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Zhang C, Xue Y, Neri F, Cai X, Slowik A. Multi-Objective Self-Adaptive Particle Swarm Optimization for Large-Scale Feature Selection in Classification. Int J Neural Syst 2024; 34:2450014. [PMID: 38352979 DOI: 10.1142/s012906572450014x] [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] [Indexed: 02/20/2024]
Abstract
Feature selection (FS) is recognized for its role in enhancing the performance of learning algorithms, especially for high-dimensional datasets. In recent times, FS has been framed as a multi-objective optimization problem, leading to the application of various multi-objective evolutionary algorithms (MOEAs) to address it. However, the solution space expands exponentially with the dataset's dimensionality. Simultaneously, the extensive search space often results in numerous local optimal solutions due to a large proportion of unrelated and redundant features [H. Adeli and H. S. Park, Fully automated design of super-high-rise building structures by a hybrid ai model on a massively parallel machine, AI Mag. 17 (1996) 87-93]. Consequently, existing MOEAs struggle with local optima stagnation, particularly in large-scale multi-objective FS problems (LSMOFSPs). Different LSMOFSPs generally exhibit unique characteristics, yet most existing MOEAs rely on a single candidate solution generation strategy (CSGS), which may be less efficient for diverse LSMOFSPs [H. S. Park and H. Adeli, Distributed neural dynamics algorithms for optimization of large steel structures, J. Struct. Eng. ASCE 123 (1997) 880-888; M. Aldwaik and H. Adeli, Advances in optimization of highrise building structures, Struct. Multidiscip. Optim. 50 (2014) 899-919; E. G. González, J. R. Villar, Q. Tan, J. Sedano and C. Chira, An efficient multi-robot path planning solution using a* and coevolutionary algorithms, Integr. Comput. Aided Eng. 30 (2022) 41-52]. Moreover, selecting an appropriate MOEA and determining its corresponding parameter values for a specified LSMOFSP is time-consuming. To address these challenges, a multi-objective self-adaptive particle swarm optimization (MOSaPSO) algorithm is proposed, combined with a rapid nondominated sorting approach. MOSaPSO employs a self-adaptive mechanism, along with five modified efficient CSGSs, to generate new solutions. Experiments were conducted on ten datasets, and the results demonstrate that the number of features is effectively reduced by MOSaPSO while lowering the classification error rate. Furthermore, superior performance is observed in comparison to its counterparts on both the training and test sets, with advantages becoming increasingly evident as the dimensionality increases.
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Affiliation(s)
- Chenyi Zhang
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
| | - Yu Xue
- School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing 210044, P. R. China
| | - Ferrante Neri
- NICE Research Group, School of Computer Science and Electronic Engineering, University of Surrey Guildford, GU2 7XS, UK
| | - Xu Cai
- School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, P. R. China
| | - Adam Slowik
- Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin 75-453, Poland
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Taslicay CA, Dervisoglu E, Ince O, Mese I, Taslicay C, Bayrak BY, Cabuk B, Anik I, Ceylan S, Anik Y. A Novel Fusion of Radiomics and Semantic Features: MRI-Based Machine Learning in Distinguishing Pituitary Cystic Adenomas from Rathke's Cleft Cysts. J Belg Soc Radiol 2024; 108:9. [PMID: 38312147 PMCID: PMC10836174 DOI: 10.5334/jbsr.3470] [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: 12/16/2023] [Accepted: 01/18/2024] [Indexed: 02/06/2024] Open
Abstract
Objectives To evaluate the performances of machine learning using semantic and radiomic features from magnetic resonance imaging data to distinguish cystic pituitary adenomas (CPA) from Rathke's cleft cysts (RCCs). Materials and Methods The study involved 65 patients diagnosed with either CPA or RCCs. Multiple observers independently assessed the semantic features of the tumors on the magnetic resonance images. Radiomics features were extracted from T2-weighted, T1-weighted, and T1-contrast-enhanced images. Machine learning models, including Support Vector Machines (SVM), Logistic Regression (LR), and Light Gradient Boosting (LGB), were then trained and validated using semantic features only and a combination of semantic and radiomic features. Statistical analyses were carried out to compare the performance of these various models. Results Machine learning models that combined semantic and radiomic features achieved higher levels of accuracy than models with semantic features only. Models with combined semantic and T2-weighted radiomics features achieved the highest test accuracies (93.8%, 92.3%, and 90.8% for LR, SVM, and LGB, respectively). The SVM model combined semantic features with T2-weighted radiomics features had statistically significantly better performance than semantic features only (p = 0.019). Conclusion Our study demonstrates the significant potential of machine learning for differentiating CPA from RCCs.
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Affiliation(s)
| | | | - Okan Ince
- Department of Vascular and Interventional Radiology, Rush University, Chicago, IL, USA
| | - Ismail Mese
- Department of Radiology, Health Sciences University, Erenkoy Mental Health and Neurology Training and Research Hospital, Istanbul, Turkey
| | | | | | - Burak Cabuk
- Department of Neurosurgery, Pituitary Research Center, Kocaeli University, Kocaeli, Turkey
| | - Ihsan Anik
- Department of Neurosurgery, Pituitary Research Center, Kocaeli University, Kocaeli, Turkey
| | - Savas Ceylan
- Department of Neurosurgery, Pituitary Research Center, Kocaeli University, Kocaeli, Turkey
| | - Yonca Anik
- Department of Radiology, Kocaeli University, Kocaeli, Turkey
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Ganjali M, Mehridehnavi A, Rakhshani S, Khorasani A. Unsupervised Neural Manifold Alignment for Stable Decoding of Movement from Cortical Signals. Int J Neural Syst 2024; 34:2450006. [PMID: 38063378 DOI: 10.1142/s0129065724500060] [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] [Indexed: 12/28/2023]
Abstract
The stable decoding of movement parameters using neural activity is crucial for the success of brain-machine interfaces (BMIs). However, neural activity can be unstable over time, leading to changes in the parameters used for decoding movement, which can hinder accurate movement decoding. To tackle this issue, one approach is to transfer neural activity to a stable, low-dimensional manifold using dimensionality reduction techniques and align manifolds across sessions by maximizing correlations of the manifolds. However, the practical use of manifold stabilization techniques requires knowledge of the true subject intentions such as target direction or behavioral state. To overcome this limitation, an automatic unsupervised algorithm is proposed that determines movement target intention before manifold alignment in the presence of manifold rotation and scaling across sessions. This unsupervised algorithm is combined with a dimensionality reduction and alignment method to overcome decoder instabilities. The effectiveness of the BMI stabilizer method is represented by decoding the two-dimensional (2D) hand velocity of two rhesus macaque monkeys during a center-out-reaching movement task. The performance of the proposed method is evaluated using correlation coefficient and R-squared measures, demonstrating higher decoding performance compared to a state-of-the-art unsupervised BMI stabilizer. The results offer benefits for the automatic determination of movement intents in long-term BMI decoding. Overall, the proposed method offers a promising automatic solution for achieving stable and accurate movement decoding in BMI applications.
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Affiliation(s)
- Mohammadali Ganjali
- Department of Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Alireza Mehridehnavi
- Department of Biomedical Engineering, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Sajed Rakhshani
- Medical Image and Signal Processing Research Center, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Abed Khorasani
- Department of Neurology, Northwestern University, Chicago, IL, 60611, USA
- Neuroscience Research Center, Institute of Neuropharmacology, Kerman University of Medical Sciences, Kerman, Iran
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Sun H, Jin J, Daly I, Huang Y, Zhao X, Wang X, Cichocki A. Feature learning framework based on EEG graph self-attention networks for motor imagery BCI systems. J Neurosci Methods 2023; 399:109969. [PMID: 37683772 DOI: 10.1016/j.jneumeth.2023.109969] [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: 05/26/2023] [Revised: 08/18/2023] [Accepted: 09/03/2023] [Indexed: 09/10/2023]
Abstract
Learning distinguishable features from raw EEG signals is crucial for accurate classification of motor imagery (MI) tasks. To incorporate spatial relationships between EEG sources, we developed a feature set based on an EEG graph. In this graph, EEG channels represent the nodes, with power spectral density (PSD) features defining their properties, and the edges preserving the spatial information. We designed an EEG based graph self-attention network (EGSAN) to learn low-dimensional embedding vector for EEG graph, which can be used as distinguishable features for motor imagery task classification. We evaluated our EGSAN model on two publicly available MI EEG datasets, each containing different types of motor imagery tasks. Our experiments demonstrate that our proposed model effectively extracts distinguishable features from EEG graphs, achieving significantly higher classification accuracies than existing state-of-the-art methods.
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Affiliation(s)
- Hao Sun
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Jing Jin
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China; Shenzhen Research Institute of East China University of Science and Technology, Shen Zhen 518063, China.
| | - Ian Daly
- Brain-Computer Interfacing and Neural Engineering Laboratory, School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom
| | - Yitao Huang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xueqing Zhao
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Xingyu Wang
- Key Laboratory of Smart Manufacturing in Energy Chemical Process, Ministry of Education, East China University of Science and Technology, Shanghai, China
| | - Andrzej Cichocki
- RIKEN Brain Science Institute, Wako 351-0198, Japan; Nicolaus Copernicus University (UMK), 87-100 Torun, Poland
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Wu X, Feng Y, Lou S, Zheng H, Hu B, Hong Z, Tan J. Improving NeuCube Spiking Neural Network for EEG-based Pattern Recognition Using Transfer Learning. Neurocomputing 2023. [DOI: 10.1016/j.neucom.2023.01.087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
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Hou Y, Gu Z, Yu ZL, Xie X, Tang R, Xu J, Qi F. Enhancement of lower limb motor imagery ability via dual-level multimodal stimulation and sparse spatial pattern decoding method. Front Hum Neurosci 2022; 16:975410. [PMID: 36034117 PMCID: PMC9406291 DOI: 10.3389/fnhum.2022.975410] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 07/22/2022] [Indexed: 11/24/2022] Open
Abstract
Recently, motor imagery brain-computer interfaces (MI-BCIs) with stimulation systems have been developed in the field of motor function assistance and rehabilitation engineering. An efficient stimulation paradigm and Electroencephalogram (EEG) decoding method have been designed to enhance the performance of MI-BCI systems. Therefore, in this study, a multimodal dual-level stimulation paradigm is designed for lower-limb rehabilitation training, whereby visual and auditory stimulations act on the sensory organ while proprioceptive and functional electrical stimulations are provided to the lower limb. In addition, upper triangle filter bank sparse spatial pattern (UTFB-SSP) is proposed to automatically select the optimal frequency sub-bands related to desynchronization rhythm during enhanced imaginary movement to improve the decoding performance. The effectiveness of the proposed MI-BCI system is demonstrated on an the in-house experimental dataset and the BCI competition IV IIa dataset. The experimental results show that the proposed system can effectively enhance the MI performance by inducing the α, β and γ rhythms in lower-limb movement imagery tasks.
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Affiliation(s)
- Yao Hou
- Mechanical and Electrical Engineering College, Hainan University, Haikou, China
| | - Zhenghui Gu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
| | - Zhu Liang Yu
- College of Automation Science and Engineering, South China University of Technology, Guangzhou, China
| | - Xiaofeng Xie
- Mechanical and Electrical Engineering College, Hainan University, Haikou, China
| | - Rongnian Tang
- Mechanical and Electrical Engineering College, Hainan University, Haikou, China
| | - Jinghan Xu
- Mechanical and Electrical Engineering College, Hainan University, Haikou, China
| | - Feifei Qi
- School of Internet Finance and Information Engineering, Guangdong University of Finance, Guangzhou, China
- Pazhou Lab, Guangzhou, China
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Xie P, Hao S, Zhao J, Liang Z, Li X. A Spatio-Temporal Method for Extracting Gamma-Band Features to Enhance Classification in a Rapid Serial Visual Presentation Task. Int J Neural Syst 2022; 32:2250010. [PMID: 35049411 DOI: 10.1142/s0129065722500101] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Rapid serial visual presentation (RSVP) is a type of electroencephalogram (EEG) pattern commonly used for target recognition. Besides delta- and theta-band responses already used for classification, RSVP task also evokes gamma-band responses having low amplitude and large individual difference. This paper proposes a filter bank spatio-temporal component analysis (FBSCA) method, extracting spatio-temporal features of the gamma-band responses for the first time, to enhance the RSVP classification performance. Considering the individual difference in time latency and responsive frequency, the proposed FBSCA method decomposes the gamma-band EEG data into sub-components in different time-frequency-space domains and seeks the weight coefficients to optimize the combinations of electrodes, common spatial pattern (CSP) components, time windows and frequency bands. Two state-of-the-art methods, i.e. hierarchical discriminant principal component analysis (HDPCA) and discriminative canonical pattern matching (DCPM), were used for comparison. The performance was evaluated in [Formula: see text] cross validations using a public dataset. Study results showed that the FBSCA method outperformed the other methods regardless of number of training trials. These results suggest that the proposed FBSCA method can enhance the RSVP classification.
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Affiliation(s)
- Ping Xie
- Key Laboratory of Intelligent Rehabilitation, and Neromodulation of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, P. R. China
| | - Shencai Hao
- Key Laboratory of Intelligent Rehabilitation, and Neromodulation of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, P. R. China
| | - Jing Zhao
- Key Laboratory of Intelligent Rehabilitation, and Neromodulation of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, P. R. China
| | - Zhenhu Liang
- Key Laboratory of Intelligent Rehabilitation, and Neromodulation of Hebei Province, School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, P. R. China
| | - Xiaoli Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, P. R. China
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Karakullukcu N, Yilmaz B. Detection of Movement Intention in EEG-Based Brain-Computer Interfaces Using Fourier-Based Synchrosqueezing Transform. Int J Neural Syst 2021; 32:2150059. [PMID: 34806939 DOI: 10.1142/s0129065721500593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Patients with motor impairments need caregivers' help to initiate the operation of brain-computer interfaces (BCI). This study aims to identify and characterize movement intention using multichannel electroencephalography (EEG) signals as a means to initiate BCI systems without extra accessories/methodologies. We propose to discriminate the resting and motor imagery (MI) states with high accuracy using Fourier-based synchrosqueezing transform (FSST) as a feature extractor. FSST has been investigated and compared with other popular approaches in 28 healthy subjects for a total of 6657 trials. The accuracy and f-measure values were obtained as 99.8% and 0.99, respectively, when FSST was used as the feature extractor and singular value decomposition (SVD) as the feature selection method and support vector machines as the classifier. Moreover, this study investigated the use of data that contain certain amount of noise without any preprocessing in addition to the clean counterparts. Furthermore, the statistical analysis of EEG channels with the best discrimination (of resting and MI states) characteristics demonstrated that F4-Fz-C3-Cz-C4-Pz channels and several statistical features had statistical significance levels, [Formula: see text], less than 0.05. This study showed that the preparation of the movement can be detected in real-time employing FSST-SVD combination and several channels with minimal pre-processing effort.
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Affiliation(s)
- Nedime Karakullukcu
- Electrical and Computer Engineering Department, Graduate School of Engineering and Sciences, Abdullah Gul University, 38080 Kayseri, Turkey.,Biomedical Instrumentation and Signal Analysis, Laboratory (BISA-Lab), School of Engineering, Abdullah Gul University, 38080 Kayseri, Turkey
| | - Bülent Yilmaz
- Electrical and Computer Engineering Department, Graduate School of Engineering and Sciences, Abdullah Gul University, 38080 Kayseri, Turkey.,Electrical-Electronics Engineering Department, School of Engineering, Abdullah Gul University, 38080 Kayseri, Turkey.,Biomedical Instrumentation and Signal Analysis Laboratory (BISA-Lab), School of Engineering, Abdullah Gul University, 38080 Kayseri, Turkey
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