1
|
Ren G, Kumar A, Mahmoud SS, Fang Q. A deep neural network and transfer learning combined method for cross-task classification of error-related potentials. Front Hum Neurosci 2024; 18:1394107. [PMID: 38933146 PMCID: PMC11199896 DOI: 10.3389/fnhum.2024.1394107] [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: 03/01/2024] [Accepted: 05/22/2024] [Indexed: 06/28/2024] Open
Abstract
Background Error-related potentials (ErrPs) are electrophysiological responses that naturally occur when humans perceive wrongdoing or encounter unexpected events. It offers a distinctive means of comprehending the error-processing mechanisms within the brain. A method for detecting ErrPs with high accuracy holds significant importance for various ErrPs-based applications, such as human-in-the-loop Brain-Computer Interface (BCI) systems. Nevertheless, current methods fail to fulfill the generalization requirements for detecting such ErrPs due to the high non-stationarity of EEG signals across different tasks and the limited availability of ErrPs datasets. Methods This study introduces a deep learning-based model that integrates convolutional layers and transformer encoders for the classification of ErrPs. Subsequently, a model training strategy, grounded in transfer learning, is proposed for the effective training of the model. The datasets utilized in this study are available for download from the publicly accessible databases. Results In cross-task classification, an average accuracy of about 78% was achieved, exceeding the baseline. Furthermore, in the leave-one-subject-out, within-session, and cross-session classification scenarios, the proposed model outperformed the existing techniques with an average accuracy of 71.81, 78.74, and 77.01%, respectively. Conclusions Our approach contributes to mitigating the challenge posed by limited datasets in the ErrPs field, achieving this by reducing the requirement for extensive training data for specific target tasks. This may serve as inspiration for future studies that concentrate on ErrPs and their applications.
Collapse
Affiliation(s)
| | | | | | - Qiang Fang
- Department of Biomedical Engineering, Shantou University, Shantou, China
| |
Collapse
|
2
|
Nogales A, Rodríguez-Aragón M, García-Tejedor ÁJ. A systematic review of the application of deep learning techniques in the physiotherapeutic therapy of musculoskeletal pathologies. Comput Biol Med 2024; 172:108082. [PMID: 38461697 DOI: 10.1016/j.compbiomed.2024.108082] [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: 06/22/2023] [Revised: 12/21/2023] [Accepted: 01/27/2024] [Indexed: 03/12/2024]
Abstract
Physiotherapy is a critical area of healthcare that involves the assessment and treatment of physical disabilities and injuries. The use of Artificial Intelligence (AI) in physiotherapy has gained significant attention due to its potential to enhance the accuracy and effectiveness of clinical decision-making and treatment outcomes. Nevertheless, it is still a rather innovative field of application of these techniques and there is a need to find what aspects are highly developed and what possible job niches can be exploited. This systematic review aims to evaluate the current state of research on the use of a particular AI called deep learning models in physiotherapy and identify the key trends, challenges, and opportunities in this field. The findings of this review, conducted following the PRISMA guidelines, provide valuable insights for researchers and clinicians. The most relevant databases included were PubMed, Web of Science, Scopus, Astrophysics Data System, and Central Citation Export. Inclusion and exclusion criteria were established to determine which items would be considered for further review. These criteria were used to screen the items during the first and second peer review processes. A set of quality criteria was developed to select the papers obtained after the second screening. Finally, of the 214 initial papers, 23 studies were selected. From our analysis of the selected articles, we can draw the following conclusions: Concerning deep learning models, innovation is primarily seen in the adoption of hybrid models, with convolutional models being extensively utilized. In terms of data, it's unsurprising that body signals and images are predominantly used. However, texts and structured data present promising avenues for groundbreaking work in the field. Additionally, medical tests that involve the collection of 3D images, Functional Movement Screening, or thermographies emerge as novel areas to explore applications within the scope of physiotherapy.
Collapse
Affiliation(s)
- Alberto Nogales
- CEIEC, Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda Km 1800, 28223, Pozuelo de Alarcón, Spain.
| | - Manuel Rodríguez-Aragón
- Rehabilitation and Technology Department, Adamo Robot SL. Miguel de Villanueva, 6, 26001, Logroño, Spain.
| | - Álvaro J García-Tejedor
- CEIEC, Research Institute, Universidad Francisco de Vitoria, Ctra. M-515 Pozuelo-Majadahonda Km 1800, 28223, Pozuelo de Alarcón, Spain.
| |
Collapse
|
3
|
Haotian X, Anmin G, Jiangong L, Fan W, Peng D, Yunfa F. Online adaptive classification system for brain–computer interface based on error-related potentials and neurofeedback. Biomed Signal Process Control 2023. [DOI: 10.1016/j.bspc.2022.104554] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
|
4
|
Tao T, Gao Y, Jia Y, Chen R, Li P, Xu G. A Multi-Channel Ensemble Method for Error-Related Potential Classification Using 2D EEG Images. SENSORS (BASEL, SWITZERLAND) 2023; 23:2863. [PMID: 36905065 PMCID: PMC10007400 DOI: 10.3390/s23052863] [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: 01/09/2023] [Revised: 02/19/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
An error-related potential (ErrP) occurs when people's expectations are not consistent with the actual outcome. Accurately detecting ErrP when a human interacts with a BCI is the key to improving these BCI systems. In this paper, we propose a multi-channel method for error-related potential detection using a 2D convolutional neural network. Multiple channel classifiers are integrated to make final decisions. Specifically, every 1D EEG signal from the anterior cingulate cortex (ACC) is transformed into a 2D waveform image; then, a model named attention-based convolutional neural network (AT-CNN) is proposed to classify it. In addition, we propose a multi-channel ensemble approach to effectively integrate the decisions of each channel classifier. Our proposed ensemble approach can learn the nonlinear relationship between each channel and the label, which obtains 5.27% higher accuracy than the majority voting ensemble approach. We conduct a new experiment and validate our proposed method on a Monitoring Error-Related Potential dataset and our dataset. With the method proposed in this paper, the accuracy, sensitivity and specificity were 86.46%, 72.46% and 90.17%, respectively. The result shows that the AT-CNNs-2D proposed in this paper can effectively improve the accuracy of ErrP classification, and provides new ideas for the study of classification of ErrP brain-computer interfaces.
Collapse
Affiliation(s)
- Tangfei Tao
- Key Laboratory of Education Ministry for Modern Design & Rotor-Bearing System, Xi’an Jiaotong University, Xi’an 710049, China
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Yuxiang Gao
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Yaguang Jia
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Ruiquan Chen
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| | - Ping Li
- School of Foreign Studies, Xi’an Jiaotong University, Xi’an 710049, China
| | - Guanghua Xu
- School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
- State Key Laboratory for Manufacturing Systems Engineering, Xi’an Jiaotong University, Xi’an 710049, China
| |
Collapse
|
5
|
Yasemin M, Cruz A, Nunes UJ, Pires G. Single trial detection of error-related potentials in brain-machine interfaces: a survey and comparison of methods. J Neural Eng 2023; 20. [PMID: 36595316 DOI: 10.1088/1741-2552/acabe9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Accepted: 12/15/2022] [Indexed: 12/23/2022]
Abstract
Objective.Error-related potential (ErrP) is a potential elicited in the brain when humans perceive an error. ErrPs have been researched in a variety of contexts, such as to increase the reliability of brain-computer interfaces (BCIs), increase the naturalness of human-machine interaction systems, teach systems, as well as study clinical conditions. Still, there is a significant challenge in detecting ErrP from a single trial, which may hamper its effective use. The literature presents ErrP detection accuracies quite variable across studies, which raises the question of whether this variability depends more on classification pipelines or on the quality of elicited ErrPs (mostly directly related to the underlying paradigms).Approach.With this purpose, 11 datasets have been used to compare several classification pipelines which were selected according to the studies that reported online performance above 75%. We also analyze the effects of different steps of the pipelines, such as resampling, window selection, augmentation, feature extraction, and classification.Main results.From our analysis, we have found that shrinkage-regularized linear discriminant analysis is the most robust method for classification, and for feature extraction, using Fisher criterion beamformer spatial features and overlapped window averages result in better classification performance. The overall experimental results suggest that classification accuracy is highly dependent on user tasks in BCI experiments and on signal quality (in terms of ErrP morphology, signal-to-noise ratio (SNR), and discrimination).Significance.This study contributes to the BCI research field by responding to the need for a guideline that can direct researchers in designing ErrP-based BCI tasks by accelerating the design steps.
Collapse
Affiliation(s)
- Mine Yasemin
- Institute of Systems and Robotics (ISR-UC), University of Coimbra, Coimbra, Portugal.,Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal
| | - Aniana Cruz
- Institute of Systems and Robotics (ISR-UC), University of Coimbra, Coimbra, Portugal
| | - Urbano J Nunes
- Institute of Systems and Robotics (ISR-UC), University of Coimbra, Coimbra, Portugal.,Department of Electrical and Computer Engineering, University of Coimbra, Coimbra, Portugal
| | - Gabriel Pires
- Institute of Systems and Robotics (ISR-UC), University of Coimbra, Coimbra, Portugal.,Engineering Department, Polytechnic Institute of Tomar, Tomar, Portugal
| |
Collapse
|
6
|
Yuceturk NE, Demir S, Ozdemir Z, Bejan I, Dresevic N, Katanic M, Dillenbourg P, Soysal A, Ozgur AG. Predictive Analysis of Errors During Robot-Mediated Gamified Training. IEEE Int Conf Rehabil Robot 2022; 2022:1-6. [PMID: 36176135 DOI: 10.1109/icorr55369.2022.9896589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
This paper presents our approach to predicting future error-related events in a robot-mediated gamified physical training activity for stroke patients. The ability to predict future error under such conditions suggests the existence of distinguishable features and separated class characteristics between the casual gameplay state and error prune state in the data. Identifying such features provides valuable insight to creating individually tailored, adaptive games as well as possible ways to increase rehabilitation success by patients. Considering the time-series nature of sensory data created by motor actions of patients we employed a predictive analysis strategy on carefully engineered features of sequenced data. We split the data into fixed time windows and explored logistic regression models, decision trees, and recurrent neural networks to predict the likelihood of a patient making an error based on the features from the time window before the error. We achieved an 84.4% F1-score with a 0.76 ROC value in our best model for predicting motion accuracy related errors. Moreover, we computed the permutation importance of the features to explain which ones are more indicative of future errors.
Collapse
|
7
|
Zhang X, Yang Y, Shen YW, Zhang KR, Jiang ZK, Ma LT, Ding C, Wang BY, Meng Y, Liu H. Diagnostic accuracy and potential covariates of artificial intelligence for diagnosing orthopedic fractures: a systematic literature review and meta-analysis. Eur Radiol 2022; 32:7196-7216. [PMID: 35754091 DOI: 10.1007/s00330-022-08956-4] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/07/2022] [Accepted: 06/08/2022] [Indexed: 02/05/2023]
Abstract
OBJECTIVES To systematically quantify the diagnostic accuracy and identify potential covariates affecting the performance of artificial intelligence (AI) in diagnosing orthopedic fractures. METHODS PubMed, Embase, Web of Science, and Cochrane Library were systematically searched for studies on AI applications in diagnosing orthopedic fractures from inception to September 29, 2021. Pooled sensitivity and specificity and the area under the receiver operating characteristic curves (AUC) were obtained. This study was registered in the PROSPERO database prior to initiation (CRD 42021254618). RESULTS Thirty-nine were eligible for quantitative analysis. The overall pooled AUC, sensitivity, and specificity were 0.96 (95% CI 0.94-0.98), 90% (95% CI 87-92%), and 92% (95% CI 90-94%), respectively. In subgroup analyses, multicenter designed studies yielded higher sensitivity (92% vs. 88%) and specificity (94% vs. 91%) than single-center studies. AI demonstrated higher sensitivity with transfer learning (with vs. without: 92% vs. 87%) or data augmentation (with vs. without: 92% vs. 87%), compared to those without. Utilizing plain X-rays as input images for AI achieved results comparable to CT (AUC 0.96 vs. 0.96). Moreover, AI achieved comparable results to humans (AUC 0.97 vs. 0.97) and better results than non-expert human readers (AUC 0.98 vs. 0.96; sensitivity 95% vs. 88%). CONCLUSIONS AI demonstrated high accuracy in diagnosing orthopedic fractures from medical images. Larger-scale studies with higher design quality are needed to validate our findings. KEY POINTS • Multicenter study design, application of transfer learning, and data augmentation are closely related to improving the performance of artificial intelligence models in diagnosing orthopedic fractures. • Utilizing plain X-rays as input images for AI to diagnose fractures achieved results comparable to CT (AUC 0.96 vs. 0.96). • AI achieved comparable results to humans (AUC 0.97 vs. 0.97) but was superior to non-expert human readers (AUC 0.98 vs. 0.96, sensitivity 95% vs. 88%) in diagnosing fractures.
Collapse
Affiliation(s)
- Xiang Zhang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Yi Yang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Yi-Wei Shen
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Ke-Rui Zhang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Ze-Kun Jiang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610000, China
| | - Li-Tai Ma
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Chen Ding
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Bei-Yu Wang
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Yang Meng
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China
| | - Hao Liu
- Department of Orthopedics, Orthopedic Research Institute, West China Hospital, Sichuan University, No. 37 Guo Xue Rd, Chengdu, 610041, China.
| |
Collapse
|