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Siviero I, Menegaz G, Storti SF. Functional Connectivity and Feature Fusion Enhance Multiclass Motor-Imagery Brain-Computer Interface Performance. SENSORS (BASEL, SWITZERLAND) 2023; 23:7520. [PMID: 37687976 PMCID: PMC10490741 DOI: 10.3390/s23177520] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Revised: 08/24/2023] [Accepted: 08/27/2023] [Indexed: 09/10/2023]
Abstract
(1) Background: in the field of motor-imagery brain-computer interfaces (MI-BCIs), obtaining discriminative features among multiple MI tasks poses a significant challenge. Typically, features are extracted from single electroencephalography (EEG) channels, neglecting their interconnections, which leads to limited results. To address this limitation, there has been growing interest in leveraging functional brain connectivity (FC) as a feature in MI-BCIs. However, the high inter- and intra-subject variability has so far limited its effectiveness in this domain. (2) Methods: we propose a novel signal processing framework that addresses this challenge. We extracted translation-invariant features (TIFs) obtained from a scattering convolution network (SCN) and brain connectivity features (BCFs). Through a feature fusion approach, we combined features extracted from selected channels and functional connectivity features, capitalizing on the strength of each component. Moreover, we employed a multiclass support vector machine (SVM) model to classify the extracted features. (3) Results: using a public dataset (IIa of the BCI Competition IV), we demonstrated that the feature fusion approach outperformed existing state-of-the-art methods. Notably, we found that the best results were achieved by merging TIFs with BCFs, rather than considering TIFs alone. (4) Conclusions: our proposed framework could be the key for improving the performance of a multiclass MI-BCI system.
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Affiliation(s)
- Ilaria Siviero
- Department of Computer Science, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
| | - Gloria Menegaz
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
| | - Silvia Francesca Storti
- Department of Engineering for Innovation Medicine, University of Verona, Strada Le Grazie 15, 37134 Verona, Italy;
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Yu S, Chai H, Xiong Y, Kang M, Geng C, Liu Y, Chen Y, Zhang Y, Zhang Q, Li C, Wei H, Zhao Y, Yu F, Lu A. Studying Complex Evolution of Hyperelastic Materials under External Field Stimuli using Artificial Neural Networks with Spatiotemporal Features in a Small-Scale Dataset. ADVANCED MATERIALS (DEERFIELD BEACH, FLA.) 2022; 34:e2200908. [PMID: 35483076 DOI: 10.1002/adma.202200908] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 04/17/2022] [Indexed: 06/14/2023]
Abstract
Deep-learning (DL) methods, in consideration of their excellence in dealing with highly complex structure-performance relationships for materials, are expected to become a new design paradigm for breakthroughs in material performance. However, in most cases, it is impractical to collect massive-scale experimental data or open-source theoretical databases to support training DL models with sufficient prediction accuracy. In a dataset consisting of 483 porous silicone rubber observations generated via ink-writing additive manufacturing, this work demonstrates that constructing low-dimensional, accurate descriptors is the prerequisite for obtaining high-precision DL models based on small experimental datasets. On this basis, a unique convolutional bidirectional long short-term memory model with spatiotemporal features extraction capability is designed, whose hierarchical learning mechanism further reduces the requirement for the amount of data by taking full advantage of data information. The proposed approach can be expected as a powerful tool for innovative material design on small experimental datasets, which can also be used to explore the evolutionary mechanisms of the structures and properties of materials under complex working conditions.
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Affiliation(s)
- Songlin Yu
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, P. R. China
- State Key Laboratory of Environment-Friendly Energy Materials, School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, P. R. China
| | - Haiyang Chai
- School of Big Data and Software Engineering, Chongqing University, Chongqing, 401331, P. R. China
| | - Yuqi Xiong
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, P. R. China
| | - Ming Kang
- State Key Laboratory of Environment-Friendly Energy Materials, School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, P. R. China
| | - Chengzhen Geng
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, P. R. China
| | - Yu Liu
- School of Mechanical Engineering, Jiangnan University, Wuxi, Jiangsu, 214000, P. R. China
| | - Yanqiu Chen
- School of Mechanical Engineering, Jiangnan University, Wuxi, Jiangsu, 214000, P. R. China
| | - Yaling Zhang
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, P. R. China
| | - Qian Zhang
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, P. R. China
| | - Changlin Li
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, P. R. China
- State Key Laboratory of Environment-Friendly Energy Materials, School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, P. R. China
| | - Hao Wei
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, P. R. China
- State Key Laboratory of Environment-Friendly Energy Materials, School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, P. R. China
| | - Yuhang Zhao
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, P. R. China
- State Key Laboratory of Environment-Friendly Energy Materials, School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, P. R. China
| | - Fengmei Yu
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, P. R. China
| | - Ai Lu
- Institute of Chemical Materials, China Academy of Engineering Physics, Mianyang, Sichuan, 621900, P. R. China
- State Key Laboratory of Environment-Friendly Energy Materials, School of Materials Science and Engineering, Southwest University of Science and Technology, Mianyang, Sichuan, 621010, P. R. China
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