Emotional state detection based on common spatial patterns of EEG

Başar, Merve Doğruyol; Duru, Adil Deniz; Akan, Aydın

doi:10.1007/s11760-019-01580-8

Cited by 32 publications

(17 citation statements)

References 27 publications

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“…In order to adapt to scale transformation, feature points need to be detected in all image scales. erefore, it is necessary to establish the scale space, and the Gaussian kernel function [11] is the only smoothing function in the scale space, so the Gaussian kernel function can be used to build the scale space. e scale transformation of an image can be expressed as follows:…”

Section: Sift Feature Point Detectionmentioning

confidence: 99%

Research on Sports Video Image Based on Clustering Extraction

2021

Mathematical Problems in Engineering

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Today, with the continuous sports events, the major sports events are also loved by the majority of the audience, so the analysis of the video data of the games has higher research value and application value. This paper takes the video of volleyball, tennis, baseball, and water polo as the research background and analyses the video images of these four sports events. Firstly, image graying, image denoising, and image binarization are used to preprocess the images of the four sports events. Secondly, feature points are used to detect the four sports events. According to the characteristics of these four sports events, SIFT algorithm is adopted to detect the good performance of SIFT feature points in feature matching. According to the simulation experiment, it can be seen that the SIFT algorithm can effectively detect football and have good anti-interference. For sports recognition, this document adopts the frame cross-sectional cumulative algorithm. Through simulation experiments, it can be seen that the grouping algorithm can achieve a recognition rate of more than 80% for sporting events, so it can be seen that the recognition algorithm is suitable for recognizing sports events videos.

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Section: Sift Feature Point Detectionmentioning

confidence: 99%

Research on Sports Video Image Based on Clustering Extraction

2021

Mathematical Problems in Engineering

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“…Due to the influence of noise and other factors in the imaging process, there are many small noises or small areas in the main area of the segmented stadium, so it is impossible to remove them directly by removing small areas. According to the nature of noise, the method of eliminating block motion noise can solve this problem well [13,14]. e blocking operation used in this paper is noise algorithm which is as follows: the image is divided into n × n blocks, and the statistical value of stadium color in each block is greater than a certain percentage; the block is regarded as stadium; otherwise, the stadium block is considered.…”

Section: Automatic Extraction Of Stadium Main Area Based On Histogrammentioning

confidence: 99%

[Retracted] Detection and Adaptive Video Processing of Hyperopia Scene in Sports Video

Qing-jie

Dong

2021

Complexity

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In the research of motion video, the existing target detection methods are susceptible to changes in the motion video scene and cannot accurately detect the motion state of the target. Moving target detection technology is an important branch of computer vision technology. Its function is to implement real-time monitoring, real-time video capture, and detection of objects in the target area and store information that users are interested in as an important basis for exercise. This article focuses on how to efficiently perform motion detection on real-time video. By introducing the mathematical model of image processing, the traditional motion detection algorithm is improved and the improved motion detection algorithm is implemented in the system. This article combines the advantages of the widely used frame difference method, target detection algorithm, and background difference method and introduces the moving object detection method combining these two algorithms. When using Gaussian mixture model for modeling, improve the parts with differences, and keep the unmatched Gaussian distribution so that the modeling effect is similar to the actual background; the binary image is obtained through the difference between frames and the threshold, and the motion change domain is extracted through mathematical morphological filtering, and finally, the moving target is detected. The experiment proved the following: when there are more motion states, the recall rate is slightly better than that of the VIBE algorithm. It decreased about 0.05 or so, but the relative accuracy rate increased by about 0.12, and the increase ratio is significantly higher than the decrease ratio. Departments need to adopt effective target extraction methods. In order to improve the accuracy of moving target detection, this paper studies the method of background model establishment and target extraction and proposes its own improvement.

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“…A previous study 63 showed that the CSP spatial filtering method entails the relationship between EEG bands, EEG channels, neural efficiency and emotional stimuli types. It demonstrated that CSP spatial filtering gives significant values on band-channels (p < 0.004) combination.…”

Section: Features Selection and Extractionmentioning

confidence: 99%

High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

Apicella

Arpaïa

Mastrati

et al. 2021

Preprint

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An emotional-valence detection method for a very–high wearable EEG-based system is proposed. Valence detection occurs along the interval scale theorized by the circumplex model of emotions. The binary choice, positive valence vs negative valence, represents a first step towards the adoption of a metric scale with a finer resolution. Wearability is guaranteed by a wireless cap with conductive-rubber dry electrodes and 8 data acquisition channels. Experimental validation was realized on 25 volunteers without depressive disorders. The metrological reference was built by combining the rating of a standardized set of pictures from dataset Oasis and results from the Self Assessment Manikin questionnaire. Two different strategies for feature extraction were compared: (i) based on a-priory knowledge (i.e. Hemispheric Asymmetry Theories) and (ii) automated. A pipeline of a custom 12-band Filter Bank and Common Spatial Pattern algorithm is the method proposed for automated feature extraction. Four machine learning classifiers were tested for validating the proposed method in discriminating two classes (high or low emotional valence). An intra-individual average accuracy, 96.2 %, was obtained by a shallow artificial neural network, while K-Nearest Neighbors allowed obtaining 80.3 % of inter-individual accuracy. Considering the wearability, and the well-founded metrological reference, results are the state of the art in emotional valence detection.

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Emotional state detection based on common spatial patterns of EEG

Cited by 32 publications

References 27 publications

Research on Sports Video Image Based on Clustering Extraction

Research on Sports Video Image Based on Clustering Extraction

[Retracted] Detection and Adaptive Video Processing of Hyperopia Scene in Sports Video

High-wearable EEG-Based Detection of Emotional Valence for Scientific Measurement of Emotions

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