Article
Multidisciplinary Sciences
Xulu Sun, Daniel J. O'Shea, Matthew D. Golub, Eric M. Trautmann, Saurabh Vyas, Stephen I. Ryu, Krishna V. Shenoy
Summary: This study explores changes in preparatory activity in the motor cortex accompanying motor learning. It was found that changes in preparatory activity were consistent with learned behavioral modifications and reassociated with updated movements. Additionally, preparatory activity uniformly shifted for all movement directions, including those not altered by learning. These persistent preparatory activity patterns may retain a motor memory of the learned field and support accelerated relearning of the same field.
Article
Engineering, Biomedical
Xiaobo Zhou, Renling Zou, Xiayang Huang
Summary: A wavelet neural network (WNN) was proposed in this study to improve the accuracy of decoding movements from motor imagery EEG signals. Experimental optimization of factors such as mother wavelet, wavelet function, number of channels, and imaging segment time led to an accuracy of 86.27 +/- 6.98%. Comparative experiments with other classifiers showed an improvement in accuracy by 15 to 40%, demonstrating the effectiveness of the WNN method in multi-movement classification.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Chenguang Li, Hongjun Yang, Long Cheng
Summary: The paper proposes a new experimental paradigm using fNIRS signals to achieve higher classification accuracy in a non-laboratory environment with properly designed motion imagination tasks. The experiment demonstrates effective classification results using support vector machine and random forest methods, highlighting the importance of positioning and adjusting fNIRS probes based on motor area theory.
COMPLEX & INTELLIGENT SYSTEMS
(2022)
Article
Biology
Kazumi Kasahara, Charles S. DaSalla, Manabu Honda, Takashi Hanakawa
Summary: This study used simultaneous EEG and fMRI to investigate the neural mechanisms underlying self-regulation of cortical oscillations in healthy participants. The findings suggest that self-regulation of cortical oscillations activates the basal ganglia-cortical network and the neurofeedback control network. Successful self-regulation is associated with striatal activity in the basal ganglia-cortical network, which likely modulates patterns of cortical oscillations. The connectivity of the basal ganglia-cortical network and the neurofeedback control network correlates with strong and weak self-regulation, respectively.
COMMUNICATIONS BIOLOGY
(2022)
Article
Engineering, Biomedical
Jiansheng Niu, Ning Jiang
Summary: This study analyzed the detection and classification of upper-limb movement volitions in a pseudo-online fashion. The results showed that the ensemble model achieved good performance in both detection and classification tasks, providing a promising design for movement decoding in brain-computer interfaces.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Neurosciences
Vahab Youssofzadeh, Sujit Roy, Anirban Chowdhury, Aqil Izadysadr, Lauri Parkkonen, Manoj Raghavan, Girijesh Prasad
Summary: This study analyzed magnetoencephalography (MEG) data to examine the cortical engagement during mental imagery tasks. They found that beta power decrements can be used as markers to map and decode cortical engagement and classify different task types with high accuracy.
HUMAN BRAIN MAPPING
(2023)
Article
Computer Science, Artificial Intelligence
P. S. Thanigaivelu, S. S. Sridhar, S. Fouziya Sulthana
Summary: This paper introduces a capuchin search algorithm(CSA)-optimized incremental support vector machine(ISVM) for the interpretation and classification of EEG-based BCI. The main aim of this paper is to aid in improving the interaction of stroke patients via computers by monitoring their thoughts. The proposed model offers high accuracy, F1-score, and recall values in identifying hand, foot, or tongue movements.
COGNITIVE COMPUTATION
(2023)
Article
Automation & Control Systems
Chun-Yi Lin, Chia-Feng Lu, Han-Mei Lu, Chi-Wen Jao, Po-Shan Wang, Yu-Te Wu
Summary: In this study, an ensemble method combining multiple classifiers was used in a motor imagery-based brain-computer interface system to improve robustness and accuracy. Experiment involving left- and right-hand motor imagery tasks found that the Linear-FTSVM method outperformed other individual classifiers.
INTERNATIONAL JOURNAL OF FUZZY SYSTEMS
(2021)
Article
Engineering, Mechanical
N. Nithya, G. Nallavan
Summary: This research introduces a wearable device for detecting the movements of a cricket player during training, using a data-driven machine learning model for real-time stroke classification. The device does not require cloud services and can perform complex analyses internally.
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE
(2022)
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)
Article
Multidisciplinary Sciences
Peemongkon Wattananon, Khin Win Thu, Soniya Maharjan, Kanphajee Sornkaew, Hsing-Kuo Wang
Summary: Evidence suggests that patients with chronic low back pain have deficits in lumbar multifidus muscle activation and changes in cortical excitability. However, one-session of anodal transcranial direct current stimulation does not induce changes in cortical excitability and lumbar multifidus muscle activation. There is a moderate to strong correlation between peak-to-peak motor evoked potential amplitude and lumbar multifidus muscle activation.
SCIENTIFIC REPORTS
(2023)
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
Mathematics
Md. Khademul Islam Molla, Sakir Ahamed, Ahmed M. M. Almassri, Hiroaki Wagatsuma
Summary: This paper presents a method for recording electrical activities of the human brain using electroencephalography. It decomposes the raw EEG signals and extracts spatial features to classify motor imagery tasks. The proposed method achieves higher classification accuracy in BCI implementation compared to other algorithms.
Article
Neurosciences
Elena Bobrova, Varvara V. Reshetnikova, Elena A. Vershinina, Alexander A. Grishin, Pavel D. Bobrov, Alexander A. Frolov, Yury P. Gerasimenko
Summary: The study suggests that individual brain activity and personality traits can affect the ability to control BCIs, while the level of handedness is a significant factor influencing the success of BCI control.
Article
Engineering, Biomedical
Rizaldi A. Fadli, Yuki Yamanouchi, Lazar Jovanovic, Milos R. Popovic, Cesar Marquez-Chin, Taishin Nomura, Matija Milosevic
Summary: This study evaluated the effectiveness of brain-computer interface (BCI)-controlled functional electrical stimulation (FES) in upper limb motor recovery. The findings showed that both M1 and PFC BCI-FES interventions had approximately 80% success rate, but M1 intervention was faster in detecting the activity. Furthermore, only the M1 intervention effectively elicited changes in corticospinal excitability, while cortical excitability measures did not indicate changes after either M1 or PFC BCI-FES.
JOURNAL OF NEURAL ENGINEERING
(2023)
Article
Chemistry, Analytical
Ying Gu, Evy Cleeren, Jonathan Dan, Kasper Claes, Wim Van Paesschen, Sabine Van Huffel, Borbala Hunyadi
Article
Chemistry, Analytical
Kaat Vandecasteele, Thomas De Cooman, Ying Gu, Evy Cleeren, Kasper Claes, Wim Van Paesschen, Sabine Van Huffel, Borbala Hunyadi
Article
Engineering, Biomedical
Andreas Trollund Boye, Ulrik Qvist Kristiansen, Martin Billinger, Omar Feix do Nascimento, Dario Farina
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2008)
Article
Clinical Neurology
Ying Gu, Kim Dremstrup, Dario Farina
CLINICAL NEUROPHYSIOLOGY
(2009)
Article
Engineering, Biomedical
Omar Feix do Nascimento, Dario Farina
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
(2008)
Article
Agriculture, Dairy & Animal Science
V. M. Thorup, O. F. do Nascimento, F. Skjoth, M. Voigt, M. D. Rasmussen, T. W. Bennedsgaard, K. L. Ingvartsen
JOURNAL OF DAIRY SCIENCE
(2014)
Article
Neurosciences
Ying Gu, Dario Farina, Ander Ramos Murguialday, Kim Dremstrup, Pedro Montoya, Niels Birbaumer
FRONTIERS IN NEUROSCIENCE
(2009)
Article
Neurosciences
Ying Gu, Dario Farina, Ander R. Murguialday, Kim Dremstrup, Niels Birbaumer
FRONTIERS IN NEUROSCIENCE
(2013)
Article
Biology
AEvar Orn Kristinsson, Ying Gu, Soren M. Rasmussen, Jesper Molgaard, Camilla Haahr-Raunkjaer, Christian S. Meyhoff, Eske K. Aasvang, Helge B. D. Sorensen
Summary: Continuous monitoring and early prediction of severe outcomes are crucial in high-risk patients. Current clinical monitoring methods lack time series dynamics and correlations. This study presents a machine learning approach for real-time outcome prediction using continuous recording of vital signs. Encouraging results were obtained, indicating the need for further validation in a clinical setting.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Proceedings Paper
Engineering, Biomedical
Ying Gu, Soren M. Rasmussen, Jesper Molgaard, Camilla Haahr-Raunkjar, Christian S. Meyhoff, Eske K. Aasvang, Helge B. D. Sorensen
Summary: Monitoring post-operative patients is crucial for preventing severe adverse events, and using wearable devices to continuously acquire and intermittently measure vital signs can effectively predict SAE with the help of machine learning techniques.
2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC)
(2021)
Article
Biochemical Research Methods
Dario Farina, Omar Feix do Nascimento, Marie-Francoise Lucas, Christian Doncarli
JOURNAL OF NEUROSCIENCE METHODS
(2007)
Article
Neurosciences
Nazarena Mazzaro, Michael J. Grey, Omar Feix do Nascimento, Thomas Sinkjaer
EXPERIMENTAL BRAIN RESEARCH
(2006)
Article
Engineering, Biomedical
KD Nielsen, AF Cabrera, OF do Nascimento
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2006)