Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network

Chang, Zhanyuan; Zhang, Congcong; Li, Chuanjiang

doi:10.3390/mi13060927

Cited by 16 publications

(9 citation statements)

References 25 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%

“…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%

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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: 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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show abstract

“…For instance, it is demonstrated that CNNs are effective in terms of both decoding and visualising EEG signals, and are able to automatically learn spatial features from raw EEG data through convolutional and pooling layers, enabling accurate classification of different action categories [2]. It is also proven that employing a multiscale CNN architecture to capture both frequency and temporal information from EEG signals can lead to improved classification accuracy [3].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Comparative Analysis of CNNs and RNNs for EEG-based Motor Imagery Classification in BCIs

Lam,

Lui

2023

J Stud Res

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EEG-based motor imagery (MI) classification plays a vital role in brain-computer interface systems (BCIs) to enable the control of external devices with the human brain. However, there is currently limited research focusing on the comparison between different machine learning models for this task. This research paper aims to present a comprehensive comparative analysis of two popular deep learning architectures, Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for MI recognition with EEG data. The experiments utilised the EEG Motor Movement/Imagery Dataset v1.0.0 from PhysioNet, which contains EEG signals recorded during a variety of motor imagery tasks. The respective performances of the CNN and RNN architectures were subsequently evaluated and compared based on classification accuracy and computational efficiency. Various metrics and statistics, namely accuracy, precision, training speed, memory usage, etc., were used for assessment. The results revealed that CNN outperforms RNN in terms of accuracy, while RNN demonstrates superior computational efficiency. These findings potentially serve as a valuable guideline for researchers and practitioners in the field of BCIs, aiding them in selecting the most suitable neural network architecture for performing MI related tasks.

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“…Initially, the random weights are selected for the input to the hidden layer and the hidden to the output layer in an ANN model. Error (Er) is calculated by comparing the calculated output denoted as C Cal i and the average of the input parameters termed as C Avg i for each parameter, as shown in Equation (11). By adjusting the weights itself, the error is minimized in each iteration.…”

Section: Update the Velocity Vector (αmentioning

confidence: 99%

“…Currently, the artificial intelligence (AI) technique is utilized for healthcare data analysis especially for the cardiogenic issues [10]. The machine mimics the way a human observes, interprets, evaluates, and makes decisions based on the trained data [11]. Powered with multiple supervised and unsupervised learnable algorithms, AI replaces traditional rule-based strategies with data-driven approaches and is capable of learning from the positive and negative experiences.…”

Section: Introductionmentioning

confidence: 99%

Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

Mohapatra,

Sahoo,

Mohapatra

2023

Electronics

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The generation of a huge volume of structured, semi-structured and unstructured real-time health monitoring data and its storage in the form of electronic health records (EHRs) need to be processed and analyzed intelligently to provide timely healthcare. A big data analytic platform is an alternative to the traditional warehouse paradigms for the processing, analysis and storage of the tremendous volume of healthcare data. However, the manual analysis of these voluminous, multi-variate patients data is tedious and error-prone. Therefore, an intelligent solution method is highly essential to perform multiple correlation analyses for disease diagnosis and prediction. In this paper, first, a structural framework is proposed to process the huge volume of cardiological big data generated from the hospital and patients. Then, an intelligent analytical model for the cardiological big data analysis is proposed by combining the concept of artificial neural network (ANN) and particle swarm optimization (PSO) to predict the abnormalities in the cardiac health of a person. In the proposed cardiac disease prediction model, an extensive electrocardiogram (ECG) data analysis method is developed to identify the probable normal and abnormal cardiac feature points. Simulation results show the effects of a number of attributes for improving the accuracy of the cardiac disease prediction and data processing time in the cloud with an increase in the number of the cardiac patients.

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Motor Imagery EEG Classification Based on Transfer Learning and Multi-Scale Convolution Network

Cited by 16 publications

References 25 publications

Survey on the research direction of EEG-based signal processing

Survey on the research direction of EEG-based signal processing

A Comparative Analysis of CNNs and RNNs for EEG-based Motor Imagery Classification in BCIs

Healthcare Big Data Analysis with Artificial Neural Network for Cardiac Disease Prediction

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