Article
Engineering, Biomedical
Xiuyu Huang, Shuang Liang, Yuanpeng Zhang, Nan Zhou, Witold Pedrycz, Kup-Sze Choi
Summary: This paper proposes a few-shot learning method called temporal episode relation learning (TERL) for generating a reliable model for a target subject with limited MI trials in practical BCI applications. TERL compares MI trials through episode-based training and can be directly applied to new users, improving user experience and enabling real-world MIBCI applications.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Engineering, Biomedical
Jingfeng Bi, Ming Chu
Summary: The goal of this study is to design a single-limb, multi-category motor imagery paradigm and achieve cross-subject intention recognition through the transfer data learning network (TDLNet). The network processes cross-subject EEG signals and assigns weights to signal channels using the Residual Attention Mechanism Module (RAMM), resulting in the best classification results.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Engineering, Biomedical
Do-Yeun Lee, Ji-Hoon Jeong, Byeong-Hoo Lee, Seong-Whan Lee
Summary: The study focused on decoding various forearm movements from EEG signals using a small number of samples. A convolutional neural network based on inter-task transfer learning was proposed, achieving improved classification performance by training the reconstructed ME-EEG signals together with a small amount of MI-EEG signals. The proposed method showed increased performance compared to conventional models, suggesting the feasibility of BCI learning strategies with stable performance using a small calibration dataset and time.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Computer Science, Artificial Intelligence
Wenchao Liu, Changjiang Guo, Chang Gao
Summary: This study proposes a novel approach, based on Riemannian geometry and deep domain adaptation network, to address the issue of long calibration time in EEG signal classification. Experimental results demonstrate that the proposed method outperforms other state-of-the-art methods.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Engineering, Biomedical
Yucun Zhong, Lin Yao, Yueming Wang
Summary: This study proposes a tactile-assisted calibration method for a motor imagery based BCI system, which significantly improves performance and reduces calibration time. By applying tactile stimulation to the hand wrist, the subjects are assisted in the MI task, resulting in better performance compared to the conventional calibration method.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Article
Engineering, Biomedical
Xinru Chen, Jiayu An, Huanyu Wu, Siyang Li, Bin Liu, Dongrui Wu
Summary: This paper proposes a simple yet effective algorithm for decoding motor imagery in brain-computer interfaces. By utilizing dynamic windows and front-end replication, the algorithm is able to reduce the classification time and improve the accuracy.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Review
Computer Science, Artificial Intelligence
Dongrui Wu, Xue Jiang, Ruimin Peng
Summary: A brain-computer interface (BCI) allows users to communicate with external devices using brain signals, and transfer learning (TL) has been widely used in MI-based BCIs to reduce calibration effort and improve utility.
Article
Neurosciences
Kishor Lakshminarayanan, Rakshit Shah, Sohail R. Daulat, Viashen Moodley, Yifei Yao, Deepa Madathil
Summary: This study investigated the effects of combining virtual reality (VR) and action observation on brain activity during motor imagery. The results indicate that combining VR-based action observation enhances brain rhythmic patterns and improves task differentiation compared to motor imagery without action observation.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Engineering, Biomedical
Qizhong Zhang, Bin Guo, Wanzeng Kong, Xugang Xi, Yizhi Zhou, Farong Gao
Summary: The study introduces a method based on a tensor model of a dynamic brain functional network to decode motion intentions, improving the classification accuracy of MI tasks through tensor decomposition and core feature extraction, displaying strong robustness.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Neurosciences
Shun Sawai, Shin Murata, Shoya Fujikawa, Ryosuke Yamamoto, Keisuke Shima, Hideki Nakano
Summary: This study aimed to examine the effect of applying tDCS directly before MI with NFB. The results showed that m-ERD significantly increased in the NFB + tDCS group compared to the NFB group, and MI vividness significantly improved before and after training. This indicates that the combination of tDCS and NFB is more effective in improving MI abilities.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Neurosciences
Tianhao Gao, Yiqian Hu, Jie Zhuang, Yulong Bai, Rongrong Lu
Summary: Approximately two-thirds of stroke survivors experience chronic upper-limb paresis, but available treatment options are limited. Repetitive transcranial magnetic stimulation (rTMS) has shown potential to enhance motor recovery, but its effectiveness remains controversial. This study investigated the efficacy of stimulating different targets in chronic stroke patients with severe upper-limb impairment, using motor imagery-based brain-computer interface training augmented with virtual reality. Both groups showed improvement after the intervention, suggesting the feasibility of inducing activation in specific brain regions during motor imagery tasks as a potential treatment approach. Further research is needed to explore the effectiveness of this intervention.
Article
Engineering, Multidisciplinary
Pasquale Arpaia, Damien Coyle, Francesco Donnarumma, Antonio Esposito, Angela Natalizio, Marco Parvis
Summary: This paper presents a wearable brain-computer interface that enhances motor imagery training through neurofeedback in extended reality. Various feedback modalities, including visual and vibrotactile, were evaluated either singularly or simultaneously. The results showed statistically significant improvement in performance over multiple sessions, demonstrating the functionality of the motor imagery-based instrument even with minimal equipment. The best feedback modality was found to be subject-dependent, with classification accuracy exceeding 80% in some cases.
Article
Engineering, Biomedical
Praveen K. Parashiva, A. P. Vinod
Summary: This study proposes a method of decoding two-directional hand movement using EEG data and improves the performance of BCI system through corrective step using ErrP. The cascaded scheme of direction decoding model and ErrP detection model achieves a higher decoding accuracy in online experiments.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Computer Science, Artificial Intelligence
Binghua Li, Zhiwen Zhang, Feng Duan, Zhenglu Yang, Qibin Zhao, Zhe Sun, Jordi Sole-Casals
Summary: This study introduces a component-mixing strategy (CMS) for motor imagery (MI) data augmentation, which extends empirical mode decomposition into multivariate empirical mode decomposition and intrinsic time-scale decomposition. CMS can generate artificial trials from a few training samples without required training and has been shown to significantly improve binary classification accuracy and area under the curve scores using different algorithms on the BCI Competition IV dataset 2b.
Article
Chemistry, Multidisciplinary
Arnau Dillen, Fakhreddine Ghaffari, Olivier Romain, Bram Vanderborght, Uros Marusic, Sidney Grospretre, Ann Nowe, Romain Meeusen, Kevin De Pauw
Summary: Brain-computer interfaces (BCIs) can enable individuals to interact with devices based on their brain activity. However, the high costs associated with research-grade electroencephalogram (EEG) acquisition devices make them impractical for everyday use. This study demonstrates that decoding movement intention from a limited number of sensors is feasible, opening up the possibility of using commercial sensor devices for BCI control.
APPLIED SCIENCES-BASEL
(2023)
Article
Clinical Neurology
Marta Matamala-Gomez, Ana M. Diaz Gonzalez, Mel Slater, Maria V. Sanchez-Vives
Article
Multidisciplinary Sciences
Dalila Burin, Konstantina Kilteni, Marco Rabuffetti, Mel Slater, Lorenzo Pia
Article
Medicine, Research & Experimental
Daniel Freeman, Rachel Lister, Felicity Waite, Ly-Mee Yu, Mel Slater, Graham Dunn, David Clark
Article
Robotics
Laura Aymerich-Franch, Sameer Kishore, Mel Slater
INTERNATIONAL JOURNAL OF SOCIAL ROBOTICS
(2020)
Article
Clinical Neurology
Marieke A. G. Martens, Angus Antley, Daniel Freeman, Mel Slater, Paul J. Harrison, Elizabeth M. Tunbridge
JOURNAL OF PSYCHOPHARMACOLOGY
(2019)
Article
Multidisciplinary Sciences
Solene Neyret, Xavi Navarro, Alejandro Beacco, Ramon Oliva, Pierre Bourdin, Jose Valenzuela, Itxaso Barberia, Mel Slater
SCIENTIFIC REPORTS
(2020)
Article
Multidisciplinary Sciences
Domna Banakou, Alejandro Beacco, Solene Neyret, Marta Blasco-Oliver, Sofia Seinfeld, Mel Slater
ROYAL SOCIETY OPEN SCIENCE
(2020)
Article
Multidisciplinary Sciences
Joan Llobera, Alejandro Beacco, Ramon Oliva, Gizem Senel, Domna Banakou, Mel Slater
Summary: A new method was proposed to evaluate participant choices in virtual reality applications, using a reinforcement learning agent to suggest possible factor changes and converging on a consistent factor configuration. The experiment showed that participants preferred using teleportation for navigation, full-body representation, responsiveness of virtual human characters, and realistic rendering.
ROYAL SOCIETY OPEN SCIENCE
(2021)
Article
Psychology, Multidisciplinary
Mel Slater, Domna Banakou
Summary: In virtual reality, the Golden Rule can be considered a paradigm for promoting prosocial behavior by creating an illusion of ownership over a virtual body. This phenomenon, known as the Golden Rule Embodiment Paradigm (GREP), has been utilized to influence implicit attitudes and enhance helping behavior in studies conducted within VR environments.
CURRENT DIRECTIONS IN PSYCHOLOGICAL SCIENCE
(2021)
Article
Criminology & Penology
Sofia Seinfeld, Ruud Hortensius, Jorge Arroyo-Palacios, Guillermo Iruretagoyena, Luis E. Zapata, Beatrice de Gelder, Mel Slater, Maria V. Sanchez-Vives
Summary: Domestic violence has long-term negative consequences on children. In this study, men with a history of partner aggression and a control group were placed in virtual reality as a child's perspective to witness a scene of domestic violence. The study found that the experience mainly affected the recognition of angry facial expressions and physiological responses during explicit violent events. This research demonstrates the potential of virtual reality in the rehabilitation and neuropsychological assessment of males with a history of domestic violence.
JOURNAL OF INTERPERSONAL VIOLENCE
(2023)
Article
Computer Science, Interdisciplinary Applications
Mel Slater, Carlos Cabriera, Gizem Senel, Domna Banakou, Alejandro Beacco, Ramon Oliva, Jaime Gallego
Summary: This study created a virtual reality version of a 1983 performance by Dire Straits and carried out two studies to understand participants' responses. The findings revealed that the presence and behavior of the virtual audience elicited negative sentiment, which dominated the overall sentiment over the virtual band. These results underscore the importance of achieving plausibility in virtual reality and highlight the significance of co-design and sentiment analysis in VR scenarios.
Article
Computer Science, Software Engineering
Ramon Oliva, Alejandro Beacco, Jaime Gallego, Raul Gallego Abellan, Mel Slater, Mike Potel
Summary: VR United is a virtual reality application that allows multiple people to interact simultaneously in the same environment. This article demonstrates the successful use of VR United in a remote interview, highlighting its potential for immersive journalism and discussing future developments in this field.
IEEE COMPUTER GRAPHICS AND APPLICATIONS
(2023)
Article
Multidisciplinary Sciences
Gizem Senel, Francisco Macia-Varela, Jaime Gallego, Hatice Pehlivan Jensen, Kasper Hornbaek, Mel Slater
Summary: Change blindness refers to the phenomenon where people fail to notice dramatic changes in their visual environment. In virtual reality, there are differences in the perception of changes in one's own virtual body compared to the body of another person. A study conducted in virtual reality showed that a majority of participants did not notice changes in their own face or the face of the virtual instructor. This suggests that people tend to make inferences about their visual surroundings without paying attention to details, and changes in one's own body may impact self-representation.
Editorial Material
Social Sciences, Interdisciplinary
William Hirst
Article
Psychology, Experimental
Rachel L. Bedder, Daniel Bush, Domna Banakou, Tabitha Peck, Mel Slater, Neil Burgess
Article
Psychology, Biological
Youling Bai, Jianguo Qu, Dan Li, Huazhan Yin
Summary: This study used resting-state functional connectivity analysis to investigate the neural pathways between internet addiction tendency and sleep quality, and found a positive correlation between internet addiction tendency and the strength of functional connectivity within the default-mode network. Furthermore, internet addiction tendency mediated the relationship between these functional couplings and sleep quality.
INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY
(2024)
Article
Psychology, Biological
Jie Zhang, Xiyan Li, Shiwei Liu, Can Xu, Zhijie Zhang
Summary: In this study, electroencephalogram data was analyzed to compare the resting network activation between heavy media multitaskers (HMM) and light media multitaskers (LMM). The results showed that HMM had weaker activation in the attention network, but enhanced activation in the salience network. They also had an enhanced visual network and may feel less comfortable during resting-state periods. This suggests that chronic media multitasking leads to a bottom-up or stimulus-driven allocation of attention for HMM, while LMM use a top-down approach.
INTERNATIONAL JOURNAL OF PSYCHOPHYSIOLOGY
(2024)
