- 标题
- 摘要
- 关键词
- 实验方案
- 产品
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[IEEE 2019 Compound Semiconductor Week (CSW) - Nara, Japan (2019.5.19-2019.5.23)] 2019 Compound Semiconductor Week (CSW) - Growth of InGaAs solar cells on InP(001) miscut substrates using solid-source molecular beam epitaxy
摘要： In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s T 2 statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.
关键词： feature extraction,linear prediction,Brain-computer interface,orthogonal transform,channel selection
更新于2025-09-23 15:21:01
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[IEEE 2019 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW) - London, United Kingdom (2019.6.18-2019.6.21)] 2019 IEEE PELS Workshop on Emerging Technologies: Wireless Power Transfer (WoW) - Impacts of Coupling Plates on Single-Switch Capacitive-Coupled WPT Systems
摘要： In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s T 2 statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.
关键词： linear prediction,Brain-computer interface,channel selection,feature extraction,orthogonal transform
更新于2025-09-19 17:13:59
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[IEEE 2019 Photonics North (PN) - Quebec City, QC, Canada (2019.5.21-2019.5.23)] 2019 Photonics North (PN) - Optimizing Bifacial Silicon Heterojunction Solar Cells for High-Latitude
摘要： In this paper, we present a new motor imagery classification method in the context of electroencephalography (EEG)-based brain–computer interface (BCI). This method uses a signal-dependent orthogonal transform, referred to as linear prediction singular value decomposition (LP-SVD), for feature extraction. The transform defines the mapping as the left singular vectors of the LP coefficient filter impulse response matrix. Using a logistic tree-based model classifier; the extracted features are classified into one of four motor imagery movements. The proposed approach was first benchmarked against two related state-of-the-art feature extraction approaches, namely, discrete cosine transform (DCT) and adaptive autoregressive (AAR)-based methods. By achieving an accuracy of 67.35%, the LP-SVD approach outperformed the other approaches by large margins (25% compared with DCT and 6 % compared with AAR-based methods). To further improve the discriminatory capability of the extracted features and reduce the computational complexity, we enlarged the extracted feature subset by incorporating two extra features, namely, Q- and the Hotelling’s T 2 statistics of the transformed EEG and introduced a new EEG channel selection method. The performance of the EEG classification based on the expanded feature set and channel selection method was compared with that of a number of the state-of-the-art classification methods previously reported with the BCI IIIa competition data set. Our method came second with an average accuracy of 81.38%.
关键词： Brain-computer interface,feature extraction,linear prediction,orthogonal transform,channel selection
更新于2025-09-19 17:13:59
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[IEEE 2018 26th European Signal Processing Conference (EUSIPCO) - Roma, Italy (2018.9.3-2018.9.7)] 2018 26th European Signal Processing Conference (EUSIPCO) - Light Field Compression of HDCA Images Combining Linear Prediction and JPEG 2000
摘要： We have proposed under JPEG Pleno standardization activities a scheme for lenslet image compression, where the regularities and similarities existing between neighbor angular views were successfully exploited, achieving competitive results in the JPEG Pleno core experiments using lenslet data. This paper proposes improvements on our previous scheme of light field compression, making our approach more suitable for compression of light fields acquired with dense camera arrays, where the disparities between farthest views can reach several hundreds of pixels. We review the functional blocks of the compression algorithm, replacing and modifying some of the functionality with more advanced and efficient solutions. Based on our submission to the JPEG Pleno core experiments, we present and discuss our results obtained on the Fraunhofer HDCA dataset. Additionally, we present a new view merging algorithm which substantially increases the PSNR at all bit rates.
关键词： HDCA images,JPEG 2000,Linear Prediction,Light field compression
更新于2025-09-04 15:30:14