Emotion classification using minimal EEG channels and frequency bands

Jatupaiboon, Noppadon; Pan-ngum, Setha; Israsena, Pasin

doi:10.1109/jcsse.2013.6567313

Cited by 118 publications

(66 citation statements)

References 14 publications

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“…Another study in [97] has aimed to show that the classification accuracy increases with the gradual rising of the number of electrodes. On the other hand, several researches concerned with reducing the number of electrodes to decrease the features' size or enhance user's acceptability have taken place in [98][99][100].…”

Section: Intracorticalmentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

459

247

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Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabilitation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding systems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain signals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed.Ó 2015 Production and hosting by Elsevier B.V. on behalf

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Section: Intracorticalmentioning

confidence: 99%

Brain computer interfacing: Applications and challenges

Abdulkader

Atia

Mostafa

2015

Egyptian Informatics Journal

459

247

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“…Jatupaiboon et al [69] proposed a method to classify two emotions based on EEG signals, which are positive and negative emotions elicited by pictures. They extracted the power spectrum from five bands and used SVM as a classifier in a wrapper channel selection evaluation approach.…”

Section: Channel Selection For Emotion Classificationmentioning

confidence: 99%

A review of channel selection algorithms for EEG signal processing

Alotaiby

El‐Samie

Alshebeili

et al. 2015

EURASIP J. Adv. Signal Process.

349

181

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Digital processing of electroencephalography (EEG) signals has now been popularly used in a wide variety of applications such as seizure detection/prediction, motor imagery classification, mental task classification, emotion classification, sleep state classification, and drug effects diagnosis. With the large number of EEG channels acquired, it has become apparent that efficient channel selection algorithms are needed with varying importance from one application to another. The main purpose of the channel selection process is threefold: (i) to reduce the computational complexity of any processing task performed on EEG signals by selecting the relevant channels and hence extracting the features of major importance, (ii) to reduce the amount of overfitting that may arise due to the utilization of unnecessary channels, for the purpose of improving the performance, and (iii) to reduce the setup time in some applications. Signal processing tools such as time-domain analysis, power spectral estimation, and wavelet transform have been used for feature extraction and hence for channel selection in most of channel selection algorithms. In addition, different evaluation approaches such as filtering, wrapper, embedded, hybrid, and human-based techniques have been widely used for the evaluation of the selected subset of channels. In this paper, we survey the recent developments in the field of EEG channel selection methods along with their applications and classify these methods according to the evaluation approach.

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“…It has applied in many researches, particularly in emotion prediction. There are five major brain waves: delta (0.5-4 Hz), theta (4-7.5 Hz), alpha (8)(9)(10)(11)(12)(13), beta (14-26 Hz), and gamma (30+ Hz) [1]. Note that each work may define these brain wave ranges differently [2].…”

Section: Introductionmentioning

confidence: 99%

An evaluation of feature extraction in EEG-based emotion prediction with support vector machines

Wichakam

Vateekul

2014

2014 11th International Joint Conference on Computer Science and Software Engineering (JCSSE)

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Electroencephalograph (EEG) data is a recording of brain electrical activities, which is commonly used in emotion prediction. To obtain promising accuracy, it is important to perform a suitable data preprocessing; however, different works employed different procedures and features. In this paper, we aim to investigate various feature extraction techniques forEEG signals. To obtain the best choice, there are fourfactors investigatedin the experiment: (i) the number of channels, (ii) signal transformation methods, (iii) feature representations, and (iv) feature transformation techniques. Support Vector Machine (SVM) is chosen to be our baseline classifier due to its promising performance. The experimentswere conducted on the DEAP benchmark dataset. The results showed that the prediction on EEG signals from 10 channels represented by the bandpower oneminute features gave the best accuracy and F1.

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Emotion classification using minimal EEG channels and frequency bands

Cited by 118 publications

References 14 publications

Brain computer interfacing: Applications and challenges

Brain computer interfacing: Applications and challenges

A review of channel selection algorithms for EEG signal processing

An evaluation of feature extraction in EEG-based emotion prediction with support vector machines

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