Journal ArticleDOI
Extended ICA and M-CSP with BiLSTM towards improved classification of EEG signals
TLDR
A Multiclass Common Spatial Pattern-based moving window technique is proposed here to obtain the most distinguishable time segment of EEG trials, and BiLSTM is used to improve classification results.About:
This article is published in Soft Computing.The article was published on 2022-02-23. It has received 7 citations till now. The article focuses on the topics: Computer science & Electroencephalography.read more
Citations
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Journal ArticleDOI
Editorial on decision support system for development of intelligent applications
TL;DR: This special issue considers decision support systems for intelligent applications for numerous types of industry sectors, which assist mid- and upper-level managers in making decisions regarding problems that change quickly and are challenging to define.
Proceedings ArticleDOI
EEG-based Emotion Classification - A Theoretical Perusal of Deep Learning Methods
TL;DR: In this paper , the authors reviewed the modern developments in the research area of emotion identification using deep learning methods, focusing on both feature extraction and classification methods, and presented the comparison of the accuracies obtained by different models.
Journal ArticleDOI
Capped L21-norm-based common spatial patterns for EEG signals classification applicable to BCI systems
Proceedings ArticleDOI
EEG-based Emotion Classification - A Theoretical Perusal of Deep Learning Methods
TL;DR: In this paper , the authors reviewed the modern developments in the research area of emotion identification using deep learning methods, focusing on both feature extraction and classification methods, and presented the comparison of the accuracies obtained by different models.
Proceedings ArticleDOI
Removal of Ocular Artifacts in EEG Using Deep Learning
TL;DR: In this article , a novel ocular artifact removal method is presented by developing bidirectional long-short term memory (BiLSTM)-based deep learning (DL) models, which are then fed to features extracted from augmented signals using highly-localized time-frequency (TF) coefficients obtained by wavelet synchrosqueezed transform (WSST).
References
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Journal ArticleDOI
DEAP: A Database for Emotion Analysis ;Using Physiological Signals
Sander Koelstra,Christian Mühl,Mohammad Soleymani,Jong-Seok Lee,Ashkan Yazdani,Touradj Ebrahimi,Thierry Pun,Anton Nijholt,Ioannis Patras +8 more
TL;DR: A multimodal data set for the analysis of human affective states was presented and a novel method for stimuli selection is proposed using retrieval by affective tags from the last.fm website, video highlight detection, and an online assessment tool.
Journal ArticleDOI
Filter Bank Common Spatial Pattern Algorithm on BCI Competition IV Datasets 2a and 2b.
TL;DR: The FBCSP algorithm performed relatively the best among the other submitted algorithms and yielded a mean kappa value of 0.569 and 0.600 across all subjects in Datasets 2a and 2b of the BCI Competition IV.
Journal ArticleDOI
EEG artifact removal?state-of-the-art and guidelines
TL;DR: This paper presents an extensive review on the artifact removal algorithms used to remove the main sources of interference encountered in the electroencephalogram (EEG), specifically ocular, muscular and cardiac artifacts, and concludes that the safest approach is to correct the measured EEG using independent component analysis-to be precise, an algorithm based on second-order statistics such as second- order blind identification (SOBI).
Journal ArticleDOI
EmotionMeter: A Multimodal Framework for Recognizing Human Emotions
TL;DR: The experimental results demonstrate that modality fusion with multimodal deep neural networks can significantly enhance the performance compared with a single modality, and the best mean accuracy of 85.11% is achieved for four emotions.