A Self-Supervised Learning Based Channel Attention MLP-Mixer Network for Motor Imagery Decoding

He, Yanbin; Lu, Zhiyang; Jun, Wang; Ying, Shihui

doi:10.1109/tnsre.2022.3199363

Cited by 16 publications

(8 citation statements)

References 36 publications

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“…Convolutional Neural Network (CNN) is a multilayer perceptron (MLP) development with two-dimensional data [34]- [36]. CNN is included in this type of deep neural network because of its high network depth and application to image data.…”

Section: Cnn (Convolutional Neural Network)mentioning

confidence: 99%

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Ariessaputra,

Vidiasari,

Sasongko

et al. 2024

JOIV : Int. J. Inform. Visualization

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Sasambo batik is a traditional batik from the West Nusa Tenggara province. Sasambo itself is an abbreviation of three tribes, namely the Sasak (sa) in the Lombok Islands, the Samawa (sam), and the Mbojo (bo) tribes in Sumbawa Island. Classification of batik motifs can use image processing technology, one of which is the Convolution Neural Network (CNN) algorithm. Before entering the classification process, the batik image first undergoes image resizing. After that, proceed with the operation of the convolution, pooling, and fully connected layers. The sample image of Lombok songket motifs and Sasambo batik consists of 20 songket fabric data with the same motif and color and 14 songket data with the same motif but different colors. In addition, there are 10 data points on songket fabrics with other motifs and colors. In addition, there are 5 data points on Sasambo batik fabrics with the same motif and color and 5 data points on Sasambo batik fabrics with the same motif but different colors. The training data rotates the image by 150as many as 20 photos. Testing with motifs with the same color shows that the system's success rate is 83.85%. The highest average recognition for Sasambo batik cloth is in testing motifs with the same color for data in the database at 93.66%. The CNN modeling classification results indicate that the Sasambo batik cloth can be a reference for developing songket categorization using a website platform or the Android system.

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Section: Cnn (Convolutional Neural Network)mentioning

confidence: 99%

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Ariessaputra,

Vidiasari,

Sasongko

et al. 2024

JOIV : Int. J. Inform. Visualization

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“…EEG. In all the reviewed papers, we find 31.7% studies [20,21,[28][29][30][31][32][33][34][35][36][37][38][39][40][41][42][43] used EEG. Among these, only one of these studies used intracranial EEG (invasive), and the others used noninvasive EEG.…”

Section: Data Types Of Medical Time Seriesmentioning

confidence: 99%

“…However, this application can make big difference in rehabilitation engineering and understanding the neural mechanisms of cognitive neuroscience. Three (5.45%) of the reviewed studies [29,36,71] focused on EEG-based motor-imagery classification.…”

Section: Medical Applicationsmentioning

confidence: 99%

Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review

Liu

Alavi

Li³

et al. 2023

Sensors

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Medical time series are sequential data collected over time that measures health-related signals, such as electroencephalography (EEG), electrocardiography (ECG), and intensive care unit (ICU) readings. Analyzing medical time series and identifying the latent patterns and trends that lead to uncovering highly valuable insights for enhancing diagnosis, treatment, risk assessment, and disease progression. However, data mining in medical time series is heavily limited by the sample annotation which is time-consuming and labor-intensive, and expert-depending. To mitigate this challenge, the emerging self-supervised contrastive learning, which has shown great success since 2020, is a promising solution. Contrastive learning aims to learn representative embeddings by contrasting positive and negative samples without the requirement for explicit labels. Here, we conducted a systematic review of how contrastive learning alleviates the label scarcity in medical time series based on PRISMA standards. We searched the studies in five scientific databases (IEEE, ACM, Scopus, Google Scholar, and PubMed) and retrieved 1908 papers based on the inclusion criteria. After applying excluding criteria, and screening at title, abstract, and full text levels, we carefully reviewed 43 papers in this area. Specifically, this paper outlines the pipeline of contrastive learning, including pre-training, fine-tuning, and testing. We provide a comprehensive summary of the various augmentations applied to medical time series data, the architectures of pre-training encoders, the types of fine-tuning classifiers and clusters, and the popular contrastive loss functions. Moreover, we present an overview of the different data types used in medical time series, highlight the medical applications of interest, and provide a comprehensive table of 51 public datasets that have been utilized in this field. In addition, this paper will provide a discussion on the promising future scopes such as providing guidance for effective augmentation design, developing a unified framework for analyzing hierarchical time series, and investigating methods for processing multimodal data. Despite being in its early stages, self-supervised contrastive learning has shown great potential in overcoming the need for expert-created annotations in the research of medical time series.

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“…Out of the three selected works, two used predictive pre-task. He et al [120] pre-trained their model using forecasting as the predictive goal, while Ou et al [122] time-shuffled the EEG signal and defined an auxiliary classification task. Lotey et al [118] assessed the impact of contrastive learning for cross-section motor imagery using a SimCLR-based approach.…”

Section: B Self-supervised Learning On Eegmentioning

confidence: 99%

Applications of Self-Supervised Learning to Biomedical Signals: where are we now

Pup¹,

Atzori²

2023

Preprint

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<p>Over the last decade, deep learning applications in biomedical research have exploded, demonstrating the ability to often outperform previous machine learning approaches in various tasks. However, training deep learning models requires large amounts of data annotated by experts, whose collection is often time- and cost- prohibitive in the biomedical domain. Self-Supervised Learning (SSL) has emerged as a prominent solution for these problems, as it allows to learn powerful data representations in an unsupervised manner. Despite most applications in biomedical research targeted images, the high amount of recent works targeting biosignals can make it difficult for researchers to have a complete picture of the current situation. The aim of this paper is to outline and clarify the state of the art in the domain. The article briefly summarizes the nature and acquisition modality of biomedical signals, introduces the SSL method, and provides a complete but synthetic overview of the main works applying SSL for the analysis of biomedical signals. The analysis of the scientific literature highlights the importance of SSL, confirming its potential to improve the integration of deep learning into clinical tasks. </p>

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A Self-Supervised Learning Based Channel Attention MLP-Mixer Network for Motor Imagery Decoding

Cited by 16 publications

References 36 publications

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Classification of Lombok Songket and Sasambo Batik Motifs Using the Convolution Neural Network (CNN) Algorithm

Self-Supervised Contrastive Learning for Medical Time Series: A Systematic Review

Applications of Self-Supervised Learning to Biomedical Signals: where are we now

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