Optimal channel-based sparse time-frequency blocks common spatial pattern feature extraction method for motor imagery classification

Yin, Xu; Meng, Ming; She, Qingshan; Gao, Yunhe; Luo, Zhizeng

doi:10.3934/mbe.2021213

Cited by 14 publications

(11 citation statements)

References 40 publications

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“…In addition, in the process of image illumination and radiation transformation, the robustness of the algorithm can also be greatly enhanced. Among the registration algorithms based on feature points, the most representative algorithms mainly include SUSAN algorithm, Harris algorithm, phase consistency algorithm, SIFT algorithm, and SURF algorithm [ 14 ].…”

Section: The Basic Theory Of the Collection Of Ceramic Sculpture Patt...mentioning

confidence: 99%

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

Wang

2022

International Journal of Analytical Chemistry

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In order to solve the research on the extraction of ceramic sculpture artwork patterns, and, in the process of image reproduction, the problem of too few feature points in the object image, the author proposes an image stitching algorithm that combines SIFT and MSER algorithms. After comprehensively analyzing the principles, advantages, and disadvantages of the current main image stitching methods, in terms of feature matching, based on the K-D tree search algorithm, the improved BBF algorithm is used to improve the search efficiency of feature points. In order to remove the possible cracks in the stitching process, an improved multiband fusion algorithm is used to seamlessly stitch the registered images. The results show that the feature points detected by the one-dimensional normal distribution algorithm are on average 0.1%, 0.5%, 1.7%, 4.4%, and 9.2%. The algorithm combining SIFT and MSER to extract feature points can reach 3.6%, 4.6%, 8.4%, 15%, and 19.1%. The experimental results show that the algorithm proposed by the author can extract more image feature points to facilitate later image registration. The image blur phenomenon in the original image fusion algorithm is solved, and a complete and clear two-dimensional plane pattern is finally obtained.

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Section: The Basic Theory Of the Collection Of Ceramic Sculpture Patt...mentioning

confidence: 99%

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

Wang

2022

International Journal of Analytical Chemistry

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“…Third, the steps of channel and time window selection were consistent among participants, guaranteeing better generalizability. CSPs have been widely used in motor imagery BCI tasks (Nguyen et al, 2018 ; Yin et al, 2021 ). These CSP‐based classification methods usually include complex selection procedures of channels (Yin et al, 2021 ) and time windows (Selim et al, 2018 ).…”

Section: Discussionmentioning

confidence: 99%

“…CSPs have been widely used in motor imagery BCI tasks (Nguyen et al, 2018 ; Yin et al, 2021 ). These CSP‐based classification methods usually include complex selection procedures of channels (Yin et al, 2021 ) and time windows (Selim et al, 2018 ). These individualized steps select different channels and windows for different individuals to reach higher offline classification accuracy but also require more time for the training process and have increased individual differences, which limits their application in online prediction situations.…”

Section: Discussionmentioning

confidence: 99%

Predicting rock–paper–scissors choices based on single‐trial EEG signals

He,

Cui,

Zhang

et al. 2023

PsyCh Journal

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Decision prediction based on neurophysiological signals is of great application value in many real‐life situations, especially in human–AI collaboration or counteraction. Single‐trial analysis of electroencephalogram (EEG) signals is a very valuable step in the development of an online decision‐prediction system. However, previous EEG‐based decision‐prediction methods focused mainly on averaged EEG signals of all decision‐making trials to predict an individual's general decision tendency (e.g., risk seeking or aversion) over a period rather than on a specific decision response in a single trial. In the present study, we used a rock–paper–scissors game, which is a common multichoice decision‐making task, to explore how to predict participants' single‐trial choice with EEG signals. Forty participants, comprising 20 females and 20 males, played the game with a computer player for 330 trials. Considering that the decision‐making process of this game involves multiple brain regions and neural networks, we proposed a new algorithm named common spatial pattern‐attractor metagene (CSP‐AM) to extract CSP features from different frequency bands of EEG signals that occurred during decision making. The results showed that a multilayer perceptron classifier achieved an accuracy significantly exceeding the chance level among 88.57% (31 of 35) of participants, verifying the classification ability of CSP features in multichoice decision‐making prediction. We believe that the CSP‐AM algorithm could be used in the development of proactive AI systems.

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“…A detailed table with information on the directions and performance of each paper can be found in the Supplementary material . The following reviewed papers are presented in ascending order of their published date (Aellen et al, 2021 ; Asheri et al, 2021 ; Ashwini and Nagaraj, 2021 ; Awais et al, 2021 ; Cai et al, 2021 ; Dagdevir and Tokmakci, 2021 ; De Venuto and Mezzina, 2021 ; Du et al, 2021 ; Fan et al, 2021 , 2022 ; Ferracuti et al, 2021 ; Gao N. et al, 2021 ; Gao Z. et al, 2021 ; Gaur et al, 2021 ; Lashgari et al, 2021 ; Lian et al, 2021 ; Liu and Jin, 2021 ; Liu and Yang, 2021 ; Liu et al, 2021 ; Qi et al, 2021 ; Rashid et al, 2021 ; Sun et al, 2021 ; Varsehi and Firoozabadi, 2021 ; Vega et al, 2021 ; Vorontsova et al, 2021 ; Wahid and Tafreshi, 2021 ; Wang and Quan, 2021 ; Xu C. et al, 2021 ; Xu F. et al, 2021 ; Yin et al, 2021 ; Zhang K. et al, 2021 ; Zhang Y. et al, 2021 ; Algarni et al, 2022 ; Ali et al, 2022 ; Asadzadeh et al, 2022 ; Ayoobi and Sadeghian, 2022 ; Bagchi and Bathula, 2022 ; Chang et al, 2022 ; Chen J. et al, 2022 ; Chen L. et al, 2022 ; Cui et al, 2022 ; Geng et al, 2022 ; Islam et al, 2022 ; Jia et al, 2022 ; Kim et al, 2022 ; Ko et al, 2022 ; Li and Sun, 2022 ; Li H. et al, 2022 ; Lin et al, 2022 ; Li Q. et al, 2022 ; Lu et al, 2022 ; Ma et al, 2022 ; Mattioli et al, 2022 ;...…”

Section: Search Methods and Reviewed Tablementioning

confidence: 99%

Survey on the research direction of EEG-based signal processing

Sun

Mou

2023

Front. Neurosci.

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Electroencephalography (EEG) is increasingly important in Brain-Computer Interface (BCI) systems due to its portability and simplicity. In this paper, we provide a comprehensive review of research on EEG signal processing techniques since 2021, with a focus on preprocessing, feature extraction, and classification methods. We analyzed 61 research articles retrieved from academic search engines, including CNKI, PubMed, Nature, IEEE Xplore, and Science Direct. For preprocessing, we focus on innovatively proposed preprocessing methods, channel selection, and data augmentation. Data augmentation is classified into conventional methods (sliding windows, segmentation and recombination, and noise injection) and deep learning methods [Generative Adversarial Networks (GAN) and Variation AutoEncoder (VAE)]. We also pay attention to the application of deep learning, and multi-method fusion approaches, including both conventional algorithm fusion and fusion between conventional algorithms and deep learning. Our analysis identifies 35 (57.4%), 18 (29.5%), and 37 (60.7%) studies in the directions of preprocessing, feature extraction, and classification, respectively. We find that preprocessing methods have become widely used in EEG classification (96.7% of reviewed papers) and comparative experiments have been conducted in some studies to validate preprocessing. We also discussed the adoption of channel selection and data augmentation and concluded several mentionable matters about data augmentation. Furthermore, deep learning methods have shown great promise in EEG classification, with Convolutional Neural Networks (CNNs) being the main structure of deep neural networks (92.3% of deep learning papers). We summarize and analyze several innovative neural networks, including CNNs and multi-structure fusion. However, we also identified several problems and limitations of current deep learning techniques in EEG classification, including inappropriate input, low cross-subject accuracy, unbalanced between parameters and time costs, and a lack of interpretability. Finally, we highlight the emerging trend of multi-method fusion approaches (49.2% of reviewed papers) and analyze the data and some examples. We also provide insights into some challenges of multi-method fusion. Our review lays a foundation for future studies to improve EEG classification performance.

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Optimal channel-based sparse time-frequency blocks common spatial pattern feature extraction method for motor imagery classification

Cited by 14 publications

References 40 publications

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

A Systematic Study on the Extraction and Image Reproduction of Ceramic Sculpture Artworks

Predicting rock–paper–scissors choices based on single‐trial EEG signals

Survey on the research direction of EEG-based signal processing

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