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Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: Three-class classification of rest, right-, and left-hand motor execution
- Convolutional neural network for high-accuracy functional near-infrared spectroscopy in a brain-computer interface: Three-class classification of rest, right-, and left-hand motor execution
- Trakoolwilaiwan, Thanawin; Behboodi, Bahareh; Lee, Jae Seok; Kim, Kyung Soo; Choi, Ji Woong
- DGIST Authors
- Choi, Ji Woong
- Issue Date
- Neurophotonics, 5(1)
- Article Type
- Artificial Neural Network; Biomedical Signal Processing; Brain Computer Interface; Brain-Computer Interface; Classification (of Information); Classification Accuracy; Clinical Article; Computer Networks; Controlled Study; Conventional Methods; Convolution; Convolutional Neural Network; Degradation; Extraction; Feature Extraction; Functional Near Infrared Spectroscopy; Functional Near-Infrared Spectroscopy; Functional Near-Infrared Spectroscopy (FNIRS); Functional Neuroimaging; Hand; Hemodynamic Response; Hemodynamics; Higher Order Statistics; Human; Infrared Devices; Interfaces (Computer); Near Infrared Spectroscopy; Nervous System; Neural Networks; Performance Degradation; Rest; Statistical Methods; Support Vector Machine; Support Vector Machines; Three-Class Classification
- The aim of this work is to develop an effective brain-computer interface (BCI) method based on functional near-infrared spectroscopy (fNIRS). In order to improve the performance of the BCI system in terms of accuracy, the ability to discriminate features from input signals and proper classification are desired. Previous studies have mainly extracted features from the signal manually, but proper features need to be selected carefully. To avoid performance degradation caused by manual feature selection, we applied convolutional neural networks (CNNs) as the automatic feature extractor and classifier for fNIRS-based BCI. In this study, the hemodynamic responses evoked by performing rest, right-, and left-hand motor execution tasks were measured on eight healthy subjects to compare performances. Our CNN-based method provided improvements in classification accuracy over conventional methods employing the most commonly used features of mean, peak, slope, variance, kurtosis, and skewness, classified by support vector machine (SVM) and artificial neural network (ANN). Specifically, up to 6.49% and 3.33% improvement in classification accuracy was achieved by CNN compared with SVM and ANN, respectively. © 2017 The Authors.
- Related Researcher
CSP(Communication and Signal Processing) Lab
Communication System; Signal Processing; Communication Circuit Design; 생체 신호 통신 및 신호 처리; 뇌-기계 인터페이스(BMI); 차세대 교차계층 통신 및 신호 처리; 5G 모바일 통신
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- Department of Information and Communication EngineeringCSP(Communication and Signal Processing) Lab1. Journal Articles
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