Cognitive Imagery Classification of EEG Signals using CSP-based Feature Selection Method

Hooda, Neha; Kumar, Neelesh

doi:10.1080/02564602.2019.1620138

Cited by 14 publications

(3 citation statements)

References 62 publications

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“…Third, issues concerning feature extraction and selection were increasingly of concern to scholars (e.g., [247][248][249][250][251][252][253]). Recently, combining EEG signal feature extraction and classification methods have been widely used to identify mild cognitive impairments [254], driving fatigue state [250], and familiar and unfamiliar persons [252].…”

Section: Latest Research Concerning Ai-enhanced Eeg Analysismentioning

confidence: 99%

“…In addition, feature selection is an important yet challenging issue, since there are numerous features, a small amount of clinical data exists, and there are many similarities between selected features. Recent studies have applied techniques such as Rényi minentropy-based feature selection [249], common spatial pattern-based feature selection [251], and principal component analysis-based feature selection [253].…”

Section: Latest Research Concerning Ai-enhanced Eeg Analysismentioning

confidence: 99%

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Global research on artificial intelligence-enhanced human electroencephalogram analysis

Chen

Tao

Wang

et al. 2021

Neural Comput & Applic

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The application of artificial intelligence (AI) technologies in assisting human electroencephalogram (EEG) analysis has become an active scientific field. This study aims to present a comprehensive review of the research field of AI-enhanced human EEG analysis. Using bibliometrics and topic modeling, research articles concerning AI-enhanced human EEG analysis collected from the Web of Science database during the period 2009-2018 were analyzed. After examining 2053 research articles published around the world, it was found that the annual number of articles had significantly grown from 78 to 468, with the USA and China being the most influential and prolific. The results of the keyword analysis showed that ''electroencephalogram,'' ''brain-computer interface,'' ''classification,'' ''support vector machine,'' ''electroencephalography,'' and ''signal'' were the most frequently used. The results of topic modeling and evolution analyses highlighted several important issues, including epileptic seizure detection, brain-machine interface, EEG classification, mental disorders, emotion, and alcoholism and anesthesia. The findings suggest that such visualization and analysis of the research articles could provide a comprehensive overview of the field for communities of practice and inquiry worldwide.

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Section: Latest Research Concerning Ai-enhanced Eeg Analysismentioning

confidence: 99%

Section: Latest Research Concerning Ai-enhanced Eeg Analysismentioning

confidence: 99%

Global research on artificial intelligence-enhanced human electroencephalogram analysis

Chen

Tao

Wang

et al. 2021

Neural Comput & Applic

View full text Add to dashboard Cite

show abstract

“…Over the past few years, there have been plenty of manual methods devoted to EEG classification tasks [21][22][23][24]. For example, Hooda et al [25] utilized common spatial patterns (CSPs) algorithm to extract features from pre-processed EEG signals and a support vector machine (SVM) for classification. With the introduction of deep learning into EEG, some studies have been explored to decode complex cognitive events.…”

Section: Introductionmentioning

confidence: 99%

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

Shen,

Li,

Pan

et al. 2023

J. Neural Eng.

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Objective. Human beings perceive stereoscopic image quality through the cerebral visual cortex, which is a complex brain activity. As a solution, the quality of stereoscopic images can be evaluated more accurately by attempting to replicate the human perception from electroencephalogram (EEG) signals on image quality in a machine, which is different from previous stereoscopic image quality assessment methods focused only on the extraction of image features. Approach. Our proposed method is based on a novel image-to-brain (I2B) cross-modality model including a spatial-temporal EEG encoder (STEE) and an I2B deep convolutional generative adversarial network (I2B-DCGAN). Specifically, the EEG representations are first learned by STEE as real samples of I2B-DCGAN, which is designed to extract both quality and semantic features from the stereoscopic images by a semantic-guided image encoder, and utilize a generator to conditionally create the corresponding EEG features for images. Finally, the generated EEG features are classified to predict the image perceptual quality level. Main results. Extensive experimental results on the collected brain-visual multimodal stereoscopic image quality ranking database, demonstrate that the proposed I2B cross-modality model can better emulate the visual perception mechanism of the human brain and outperform the other methods by achieving an average accuracy of 95.95 % . Significance. The proposed method can convert the learned stereoscopic image features into brain representations without EEG signals during testing. Further experiments verify that the proposed method has good generalization ability on new datasets and the potential for practical applications.

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Paper-Based Electronics for Brain-Machine Interface Home Supercomputer

Lori

Pais-Vieira

Curado

et al. 2021

Advances in Intelligent Systems and Computing

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Cognitive Imagery Classification of EEG Signals using CSP-based Feature Selection Method

Cited by 14 publications

References 62 publications

Global research on artificial intelligence-enhanced human electroencephalogram analysis

Global research on artificial intelligence-enhanced human electroencephalogram analysis

Image2Brain: a cross-modality model for blind stereoscopic image quality ranking

Paper-Based Electronics for Brain-Machine Interface Home Supercomputer

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