1
|
Cunlin H, Ye Y, Nenggang X. Self-supervised motor imagery EEG recognition model based on 1-D MTCNN-LSTM network. J Neural Eng 2024; 21:036014. [PMID: 38757187 DOI: 10.1088/1741-2552/ad48bc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 05/08/2024] [Indexed: 05/18/2024]
Abstract
Objective.Aiming for the research on the brain-computer interface (BCI), it is crucial to design a MI-EEG recognition model, possessing a high classification accuracy and strong generalization ability, and not relying on a large number of labeled training samples.Approach.In this paper, we propose a self-supervised MI-EEG recognition method based on self-supervised learning with one-dimensional multi-task convolutional neural networks and long short-term memory (1-D MTCNN-LSTM). The model is divided into two stages: signal transform identification stage and pattern recognition stage. In the signal transform recognition phase, the signal transform dataset is recognized by the upstream 1-D MTCNN-LSTM network model. Subsequently, the backbone network from the signal transform identification phase is transferred to the pattern recognition phase. Then, it is fine-tuned using a trace amount of labeled data to finally obtain the motion recognition model.Main results.The upstream stage of this study achieves more than 95% recognition accuracy for EEG signal transforms, up to 100%. For MI-EEG pattern recognition, the model obtained recognition accuracies of 82.04% and 87.14% with F1 scores of 0.7856 and 0.839 on the datasets of BCIC-IV-2b and BCIC-IV-2a.Significance.The improved accuracy proves the superiority of the proposed method. It is prospected to be a method for accurate classification of MI-EEG in the BCI system.
Collapse
Affiliation(s)
- Hu Cunlin
- College of Mechanical Engineering, Anhui University of Technology, Maanshan, Anhui 243002, People's Republic of China
| | - Ye Ye
- College of Mechanical Engineering, Anhui University of Technology, Maanshan, Anhui 243002, People's Republic of China
| | - Xie Nenggang
- College of Mechanical Engineering, Anhui University of Technology, Maanshan, Anhui 243002, People's Republic of China
- College of Management Science and Engineering, Anhui University of Technology, Maanshan, Anhui 243002, People's Republic of China
| |
Collapse
|
2
|
Antony MJ, Sankaralingam BP, Khan S, Almjally A, Almujally NA, Mahendran RK. Brain-Computer Interface: The HOL-SSA Decomposition and Two-Phase Classification on the HGD EEG Data. Diagnostics (Basel) 2023; 13:2852. [PMID: 37685390 PMCID: PMC10486696 DOI: 10.3390/diagnostics13172852] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 08/25/2023] [Accepted: 08/28/2023] [Indexed: 09/10/2023] Open
Abstract
An efficient processing approach is essential for increasing identification accuracy since the electroencephalogram (EEG) signals produced by the Brain-Computer Interface (BCI) apparatus are nonlinear, nonstationary, and time-varying. The interpretation of scalp EEG recordings can be hampered by nonbrain contributions to electroencephalographic (EEG) signals, referred to as artifacts. Common disturbances in the capture of EEG signals include electrooculogram (EOG), electrocardiogram (ECG), electromyogram (EMG) and other artifacts, which have a significant impact on the extraction of meaningful information. This study suggests integrating the Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) methods to preprocess the EEG data. The key objective of our research was to employ Higher-Order Linear-Moment-based SSA (HOL-SSA) to decompose EEG signals into multivariate components, followed by extracting source signals using Online Recursive ICA (ORICA). This approach effectively improves artifact rejection. Experimental results using the motor imagery High-Gamma Dataset validate our method's ability to identify and remove artifacts such as EOG, ECG, and EMG from EEG data, while preserving essential brain activity.
Collapse
Affiliation(s)
- Mary Judith Antony
- Department of Computer Science & Engineering, Panimalar College of Engineering, Chennai 600123, India
| | - Baghavathi Priya Sankaralingam
- Department of Computer Science & Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai 601103, India
| | - Shakir Khan
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (S.K.); (A.A.)
- University Centre for Research and Development, Department of Computer Science and Engineering, Chandigarh University, Mohali 140413, India
| | - Abrar Almjally
- College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia; (S.K.); (A.A.)
| | - Nouf Abdullah Almujally
- Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh 11671, Saudi Arabia
| | - Rakesh Kumar Mahendran
- Department of Computer Science and Engineering, Rajalakshmi Engineering College, Chennai 602105, India;
| |
Collapse
|
3
|
Soangra R, Smith JA, Rajagopal S, Yedavalli SVR, Anirudh ER. Classifying Unstable and Stable Walking Patterns Using Electroencephalography Signals and Machine Learning Algorithms. SENSORS (BASEL, SWITZERLAND) 2023; 23:6005. [PMID: 37447852 DOI: 10.3390/s23136005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/24/2023] [Revised: 06/21/2023] [Accepted: 06/26/2023] [Indexed: 07/15/2023]
Abstract
Analyzing unstable gait patterns from Electroencephalography (EEG) signals is vital to develop real-time brain-computer interface (BCI) systems to prevent falls and associated injuries. This study investigates the feasibility of classification algorithms to detect walking instability utilizing EEG signals. A 64-channel Brain Vision EEG system was used to acquire EEG signals from 13 healthy adults. Participants performed walking trials for four different stable and unstable conditions: (i) normal walking, (ii) normal walking with medial-lateral perturbation (MLP), (iii) normal walking with dual-tasking (Stroop), (iv) normal walking with center of mass visual feedback. Digital biomarkers were extracted using wavelet energy and entropies from the EEG signals. Algorithms like the ChronoNet, SVM, Random Forest, gradient boosting and recurrent neural networks (LSTM) could classify with 67 to 82% accuracy. The classification results show that it is possible to accurately classify different gait patterns (from stable to unstable) using EEG-based digital biomarkers. This study develops various machine-learning-based classification models using EEG datasets with potential applications in detecting unsteady gait neural signals and intervening by preventing falls and injuries.
Collapse
Affiliation(s)
- Rahul Soangra
- Fowler School of Engineering, Chapman University, Orange, CA 92866, USA
- Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA 92866, USA
| | - Jo Armour Smith
- Crean College of Health and Behavioral Sciences, Chapman University, Orange, CA 92866, USA
| | - Sivakumar Rajagopal
- School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India
| | - Sai Viswanth Reddy Yedavalli
- School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India
- School of Electrical and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
| | - Erandumveetil Ramadas Anirudh
- School of Electronics Engineering, Vellore Institute of Technology, Vellore 632014, India
- Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 3P4, Canada
| |
Collapse
|
4
|
Lu X, Wang T, Ye M, Huang S, Wang M, Zhang J. Study on characteristic of epileptic multi-electroencephalograph base on Hilbert-Huang transform and brain network dynamics. Front Neurosci 2023; 17:1117340. [PMID: 37214385 PMCID: PMC10192695 DOI: 10.3389/fnins.2023.1117340] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Accepted: 04/11/2023] [Indexed: 05/24/2023] Open
Abstract
Lots of studies have been carried out on characteristic of epileptic Electroencephalograph (EEG). However, traditional EEG characteristic research methods lack exploration of spatial information. To study the characteristics of epileptic EEG signals from the perspective of the whole brain,this paper proposed combination methods of multi-channel characteristics from time-frequency and spatial domains. This paper was from two aspects: Firstly, signals were converted into 2D Hilbert Spectrum (HS) images which reflected the time-frequency characteristics by Hilbert-Huang Transform (HHT). These images were identified by Convolutional Neural Network (CNN) model whose sensitivity was 99.8%, accuracy was 98.7%, specificity was 97.4%, F1-score was 98.7%, and AUC-ROC was 99.9%. Secondly, the multi-channel signals were converted into brain networks which reflected the spatial characteristics by Symbolic Transfer Entropy (STE) among different channels EEG. And the results show that there are different network properties between ictal and interictal phase and the signals during the ictal enter the synchronization state more quickly, which was verified by Kuramoto model. To summarize, our results show that there was different characteristics among channels for the ictal and interictal phase, which can provide effective physical non-invasive indicators for the identification and prediction of epileptic seizures.
Collapse
Affiliation(s)
- Xiaojie Lu
- School of Physics and Electronic Information, Anhui Normal University, Wuhu, China
- Research Center of Health Big Data Mining and Applications, School of Medicine Information, Wan Nan Medical College, Wuhu, China
| | - Tingting Wang
- Research Center of Health Big Data Mining and Applications, School of Medicine Information, Wan Nan Medical College, Wuhu, China
| | - Mingquan Ye
- Research Center of Health Big Data Mining and Applications, School of Medicine Information, Wan Nan Medical College, Wuhu, China
| | - Shoufang Huang
- School of Physics and Electronic Information, Anhui Normal University, Wuhu, China
| | - Maosheng Wang
- School of Physics and Electronic Information, Anhui Normal University, Wuhu, China
| | - Jiqian Zhang
- School of Physics and Electronic Information, Anhui Normal University, Wuhu, China
| |
Collapse
|
5
|
Zhang N, Lin C. The Image Definition Assessment of Optoelectronic Tracking Equipment Based on the BRISQUE Algorithm with Gaussian Weights. SENSORS (BASEL, SWITZERLAND) 2023; 23:1621. [PMID: 36772660 PMCID: PMC9921252 DOI: 10.3390/s23031621] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 01/12/2023] [Accepted: 01/28/2023] [Indexed: 06/18/2023]
Abstract
Defocus is an important factor that causes image quality degradation of optoelectronic tracking equipment in the shooting range. In this paper, an improved blind/referenceless image spatial quality evaluator (BRISQUE) algorithm is formulated by using the image characteristic extraction technology to obtain a characteristic vector (CV). The CV consists of 36 characteristic values that can effectively reflect the defocusing condition of the corresponding image. The image is evaluated and scored subjectively by the human eyes. The subjective evaluation scores and CVs constitute a set of training data samples for the defocusing evaluation model. An image database that contains sufficiently many training samples is constructed. The training model is trained to obtain the support vector machine (SVM) model by using the regression function of the SVM. In the experiments, the BRISQUE algorithm is used to obtain the image feature vector. The method of establishing the image definition evaluation model via SVM is feasible and yields higher subjective and objective consistency.
Collapse
Affiliation(s)
- Ning Zhang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
| | - Cui Lin
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun 130033, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| |
Collapse
|
6
|
Shi C, Yan L, Zhang J, Cheng Y, Peng F, Yan F. Emergency Braking Evoked Brain Activities during Distracted Driving. SENSORS (BASEL, SWITZERLAND) 2022; 22:9564. [PMID: 36502266 PMCID: PMC9736420 DOI: 10.3390/s22239564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/16/2022] [Revised: 12/03/2022] [Accepted: 12/05/2022] [Indexed: 06/17/2023]
Abstract
Electroencephalogram (EEG) was used to analyze the mechanisms and differences in brain neural activity of drivers in visual, auditory, and cognitive distracted vs. normal driving emergency braking conditions. A pedestrian intrusion emergency braking stimulus module and three distraction subtasks were designed in a simulated experiment, and 30 subjects participated in the study. The common activated brain regions during emergency braking in different distracted driving states included the inferior temporal gyrus, associated with visual information processing and attention; the left dorsolateral superior frontal gyrus, related to cognitive decision-making; and the postcentral gyrus, supplementary motor area, and paracentral lobule associated with motor control and coordination. When performing emergency braking under different driving distraction states, the brain regions were activated in accordance with the need to process the specific distraction task. Furthermore, the extent and degree of activation of cognitive function-related prefrontal regions increased accordingly with the increasing task complexity. All distractions caused a lag in emergency braking reaction time, with 107.22, 67.15, and 126.38 ms for visual, auditory, and cognitive distractions, respectively. Auditory distraction had the least effect and cognitive distraction the greatest effect on the lag.
Collapse
Affiliation(s)
- Changcheng Shi
- Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
| | - Lirong Yan
- Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
- Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory, Foshan 528200, China
| | - Jiawen Zhang
- Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
| | - Yu Cheng
- Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
| | - Fumin Peng
- Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
| | - Fuwu Yan
- Hubei Key Laboratory of Advanced Technology for Automotive Components, Wuhan University of Technology, Wuhan 430070, China
- Foshan Xianhu Laboratory of the Advanced Energy Science and Technology Guangdong Laboratory, Foshan 528200, China
| |
Collapse
|