Motor Imagery EEG Decoding Based on New Spatial-Frequency Feature and Hybrid Feature Selection Method

Tang, Yuan; Zhao, Zining; Zhang, Shaorong; Li, Zhi; Mo, Yun; Guo, Yan

doi:10.1155/2022/2856818

Cited by 6 publications

(4 citation statements)

References 36 publications

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“… Accuracy comparison of different methods on the BCI competition IV dataset 2a. Each label on the vertical axis represents a method in a study, which is from Gaur et al ( 2021 ); Lashgari et al ( 2021 ); Lian et al ( 2021 ); Liu and Yang ( 2021 ); Liu et al ( 2021 ); Qi et al ( 2021 ); Ali et al ( 2022 ); Ayoobi and Sadeghian ( 2022 ); Chang et al ( 2022 ); Chen L. et al ( 2022 ); Ko et al ( 2022 ); Li and Sun ( 2022 ); Li H. et al ( 2022 ), and Tang et al ( 2022 ), from top to bottom, respectively. …”

Section: Resultsmentioning

confidence: 99%

“…The innovative methods mean new methods proposed in the papers, whose accuracy increments are denoted by orange bars. Each label on the vertical axis represents a method in a study, which is from Du et al ( 2021 ); Gao N. et al ( 2021 ); Qi et al ( 2021 ); Rashid et al ( 2021 ); Wang and Quan ( 2021 ); Xu C. et al ( 2021 ); Yin et al ( 2021 ); Zhang Y. et al ( 2021 ); Algarni et al ( 2022 ); Cui et al ( 2022 ); Jia et al ( 2022 ); Ma et al ( 2022 ); Pei et al ( 2022 ), and Tang et al ( 2022 ), from top to bottom, respectively.…”

Section: Resultsmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Survey on the research direction of EEG-based signal processing

Sun

Mou

2023

Front. Neurosci.

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“…It helps to minimize fitting challenges, decreases adaption efficiency on test datasets, shrinks the amount of time required for training, and minimizes the model's interpretability, making it an essential phase in the procedure of ML. There are three basic kinds of methods for selecting features, which may be broken down as follows: a technique of selection that is reliant on either the filters, the wrappers, or the embedding features [47]. The combined feature selection technique makes it possible to design a model without the need to implement any additional feature selection techniques.…”

Section: Hybrid Model With Machine and Deep Learningmentioning

confidence: 99%

Enhancing Intrusion Detection Systems With a Novel Hybrid Learning-Based Framework

2023

ecb

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The proliferation of cyber threats and the increasing sophistication of attacks have necessitated the development of robust Intrusion Detection Systems (IDS) to protect sensitive information and network infrastructures. Traditional IDS methods often struggle to effectively detect and respond to emerging threats due to their reliance on static rule-based approaches. To address these limitations, this paper presents a simplified and novel hybrid learning-based framework for strengthening IDS. The proposed framework integrates the strengths of two prominent Machine Learning (ML) techniques: Deep Learning (DL) and Ensemble Learning. DL models, specifically Convolutional Neural Networks (CNNs), are employed to extract high-level features from network traffic data, enabling the system to automatically learn and adapt to complex attack patterns. Ensemble learning is then utilized to combine multiple classifiers, leveraging the diversity of their decision boundaries, and enhancing the overall detection performance. The experimental results demonstrate that the suggested framework achieves superior detection accuracy, outperforming traditional rule-based approaches as well as standalone DL and ensemble learning models.

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“…Specifically, Zhang et al ( 2021a ) have extracted time-frequency features through wavelet transformation and selected crucial EEG channels via squeeze-and-excitation blocks. In addition, recent studies focusing on EEG feature selection have been actively considering various combinations of the spatial, temporal, and spectral domains through a range of approaches (Abbas and Khan, 2018 ; Liu et al, 2022 ; Sadiq et al, 2022 ; Tang et al, 2022 ; Luo, 2023 ; Meng et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets

Nam

Kim

Choi

et al. 2023

Front. Hum. Neurosci.

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IntroductionThe brain-computer interface (BCI) allows individuals to control external devices using their neural signals. One popular BCI paradigm is motor imagery (MI), which involves imagining movements to induce neural signals that can be decoded to control devices according to the user's intention. Electroencephalography (EEG) is frequently used for acquiring neural signals from the brain in the fields of MI-BCI due to its non-invasiveness and high temporal resolution. However, EEG signals can be affected by noise and artifacts, and patterns of EEG signals vary across different subjects. Therefore, selecting the most informative features is one of the essential processes to enhance classification performance in MI-BCI.MethodsIn this study, we design a layer-wise relevance propagation (LRP)-based feature selection method which can be easily integrated into deep learning (DL)-based models. We assess its effectiveness for reliable class-discriminative EEG feature selection on two different publicly available EEG datasets with various DL-based backbone models in the subject-dependent scenario.Results and discussionThe results show that LRP-based feature selection enhances the performance for MI classification on both datasets for all DL-based backbone models. Based on our analysis, we believe that it can broad its capability to different research domains.

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Motor Imagery EEG Decoding Based on New Spatial-Frequency Feature and Hybrid Feature Selection Method

Cited by 6 publications

References 36 publications

Survey on the research direction of EEG-based signal processing

Survey on the research direction of EEG-based signal processing

Enhancing Intrusion Detection Systems With a Novel Hybrid Learning-Based Framework

The effects of layer-wise relevance propagation-based feature selection for EEG classification: a comparative study on multiple datasets

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