Article
Engineering, Biomedical
Hoseok Choi, Seokbeen Lim, Kyeongran Min, Kyoung-ha Ahn, Kyoung-Min Lee, Dong Pyo Jang
Summary: This study focused on using deep neural networks to explain high-dimensional neurophysiological information extracted from XAI, compared to previous neuroscientific studies. The 3D DNN classifier showed superior accuracy in classifying monkey ECoG data, with the 3D CAM revealing activation patterns in different brain regions during unimanual movements. The study highlights the potential of using XAI for explainability in neuroscience and electrophysiology research.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Chemistry, Analytical
Wongyu Jung, Seokbeen Lim, Youngjong Kwak, Jeongeun Sim, Jinsick Park, Dongpyo Jang
Summary: The study compared BMI learning performances using different frequency bands and brain regions, finding that performance was best in gamma frequency band and worst in alpha band. Better performance was also observed in the primary motor cortex, indicating its positive impact on BMI learning performance.
Article
Chemistry, Analytical
Md Eshrat E. Alahi, Yonghong Liu, Sara Khademi, Anindya Nag, Hao Wang, Tianzhun Wu, Subhas Chandra Mukhopadhyay
Summary: This study presents a new approach to developing a slippery liquid-infused porous surface (SLIPS) on a flexible electrocorticography (ECoG) electrode for better cell adhesion and reduced noise level. The SLIPS coating demonstrated low impedance and biocompatibility, making it suitable for chronic neural recording applications.
Article
Engineering, Biomedical
Yoon Kyung Cho, Chin Su Koh, Youjin Lee, Minkyung Park, Tae Jun Kim, Hyun Ho Jung, Jin Woo Chang, Sang Beom Jun
Summary: This study designed a brain-machine interface (BMI) system that uses sensory-related neural signals for control and combines it with electrical stimulation as reward. Electrodes were used to record electrocorticographic (ECoG) signals from the whisker-related somatosensory cortex of rats, which were then converted into BMI signals to control the movement of a dot on the screen. The rats were trained for 7 days using operant conditioning with medial forebrain bundle (MFB) electrical stimulation to learn the task of moving the dot towards the desired position.
BIOMEDICAL ENGINEERING LETTERS
(2023)
Article
Engineering, Biomedical
Lin Yao, Bingzhao Zhu, Mahsa Shoaran
Summary: This work introduces the use of Riemannian-space features and temporal dynamics of electrocorticography (ECoG) signal combined with modern machine learning tools to improve the decoding accuracy of individual finger movements. By selecting informative biomarkers and exploring the concatenation of features, the proposed method achieved significant improvements in both classification and regression tasks. The results show that the approach outperformed previous methods in detecting individual finger movements and continuous decoding of movement trajectory, with a low time complexity.
JOURNAL OF NEURAL ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Quanduo Liang, Xiangjiao Xia, Xiguang Sun, Dehai Yu, Xinrui Huang, Guanghong Han, Samuel M. Mugo, Wei Chen, Qiang Zhang
Summary: This study utilizes microgels as large crosslinking centers in hydrogel networks to produce hydrogels that closely match the chemomechanical properties of neural tissues. These hydrogels exhibit low modulus, good stretchability, and outstanding fatigue resistance, making them suitable for wearable and implantable sensors. The hydrogels can obtain physiological signals and be successfully implanted in rats for a long-term period. This work contributes to a deeper understanding of biohybrid interfaces and advances the design concepts for implantable neural probes that efficiently obtain physiological information.
Article
Multidisciplinary Sciences
Masoud Amiri, Soheila Nazari, Amir Homayoun Jafari, Bahador Makkiabadi
Summary: This study develops a novel bidirectional brain-machine interface (BMI) algorithm, which utilizes a neural network model connecting the sensory cortex and motor cortex to achieve information exchange and control between the brain and mechanical external device. The algorithm has successfully controlled the movement of a mechanical arm in both simulation and experimental data, demonstrating good performance.
Article
Chemistry, Multidisciplinary
Yong-Jin Park, Yun Goo Ro, Young-Eun Shin, Cheolhong Park, Sangyun Na, Yoojin Chang, Hyunhyub Ko
Summary: This study proposes a multi-layered micropatterned triboelectric nanogenerator (M-TENG) that generates multiple charges from a single touch by utilizing multiple friction layers and distinct spacers between layers. By integrating M-TENGs with an organic electrochemical transistor, a low energy consumption artificial synaptic device is successfully demonstrated, and long-term plasticity is emulated through continuous memory training.
Article
Engineering, Biomedical
Pablo Ortega, A. Aldo Faisal
Summary: The study shows that by using EEG and fNIRS signals along with deep learning methods, hand-specific forces can be effectively decoded, with the cnnatt model performing better in signal fusion. Detection of force generation is crucial for performance improvement, and at the cortical level, forces from each hand are encoded differently.
JOURNAL OF NEURAL ENGINEERING
(2021)
Article
Computer Science, Artificial Intelligence
Mohammadali Ganjali, Alireza Mehridehnavi, Sajed Rakhshani, Abed Khorasani
Summary: Stable decoding of movement parameters is crucial for the success of brain-machine interfaces (BMIs). This study proposes an automatic unsupervised algorithm that addresses the issue of neural activity instability by aligning manifolds and reducing dimensions. The method shows higher decoding performance compared to a state-of-the-art unsupervised BMI stabilizer, offering a promising solution for achieving stable and accurate movement decoding in BMI applications.
INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
(2023)
Article
Biology
Omair Ali, Muhammad Saif-ur-Rehman, Tobias Glasmachers, Ioannis Iossifidis, Christian Klaes
Summary: This study introduces a novel hybrid model called ConTraNet, which combines the strengths of CNN and Transformer neural networks, and achieves significant improvement in classification performance with limited training data.
COMPUTERS IN BIOLOGY AND MEDICINE
(2024)
Article
Chemistry, Multidisciplinary
Vicente Quiles, Laura Ferrero, Eduardo Ianez, Mario Ortiz, Jose M. Cano, Jose M. Azorin
Summary: This paper presents a preliminary real-time BMI for the speed control of an exoskeleton, focusing on the underdeveloped topic of control of assistive devices by voluntary user intention. The study proposes an offline analysis to select intention patterns based on optimum features and electrodes, and tests the selection through pseudo-online analysis. The viability of the approach is also checked through a case study. The pros and cons of implementing closed-loop control of speed change for the H3 exoskeleton through EEG analysis are discussed.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Biomedical
Serkan Kirik, Sengul Dogan, Mehmet Baygin, Prabal Datta Barua, Caner Feyzi Demir, Tugce Keles, Arif Metehan Yildiz, Nursena Baygin, Ilknur Tuncer, Turker Tuncer, Ru-San Tan, U. Rajendra Acharya
Summary: We developed an efficient handcrafted feature engineering model for EEG-based language identification using four directed graphs modeled on Feynman graph patterns (FGPat). We collected a dataset of 3252 EEG signals from native English-speaking Nigerian-born and Turkish subjects. In our FGPat18 model, input EEG signals and their Q wavelet transform-decomposed wavelet bands were used to extract textural and statistical features. The obtained feature vectors were input to a classification algorithm to achieve high accuracy rates. The FGPat18 model achieved a classification accuracy rate of 99.38% with 10-fold cross-validation.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Computer Science, Artificial Intelligence
Xiwei She, Theodore W. Berger, Dong Song
Summary: A double-layer, multiple temporal-resolution classification model was built to decode single-trial spatiotemporal patterns of spikes. The model included a wide range of temporal resolutions of neural features by using a large number of classifiers with different numbers of B-spline knots. A second-layer classifier fused multiple temporal resolutions to accurately classify spatiotemporal patterns of spikes.
NEURAL COMPUTATION
(2021)
Article
Multidisciplinary Sciences
Rabia Avais Khan, Nasir Rashid, Muhammad Shahzaib, Umar Farooq Malik, Arshia Arif, Javaid Iqbal, Mubasher Saleem, Umar Shahbaz Khan, Mohsin Tiwana
Summary: Robotics and artificial intelligence have played a significant role in developing assistive technologies for people with motor disabilities. In this study, a novel framework for classifying binary-class electroencephalogram (EEG) data has been proposed. The framework achieved high classification accuracies with logistic regression classifier on two datasets, indicating its potential for real-time Brain-Computer Interface (BCI) systems and 2-class Motor Imagery signals classification applications.
Article
Engineering, Biomedical
Robert D. Flint, Joshua M. Rosenow, Matthew C. Tate, Marc W. Slutzky
JOURNAL OF NEURAL ENGINEERING
(2017)
Article
Neurosciences
Robert D. Flint, Michael R. Scheid, Zachary A. Wright, Sara A. Solla, Marc W. Slutzky
JOURNAL OF NEUROSCIENCE
(2016)
Review
Neurosciences
Max O. Krucoff, Shervin Rahimpour, Marc W. Slutzky, V. Reggie Edgerton, Dennis A. Turner
FRONTIERS IN NEUROSCIENCE
(2016)
Article
Multidisciplinary Sciences
Karthikeyan Balasubramanian, Mukta Vaidya, Joshua Southerland, Islam Badreldin, Ahmed Eleryan, Kazutaka Takahashi, Kai Qian, Marc W. Slutzky, Andrew H. Fagg, Karim Oweiss, Nicholas G. Hatsopoulos
NATURE COMMUNICATIONS
(2017)
Article
Neurosciences
Mukta Vaidya, Karthikeyan Balasubramanian, Joshua Southerland, Islam Badreldin, Ahmed Eleryan, Kelsey Shattuck, Suchin Gururangan, Marc Slutzky, Leslie Osborne, Andrew Fagg, Karim Oweiss, Nicholas G. Hatsopoulos
JOURNAL OF NEUROPHYSIOLOGY
(2018)
Letter
Neurosciences
Marc W. Slutzky, Robert D. Flint
JOURNAL OF NEUROPHYSIOLOGY
(2018)
Review
Clinical Neurology
Marc W. Slutzky
Article
Neurosciences
Emily M. Mugler, Matthew C. Tate, Karen Livescu, Jessica W. Templer, Matthew A. Goldrick, Marc W. Slutzky
JOURNAL OF NEUROSCIENCE
(2018)
Article
Engineering, Biomedical
Miguel Angrick, Christian Herff, Emily Mugler, Matthew C. Tate, Marc W. Slutzky, Dean J. Krusienski, Tanja Schultz
JOURNAL OF NEURAL ENGINEERING
(2019)
Article
Engineering, Biomedical
Mukta Vaidya, Robert D. Flint, Po T. Wang, Alex Barry, Yongcheng Li, Mohammad Ghassemi, Goran Tomic, Jun Yao, Carolina Carmona, Emily M. Mugler, Sarah Gallick, Sangeeta Driver, Nenad Brkic, David Ripley, Charles Liu, Derek Kamper, An H. Do, Marc W. Slutzky
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
(2019)
Article
Neurosciences
Robert D. Flint, Matthew C. Tate, Kejun Li, Jessica W. Templer, Joshua M. Rosenow, Chethan Pandarinath, Marc W. Slutzky
Editorial Material
Engineering, Biomedical
Marc W. Slutzky
NATURE BIOMEDICAL ENGINEERING
(2020)
Article
Neurosciences
Larry Y. Cheng, Tiffanie Che, Goran Tomic, Marc W. Slutzky, Ken A. Paller
Summary: Memory reactivation during sleep contributes to faster and more efficient learning of action execution, as demonstrated in this study. This finding suggests that sleep plays a role in supporting the learning of novel actions.
JOURNAL OF NEUROSCIENCE
(2021)
Article
Clinical Neurology
Emily M. Mugler, Goran Tomic, Aparna Singh, Saad Hameed, Eric W. Lindberg, Jon Gaide, Murad Alqadi, Elizabeth Robinson, Katherine Dalzotto, Camila Limoli, Tyler Jacobson, Jungwha Lee, Marc W. Slutzky
NEUROREHABILITATION AND NEURAL REPAIR
(2019)