA) [73], k-nearest neighbors (kNN) [58], [74], [75], linear discriminant analysis (LDA) [14], relevance vector machines (RVM) [67], xtreme gradient boosting (XGBoost) [76], support vector machine (SVM) [77]- [79], AdaBoost [80], logistic regression via variable splitting and augmented lagrangian (LORSAL) [81], random forest (RF) [56], [82], and graph regularized extreme learning machine (GELM) [83]. b) Neural network approach: This method include artificial neural network (ANN) [61], [63], [84] deep belief networks [70], [85], convolutional neural network (CNN) [40], [46], [86], [87], long short-term memory (LSTM) [66], generative adversarial networks (GAN) [88], capsule network (CapsNet) [45], [62], and hybrid methods [4], [44], [69].…”

Section: Classification Processmentioning

confidence: 99%

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Wirawan

Wardoyo

Lelono

2022

IJECE

View full text Add to dashboard Cite

Electroencephalogram (EEG) signals in recognizing emotions have several advantages. Still, the success of this study, however, is strongly influenced by: i) the distribution of the data used, ii) consider of differences in participant characteristics, and iii) consider the characteristics of the EEG signals. In response to these issues, this study will examine three important points that affect the success of emotion recognition packaged in several research questions: i) What factors need to be considered to generate and distribute EEG data?, ii) How can EEG signals be generated with consideration of differences in participant characteristics?, and iii) How do EEG signals with characteristics exist among its features for emotion recognition? The results, therefore, indicate some important challenges to be studied further in EEG signals-based emotion recognition research. These include i) determine robust methods for imbalanced EEG signals data, ii) determine the appropriate smoothing method to eliminate disturbances on the baseline signals, iii) determine the best baseline reduction methods to reduce the differences in the characteristics of the participants on the EEG signals, iv) determine the robust architecture of the capsule network method to overcome the loss of knowledge information and apply it in more diverse data set.

show abstract

“…Polysomnography (PSG) merupakan metode standar bagi tenaga medis untuk merekam aktivitas listrik jantung pada bagian tubuh. PSG umumnya menggunakan lebih dari dua belas sensor [7], di antaranya sensor electrocardiogram (ECG) untuk merekam aktivitas listrik pada bagian jantung [8], [9], sensor electroencephalogram (EEG) untuk merekam aktivitas listrik pada bagian otak [10]- [12], dan sensor yang merekam kandungan SpO2 pada darah [13]. Penggunaan PSG pada dasarnya memiliki beberapa kekurangan, di antaranya kompleksitas dan kerumitan saat perekaman data karena banyaknya sensor yang digunakan.…”

Section: Pendahuluanunclassified

Asesmen ECG-Apnea Satu Sadapan untuk Peningkatan Akurasi Klasifikasi Gangguan Tidur Berdasarkan AdaBoost

Fahruzi

Purnama

Purnomo

2020

JNTETI

Self Cite

View full text Add to dashboard Cite

Gangguan tidur adalah terganggunya aliran pernapasan (kolaps) yang berulang saat tidur. Gangguan tidur umumnya tidak terdiagnosis dan tidak tertangani dengan baik sehingga terjadi interupsi berulang pada saluran pernapasan. Kondisi tersebut berpotensi menyebabkan penderita mengalami gangguan tidur yang serius. Salah satu alat bantu yang digunakan untuk menganalisis adanya gangguan tidur pada ritme jantung adalah melalui rekaman electrocardiogram (ECG). ECG merupakan suatu grafik yang merepresentasikan aktivitas listrik pada jantung berupa gelombang P, gelombang QRS, dan gelombang T. Banyaknya sensor untuk merekam ECG menyebabkan ketidaknyamanan pasien saat perekaman data, menyebabkan kompleksitas rekaman, lambatnya komputasi, kesalahan dalam interpretasi, dan kehilangan informasi klinis. Oleh karena itu, dibutuhkan suatu sistem peringatan dini sebagai alat bantu medis yang mampu mendiagnosis rekaman ECG menggunakan satu sadapan. Sistem didesain menjadi lima tahapan, yaitu akuisisi rekaman ECG, praproses, ekstraksi fitur, seleksi fitur, dan proses klasifikasi. Fitur set yang diperoleh dari rekaman ECG berupa variabel-variabel yang diperoleh dari domain waktu, domain frekuensi, dan analisis nonlinear. Pengujian menunjukkan metode AdaBoost memiliki nilai kinerja paling tinggi dibandingkan dengan SVM, k-NN, dan NN. Metode AdaBoost secara berurutan memiliki kinerja classification accuracy, F1 score, precision, recall, sensitivity, dan specificity sebesar 90,1%, 90,1%, 90,3%, 90,1%, 86,9%, dan 93,3%.

show abstract

Improving the accuracy of EEG emotion recognition by combining valence lateralization and ensemble learning with tuning parameters

Cited by 55 publications

References 40 publications

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

Application of Electroencephalography-Based Machine Learning in Emotion Recognition: A Review

The challenges of emotion recognition methods based on electroencephalogram signals: a literature review

Asesmen ECG-Apnea Satu Sadapan untuk Peningkatan Akurasi Klasifikasi Gangguan Tidur Berdasarkan AdaBoost

Contact Info

Product

Resources

About