A Survey on Feature Extraction Methods for EEG Based Emotion Recognition

Phadikar, Souvik; Sinha, Nidul; Ghosh, Rajdeep

doi:10.1007/978-3-030-42363-6_5

Cited by 9 publications

(4 citation statements)

References 42 publications

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“…Though Hilbert–Huang transformation (HHT) (Huang et al, 1998 ; Huang, 2014 ) is a popular tool for feature extraction in classifying emotion from EEG (Uzun et al, 2012 ; Vanitha and Krishnan, 2017 ; Phadikar et al, 2019 ; Chen et al, 2020 ), the only work that makes use of HHT for classifying imagined speech is the work by Deng et al ( 2010 ). Hilbert spectrum was extracted from the four primary SOBI (second-order blind identification) components and multiclass linear discriminant analysis (LDA) was used as the classifier.…”

Section: Feature Extraction and Classificationmentioning

confidence: 99%

Decoding Covert Speech From EEG-A Comprehensive Review

Panachakel

Ramakrishnan

2021

Front. Neurosci.

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Over the past decade, many researchers have come up with different implementations of systems for decoding covert or imagined speech from EEG (electroencephalogram). They differ from each other in several aspects, from data acquisition to machine learning algorithms, due to which, a comparison between different implementations is often difficult. This review article puts together all the relevant works published in the last decade on decoding imagined speech from EEG into a single framework. Every important aspect of designing such a system, such as selection of words to be imagined, number of electrodes to be recorded, temporal and spatial filtering, feature extraction and classifier are reviewed. This helps a researcher to compare the relative merits and demerits of the different approaches and choose the one that is most optimal. Speech being the most natural form of communication which human beings acquire even without formal education, imagined speech is an ideal choice of prompt for evoking brain activity patterns for a BCI (brain-computer interface) system, although the research on developing real-time (online) speech imagery based BCI systems is still in its infancy. Covert speech based BCI can help people with disabilities to improve their quality of life. It can also be used for covert communication in environments that do not support vocal communication. This paper also discusses some future directions, which will aid the deployment of speech imagery based BCI for practical applications, rather than only for laboratory experiments.

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Section: Feature Extraction and Classificationmentioning

confidence: 99%

Decoding Covert Speech From EEG-A Comprehensive Review

Panachakel

Ramakrishnan

2021

Front. Neurosci.

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“…Since its involvement in successfully executing the classification stage at discriminating mental states, the EEG signal feature extraction method is one of the essential components of a BCI system. According to [267] on EEG, three types of feature extraction are discussed in detail in the following sections. These features are the time domain, the frequency domain, and the time-frequency domain.…”

Section: Eeg-based Feature Extractionmentioning

confidence: 99%

Brain-Computer Interface: Advancement and Challenges

Mridha

Das

Kabir

et al. 2021

Sensors

123

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Brain-Computer Interface (BCI) is an advanced and multidisciplinary active research domain based on neuroscience, signal processing, biomedical sensors, hardware, etc. Since the last decades, several groundbreaking research has been conducted in this domain. Still, no comprehensive review that covers the BCI domain completely has been conducted yet. Hence, a comprehensive overview of the BCI domain is presented in this study. This study covers several applications of BCI and upholds the significance of this domain. Then, each element of BCI systems, including techniques, datasets, feature extraction methods, evaluation measurement matrices, existing BCI algorithms, and classifiers, are explained concisely. In addition, a brief overview of the technologies or hardware, mostly sensors used in BCI, is appended. Finally, the paper investigates several unsolved challenges of the BCI and explains them with possible solutions.

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“…Some of the feature extraction methods available in the literature are the asymmetry measure [ 16 ], power spectral density (PSD) [ 14 ], differential entropy (DE) [ 16 ], wavelet transform [ 22 , 29 , 30 ], higher-order crossings [ 21 ], common spatial patterns [ 15 ], asymmetry index [ 31 ], differential asymmetry (DASM), relative asymmetry (RASM), and differential caudality (DCAU) [ 25 ]. Most feature extraction methods are manual and the selection of an appropriate method for emotion classification is still a challenging task [ 32 ].…”

Section: Introductionmentioning

confidence: 99%

Automated Feature Extraction on AsMap for Emotion Classification Using EEG

Ahmed

Sinha

Phadikar

et al. 2022

Sensors

Self Cite

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Emotion recognition using EEG has been widely studied to address the challenges associated with affective computing. Using manual feature extraction methods on EEG signals results in sub-optimal performance by the learning models. With the advancements in deep learning as a tool for automated feature engineering, in this work, a hybrid of manual and automatic feature extraction methods has been proposed. The asymmetry in different brain regions is captured in a 2D vector, termed the AsMap, from the differential entropy features of EEG signals. These AsMaps are then used to extract features automatically using a convolutional neural network model. The proposed feature extraction method has been compared with differential entropy and other feature extraction methods such as relative asymmetry, differential asymmetry and differential caudality. Experiments are conducted using the SJTU emotion EEG dataset and the DEAP dataset on different classification problems based on the number of classes. Results obtained indicate that the proposed method of feature extraction results in higher classification accuracy, outperforming the other feature extraction methods. The highest classification accuracy of 97.10% is achieved on a three-class classification problem using the SJTU emotion EEG dataset. Further, this work has also assessed the impact of window size on classification accuracy.

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A Survey on Feature Extraction Methods for EEG Based Emotion Recognition

Cited by 9 publications

References 42 publications

Decoding Covert Speech From EEG-A Comprehensive Review

Decoding Covert Speech From EEG-A Comprehensive Review

Brain-Computer Interface: Advancement and Challenges

Automated Feature Extraction on AsMap for Emotion Classification Using EEG

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