Article
Neurosciences
Vadivelan Ramu, Kishor Lakshminarayanan
Summary: The purpose of this study was to evaluate the effect of vibrotactile stimulation prior to repeated complex motor imagery of finger movements using the non-dominant hand on motor imagery performance. The results showed that motor imagery performance was better in terms of event-related desynchronization and digit classification with vibrotactile stimulation compared to without stimulation.
FRONTIERS IN NEUROSCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Paula Soriano-Segura, Eduardo Ianez, Mario Ortiz, Vicente Quiles, Jose M. Azorin
Summary: This study proposes a new method based on EEG signals to detect the intention to change direction during gait, providing more accurate and natural use of external devices, with promising potential for future BCIs.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2021)
Article
Neurosciences
Nikki Leeuwis, Sue Yoon, Maryam Alimardani
Summary: This study focused on comparing the functional connectivity of 54 novice MI-BCI users in different network scales during resting state, left vs. right-hand motor imagery task, and the transition between the two phases. The results showed that in the alpha band, functional connectivity in the right hemisphere was increased in High compared to Low aptitude MI-BCI users during motor imagery. These findings suggest that connectivity might be a valuable feature in MI-BCI classification and in solving the MI-BCI inefficiency problem.
FRONTIERS IN HUMAN NEUROSCIENCE
(2021)
Article
Neurosciences
Maitreyee Wairagkar, Yoshikatsu Hayashi, Slawomir J. Nasuto
Summary: This study investigates changes in long-range temporal correlations (LRTCs) in broadband EEG during different types of movements and motor imagery tasks, contrasting them with LRTCs in alpha oscillation amplitude typically found in the literature. Results indicate that broadband LRTC increases significantly during motor tasks while alpha oscillation LRTC decreases, suggesting complementarity between fast and slow neuronal scale-free dynamics during movement and motor imagery.
FRONTIERS IN NEUROSCIENCE
(2021)
Article
Biology
Huiying Li, Dongxue Zhang, Jingmeng Xie
Summary: A novel dual-attention-based adversarial network for motor imagery classification (MI-DABAN) is proposed, which leverages multiple subjects' knowledge to improve a single subject's classification performance. The method employs a clever adversarial learning method and two unshared attention blocks, resulting in effective and superior classification performance.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Review
Chemistry, Analytical
Amardeep Singh, Ali Abdul Hussain, Sunil Lal, Hans W. Guesgen
Summary: The paper reviews recent advances and current status of MI-BCI systems, highlighting that the technology is mainly confined to controlled environments and commercial deployment is still pending.
Article
Computer Science, Artificial Intelligence
Kaishuo Zhang, Neethu Robinson, Seong-Whan Lee, Cuntai Guan
Summary: In this paper, five adaptation schemes for deep convolutional neural networks are proposed to improve the performance of Brain-Computer Interface systems. The highest subject independent performance in decoding hand motor imagery was achieved, with significant improvement compared to baseline models. Using transfer learning methods can lead to better accuracy in decoding hand motor imagery.
Article
Engineering, Biomedical
Marta Borras, Sergio Romero, Joan F. Alonso, Alejandro Bachiller, Leidy Y. Serna, Carolina Migliorelli, Miguel A. Mananas
Summary: This study assessed the effect of a limited number of trials on motor-related activity during upper limb movements. The findings suggested that 50 trials can be an appropriate number to obtain stable motor-related features.
JOURNAL OF NEURAL ENGINEERING
(2022)
Review
Neurosciences
Jaime Peter, Francesca Ferraioli, Dave Mathew, Shaina George, Cameron Chan, Tomisin Alalade, Sheilla A. Salcedo, Shannon Saed, Elisa Tatti, Angelo Quartarone, M. Felice Ghilardi
Summary: This study reviews the manifestation of movement-related oscillations in various neurological and psychiatric disorders. It is found that these abnormalities are present across different pathologies, development, and aging. The study also suggests that cognition and movement are closely related processes that may share common mechanisms regulated by beta modulation.
FRONTIERS IN NEUROSCIENCE
(2022)
Article
Biology
Xiao-Cong Zhong, Qisong Wang, Dan Liu, Jing-Xiao Liao, Runze Yang, Sanhe Duan, Guohua Ding, Jinwei Sun
Summary: In this paper, a deep domain adaptation framework with correlation alignment (DDAF-CORAL) is proposed to address the problem of distribution divergence in motor imagery classification across domains.
COMPUTERS IN BIOLOGY AND MEDICINE
(2023)
Article
Engineering, Biomedical
Hongyi Zhi, Zhuliang Yu, Tianyou Yu, Zhenghui Gu, Jian Yang
Summary: The proposed multi-domain temporal-spatial-frequency convolutional neural network (TSFCNet) enhances the decoding performance of Motor Imagery (MI) by effectively utilizing different EEG feature domains. By performing multiple independent convolution operations in the spatial, frequency, and time-frequency domains and aggregating features using average pooling and variance layers, TSFCNet outperforms other models in terms of classification accuracy and kappa values, promising to improve the decoding performance of MI BCIs.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2023)
Review
Chemistry, Analytical
Arrigo Palumbo, Vera Gramigna, Barbara Calabrese, Nicola Ielpo
Summary: Innovative aids, devices, and assistive technologies are needed to help individuals with severe disabilities live independently and improve overall health. Brain-Computer Interfaces using EEG data show potential for enhancing wheelchair control and movement in people with significant health challenges.
Article
Engineering, Biomedical
Aishi Zhou, Li Zhang, Xiaoyang Yuan, Changsheng Li
Summary: A novel method based on signal prediction was proposed in this study to achieve high motor imagery (MI) classification accuracy by using EEG recorded from only a small number of electrodes. The method built a signal prediction model using regression technique to estimate the full-channel EEG signals from the few-channel EEG signals on the central region, and then used the predicted full-channel EEG signals for MI feature extraction and classification. The proposed method outperformed the traditional method that directly used few-channel EEG and was comparable to or even superior to the traditional method that directly used full-channel EEG.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Lin Yao, Ning Jiang, Natalie Mrachacz-Kersting, Xiangyang Zhu, Dario Farina, Yueming Wang
Summary: This study proposes a method to reduce the calibration time in somatosensory brain-computer interfaces by using tactile-induced oscillation. The results show that the performance of the sensory calibration is significantly better than the conventional calibration, with an average 5.1% improvement in accuracy and a 39.3% faster reaching of above 70% accuracy.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2022)
Article
Engineering, Biomedical
Lie Yang, Yonghao Song, Ke Ma, Enze Su, Longhan Xie
Summary: The proposed motor imagery EEG decoding method based on feature separation effectively improves the decoding accuracy by separating class-related features and class-independent features using the FSNAL network. Experimental results show that the method outperforms other state-of-the-art methods on public EEG datasets, demonstrating its potential for improving the performance of motor imagery BCI systems in the future.
JOURNAL OF NEURAL ENGINEERING
(2021)
Letter
Clinical Neurology
Peter T. Lin, Erika K. Ross, Paula Chidester, Kathryn H. Rosenbluth, Samuel R. Hamner, Serena H. Wong, Terence D. Sanger, Mark Hallett, Scott L. Delp
MOVEMENT DISORDERS
(2018)
Article
Neurosciences
Logan Schneider, Elise Houdayer, Ou Bai, Mark Hallett
JOURNAL OF COGNITIVE NEUROSCIENCE
(2013)
Article
Multidisciplinary Sciences
Qi Li, Shuai Liu, Jian Li, Ou Bai
Article
Engineering, Mechanical
Samuel Lawoyin, Ding-Yu Fei, Ou Bai
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING
(2015)
Article
Behavioral Sciences
Zhaohua Lu, Qi Li, Ning Gao, Jingjing Yang, Ou Bai
BRAIN AND BEHAVIOR
(2019)
Article
Computer Science, Information Systems
Tao Xue, Zi-wei Wang, Tao Zhang, Ou Bai, Meng Zhang, Bin Han
FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING
(2020)
Article
Chemistry, Analytical
Masudur R. Siddiquee, Roozbeh Atri, J. Sebastian Marquez, S. M. Shafiul Hasan, Rodrigo Ramon, Ou Bai
Article
Clinical Neurology
Elizabeth Erickson-DiRenzo, Fiene Marie Kuijper, Daniel A. N. Barbosa, Erika A. Lim, Peter T. Lin, Melanie A. Lising, Yuhao Huang, C. Kwang Sung, Casey H. Halpern
PARKINSONISM & RELATED DISORDERS
(2020)
Article
Engineering, Biomedical
Meiyan Zhang, Dan Liu, Qisong Wang, Boqi Zhao, Ou Bai, Jinwei Sun
Summary: This study presents an alertness detection method based on EEG signals using a decision fused BP neural network. By analyzing the EEG changes of participants during tasks, frequency domain features are extracted and input into a BP neural network for classification. The results show that the proposed method has good classification performance and enables continuous monitoring of alertness to prevent potential catastrophes.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Correction
Clinical Neurology
J. Sebastian Marquez, S. M. Shafiul Hasan, Masudur R. Siddiquee, Corneliu C. Luca, Virendra R. Mishra, Zoltan Mari, Ou Bai
FRONTIERS IN NEUROLOGY
(2022)
Article
Engineering, Biomedical
Lin Tao, Tianao Cao, Qisong Wang, Dan Liu, Ou Bai, Jinwei Sun
Summary: This study proposes the application of a classifier based on kernel functions in brain-computer interface (BCI) to address BCI illiteracy. By mapping data to multidimensional and nonlinear spaces, hidden features can be extracted. Compared to existing methods, this approach achieves higher classification accuracy on a public BMI dataset.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Clinical Neurology
J. Sebastian Marquez, Ronny P. Bartsch, Moritz Guenther, S. M. Shafiul Hasan, Or Koren, Meir Plotnik, Ou Bai
Summary: The study investigates the neural changes in Parkinson's disease patients with freezing of gait using ambulatory electroencephalography event-related features. Results showed that definite freezers exhibited reduced alpha desynchronization and low-beta desynchronization at specific time points during walking. Differences in motor potentials were observed between groups in both preparation and execution of walking. The study findings provide valuable insights for drug development and personalized care in patients with freezing of gait, as well as evaluating nonpharmacological therapies for Parkinson's disease.
PARKINSONS DISEASE
(2023)
Article
Computer Science, Information Systems
S. M. Shafiul Hasan, J. Sebastian Marquez, Masudur R. Siddiquee, Ding-Yu Fei, Ou Bai
Summary: This study investigated neural changes related to human intention to accelerate during walking and explored the feasibility of predicting acceleration intention from real-time EEG data. By collecting EEG, IMU, and GRF signals from a healthy subject and applying classifiers for classification between constant speed and acceleration, promising classification performance was observed in offline, pseudo-online, and real-time scenarios.
Article
Computer Science, Information Systems
Masudur R. Siddiquee, S. M. Shafiul Hasan, J. Sebastian Marquez, Rodrigo Nicolas Ramon, Ou Bai
Article
Clinical Neurology
Ou Bai, Dandan Huang, Ding-Yu Fei, Richard Kunz
NEUROREHABILITATION
(2014)
Article
Clinical Neurology
Jaakko Vallinoja, Timo Nurmi, Julia Jaatela, Vincent Wens, Mathieu Bourguignon, Helena Maenpaa, Harri Piitulainen
Summary: The study aimed to assess the effects of lesions related to spastic diplegic cerebral palsy on functional connectivity. Using multiple imaging modalities, the researchers found enhanced functional connectivity in the sensorimotor network of individuals with spastic diplegic cerebral palsy, which was not correlated with hand coordination performance.
CLINICAL NEUROPHYSIOLOGY
(2024)
Article
Clinical Neurology
Francesca Ginatempo, Nicola Loi, John C. Rothwell, Franca Deriu
Summary: This study comprehensively investigated sensorimotor integration in the cranial-cervical muscles of healthy adults and found that the integration of sensory inputs with motor output is profoundly influenced by the type of sensory afferent involved and the functional role played by the target muscle.
CLINICAL NEUROPHYSIOLOGY
(2024)
