Search Results - "Classification Performance"

Data Augmentation: Using Channel-Level Recombination to Improve Classification Performance for Motor Imagery EEG by Pei, Yu, Luo, Zhiguo, Yan, Ye, Yan, Huijiong, Jiang, Jing, Li, Weiguo, Xie, Liang, Yin, Erwei

“… We then designed two schemas (intra- and adaptive-subject schema) corresponding to the single- and multi-subject scenarios…”
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Exogenous Volatile Organic Compound (EVOC®) Breath Testing Maximizes Classification Performance for Subjects with Cirrhosis and Reveals Signs of Portal Hypertension by Ferrandino, Giuseppe, Ricciardi, Federico, Murgia, Antonio, Banda, Iris, Manhota, Menisha, Ahmed, Yusuf, Sweeney, Kelly, Nicholson-Scott, Louise, McConville, Lucinda, Gandelman, Olga, Allsworth, Max, Boyle, Billy, Smolinska, Agnieszka, Ginesta Frings, Carmen A., Contreras, Jorge, Asenjo-Lobos, Claudia, Barrientos, Viviana, Clavo, Nataly, Novoa, Angela, Riviotta, Amy, Jerez, Melissa, Méndez, Luis

“… Consequently, up to 75% of subjects with cirrhosis receive their first diagnosis with decompensation when causal treatments are less effective at preserving liver function…”
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S1578 Dynamic Limonene Breath Testing Maximizes Classification Performance for Subjects With Cirrhosis by Ferrandino, Giuseppe, Ricciardi, Federico, Murgia, Antonio, Banda, Iris, Ahmed, Yusuf, Manhota, Menisha, Sweeney, Kelly, Nicholson-Scott, Louise, McConville, Lucinda, Gandelman, Olga, Allsworth, Max, Boyle, Billy, Smolinska, Agnieszka, Frings, Alexandra Ginesta, Basulto, Jorge Contreras, Asenjo-Lobos, Claudia, Parra, Viviana Barrientos, Morales, Nataly Clavo, Arce, Angela Novoa, Riviotta, Amy, Jerez Pardo, Melissa Javiera, Alcamán, Luis Méndez

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Heterogeneous transfer learning model for improving the classification performance of fNIRS signals in motor imagery among cross-subject stroke patients by Feng, Jin, Li, YunDe, Huang, ZiJun, Chen, Yehang, Lu, SenLiang, Hu, RongLiang, Hu, QingHui, Chen, YuYao, Wang, XiMiao, Fan, Yong, He, Jing

“…Motor imagery functional near-infrared spectroscopy (MI-fNIRS) offers precise monitoring of neural activity in stroke rehabilitation, yet accurate cross-subject…”
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Using Semi-Supervised Domain Adaptation to Enhance EEG-Based Cross-Task Mental Workload Classification Performance by Wang, Tao, Ke, Yufeng, Huang, Yichao, He, Feng, Zhong, Wenxiao, Liu, Shuang, Ming, Dong

“…) features for MWL recognition across tasks (MATB-II and n-back). Our results demonstrated that the SCDA method achieved the best cross-task classification performance…”
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Evaluating sleep-stage classification: how age and early-late sleep affects classification performance by Moris, Eugenia, Larrabide, Ignacio

“… The age of the subjects, as well as the moment of sleep (early-night and late-night), were confronted to the performance of the classifier…”
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Improving cross-subject classification performance of motor imagery signals: a data augmentation-focused deep learning framework by Ozelbas, Enes, Tülay, Emine Elif, Ozekes, Serhat

“… However, accurate classification of motor imagery signals remains a challenging task due to the high inter-subject variability and non-stationarity in the electroencephalogram (EEG) data…”
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Improving EEG-Based Cross-Subject Mental Workload Classification Performance with Euclidean-Aligned Periodic and Aperiodic Features by Wang, Tao, Ke, Yufeng, He, Feng, Ming, Dong

“…Enhancing the cross-subject classification performance of EEG-based mental workload (MWL…”
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Feature selection using regularized neighbourhood component analysis to enhance the classification performance of motor imagery signals by Malan, Nitesh Singh, Sharma, Shiru

“… classification performance along with increased computational efficiency. The present study proposes a feature selection algorithm based on neighbourhood component analysis (NCA…”
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Prediction and classification performance of reservoir computing system using mutually delay-coupled semiconductor lasers by Hou, Yu-Shuang, Xia, Guang-Qiong, Jayaprasath, Elumalai, Yue, Dian-Zuo, Yang, Wen-Yan, Wu, Zheng-Mao

“…) subject to optical injection is proposed, and the chaotic time series prediction and signal classification performance for such a system is numerically investigated via the Santa-Fe time-series…”
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Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks by Hamid, Huma, Naseer, Noman, Nazeer, Hammad, Khan, Muhammad Jawad, Khan, Rayyan Azam, Shahbaz Khan, Umar

“… in the brain’s left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs…”
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The effect of target and non-target similarity on neural classification performance: a boost from confidence by Marathe, Amar R., Ries, Anthony J., Lawhern, Vernon J., Lance, Brent J., Touryan, Jonathan, McDowell, Kaleb, Cecotti, Hubert

“… This study address this question by comparing behavioral and neural classification performance across two conditions…”
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A BCI based visual-haptic neurofeedback training improves cortical activations and classification performance during motor imagery by Wang, Zhongpeng, Zhou, Yijie, Chen, Long, Gu, Bin, Liu, Shuang, Xu, Minpeng, Qi, Hongzhi, He, Feng, Ming, Dong

“…) by incorporating synchronous visual scene and proprioceptive electrical stimulation feedback. The goal of this work was to improve sensorimotor cortical activations and classification performance during motor imagery (MI…”
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Mitigating the Impact of Electrode Shift on Classification Performance in Electromyography Applications Using Sliding-Window Normalization by Tanaka, Taichi, Nambu, Isao, Wada, Yasuhiro

“… In a previous study, while transfer learning narrowed the classification performance gap to −1% in an eight-class scenario under electrode shift, they imposed the burden of additional data collection and re-training…”
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Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method by Nazeer, Hammad, Naseer, Noman, Mehboob, Aakif, Khan, Muhammad Jawad, Khan, Rayyan Azam, Khan, Umar Shahbaz, Ayaz, Yasar

“… In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity…”
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Comparative Study of sEMG Feature Evaluation Methods Based on the Hand Gesture Classification Performance by Hellara, Hiba, Barioul, Rim, Sahnoun, Salwa, Fakhfakh, Ahmed, Kanoun, Olfa

“… The investigation is based on several benchmark datasets and one real hand gesture dataset, including 15 hand force exercises collected from 14 healthy subjects using eight commercial sEMG sensors…”
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Improving classification performance of motor imagery BCI through EEG data augmentation with conditional generative adversarial networks by Choo, Sanghyun, Park, Hoonseok, Jung, Jae-Yoon, Flores, Kevin, Nam, Chang S.

“… However, collecting large enough EEG datasets is difficult due to intra-/inter-subject variabilities and experimental costs…”
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Improving Generalized Zero-Shot Learning SSVEP Classification Performance From Data-Efficient Perspective by Wang, Xietian, Liu, Aiping, Wu, Le, Guan, Ling, Chen, Xun

“…: data acquisition, feature extraction, and decision-making. First, prevalent SSVEP-based BCIs overlook the inter-subject variance in visual latency and employ fixed sampling starting time (SST…”
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Enhancing Classification Performance of Functional Near-Infrared Spectroscopy- Brain–Computer Interface Using Adaptive Estimation of General Linear Model Coefficients by Qureshi, Nauman Khalid, Naseer, Noman, Noori, Farzan Majeed, Nazeer, Hammad, Khan, Rayyan Azam, Saleem, Sajid

“… The best classification accuracies achieved for five subjects, for MI versus rest are 79.5, 83.7, 82.6, 81.4, and 84.1% whereas those for MR versus rest…”
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Evaluation of a Novel Speech-in-Noise Test for Hearing Screening: Classification Performance and Transducers' Characteristics by Zanet, Marco, Polo, Edoardo M., Lenatti, Marta, van Waterschoot, Toon, Mongelli, Maurizio, Barbieri, Riccardo, Paglialonga, Alessia

“…One of the current gaps in teleaudiology is the lack of methods for adult hearing screening viable for use in individuals of unknown language and in varying…”
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