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Classification of High-Dimensional Motor Imagery Tasks based on An End-to-end role assigned convolutional neural network [article]

Byeong-Hoo Lee, Ji-Hoon Jeong, Kyung-Hwan Shim, Seong-Whan Lee
2020 arXiv   pre-print
In this study, we collected intuitive EEG data contained the nine different types of movements of a single-arm from 9 subjects.  ...  By imagining numerous movements of a single-arm, decoding performance can be improved without artificial command matching.  ...  Kalafatovich for their discussion of the data analysis.  ... 
arXiv:2002.00210v2 fatcat:udntfl7bqvafdkzh7vkp6abxvq

SessionNet: Feature Similarity-based Weighted Ensemble Learning for Motor Imagery Classification

Byeong-Hoo Lee, Ji-Hoon Jeong, Seong-Whan Lee
2020 IEEE Access  
Additionally, the SessionNet adopts the principle of a hierarchical convolutional neural network that shows robust classification performance regardless of the number of classes.  ...  In this study, we recorded a large intuitive EEG dataset that contained nine types of movements of a single-arm across 12 subjects.  ...  Furthermore, they have tried to decode complex kinematics information such as forearm rotation from EEG signals.  ... 
doi:10.1109/access.2020.3011140 fatcat:ayohrpxzxra6viw5yn4pvfydde

Feel Your Reach: An EEG-Based Framework to Continuously Detect Goal-Directed Movements and Error Processing to Gate Kinesthetic Feedback Informed Artificial Arm Control

Gernot R. Müller-Putz, Reinmar J. Kobler, Joana Pereira, Catarina Lopes-Dias, Lea Hehenberger, Valeria Mondini, Víctor Martínez-Cagigal, Nitikorn Srisrisawang, Hannah Pulferer, Luka Batistić, Andreea I. Sburlea
2022 Frontiers in Human Neuroscience  
In detail, we investigated goal-directed movement detection, decoding of executed and attempted movement trajectories, grasping correlates, error processing, and kinesthetic feedback.  ...  electroencephalogram (EEG).  ...  ACKNOWLEDGMENTS We acknowledge the participation of about 320 participants, about 10 with spinal cord injury, in more than 20 studies performed during the runtime of ''Feel Your Reach''.  ... 
doi:10.3389/fnhum.2022.841312 pmid:35360289 pmcid:PMC8961864 fatcat:mkselw7a5jemnlq5ibvqh2wpte

Decoding Complex Imagery Hand Gestures [article]

Seyed Sadegh Mohseni Salehi, Mohammad Moghadamfalahi, Fernando Quivira, Alexander Piers, Hooman Nezamfar, Deniz Erdogmus
2017 arXiv   pre-print
In this study, we design a new, intuitively connected motor imagery (MI) paradigm using hierarchical common spatial patterns (HCSP) and context information to effectively predict intended hand grasps from  ...  electroencephalogram (EEG) data.  ...  In this paper, we propose classification of four grasps on each hand totaling to 8 options. The grasps mimic four of the required grasps to complete everyday tasks.  ... 
arXiv:1703.02929v1 fatcat:4u4g4dw23zebxgzxqbpk6bw4em

Decoding Voluntary Movement of Single Hand Based on Analysis of Brain Connectivity by Using EEG Signals

Ting Li, Tao Xue, Baozeng Wang, Jinhua Zhang
2018 Frontiers in Human Neuroscience  
Finally, we proposed a model for decoding voluntary movement of the right hand by using a hierarchical linear model (HLM).  ...  Research about decoding neurophysiological signals mainly aims to elucidate the details of human motion control from the perspective of neural activity.  ...  The authors declare that there is no conflict of interest regarding the publication of this paper.  ... 
doi:10.3389/fnhum.2018.00381 pmid:30455636 pmcid:PMC6231062 fatcat:3566q5uzr5elrec2kb24xizbva

Brain-machine interfaces: an overview

Mikhail Lebedev
2014 Translational Neuroscience  
Invasive BMIs hold promise to improve the bandwidth by utilizing multichannel recordings from ensembles of brain neurons.  ...  Additionally, cognitive BMIs have emerged in the domain of higher brain functions.  ...  In support of this idea, they decoded gait kinematics from the EEGs recorded in human subjects walking on a treadmill [60] .  ... 
doi:10.2478/s13380-014-0212-z fatcat:7gqv37xlzfbqtobakl2htxqxzi

Automatic extraction of command hierarchies for adaptive brain-robot interfacing

Matthew Bryan, Griffin Nicoll, Vibinash Thomas, Mike Chung, Joshua R. Smith, Rajesh P. N. Rao
2012 2012 IEEE International Conference on Robotics and Automation  
The user "executes" single symbols from this grammar, which produce sequences of lower-level commands.  ...  We present results from two human subjects who successfully used the hierarchical BCI to control a simulated PR2 robot using brain signals recorded noninvasively through electroencephalography (EEG).  ...  We present results from two human subjects who successfully used the hierarchical BCI to control a simulated PR2 robot using brain signals recorded noninvasively through electroencephalography (EEG).  ... 
doi:10.1109/icra.2012.6225108 dblp:conf/icra/BryanNTCSR12 fatcat:ukslb7ecgnfs3g5rp6lsqhzdze

BCI Control of Whole-Body Simulated Humanoid by Combining Motor Imagery Detection and Autonomous Motion Planning [chapter]

Karim Bouyarmane, Joris Vaillant, Norikazu Sugimoto, Franc̨ois Keith, Jun-ichiro Furukawa, Jun Morimoto
2013 Lecture Notes in Computer Science  
In the second stage of the approach, the humanoid executes the planned motion and the user can exert online some control on the motion being executed through an EEG decoding interface.  ...  biasing and correction of the offline planned motion.  ...  The command c t that comes from BCI decoding system is finally used to modify in real-time this way-point position P w by modifying its height h.  ... 
doi:10.1007/978-3-642-42054-2_39 fatcat:aqvyji6okbfcth557abvjkmfdm

EEG-Based Brain-Computer Interface for Decoding Motor Imagery Tasks within the Same Hand Using Choi-Williams Time-Frequency Distribution

Rami Alazrai, Hisham Alwanni, Yara Baslan, Nasim Alnuman, Mohammad Daoud
2017 Sensors  
The TFFs are processed using a hierarchical classification model to identify the MI task encapsulated within the EEG signals.  ...  These results suggest the feasibility of applying the proposed approach to control dexterous prosthetic hands, which can be of great benefit for individuals suffering from hand amputations.  ...  Then, for each subject, we construct four hierarchical classification models using the TFFs extracted from the four groups of EEG channels.  ... 
doi:10.3390/s17091937 pmid:28832513 pmcid:PMC5621048 fatcat:hk2m6qya3za3xl42njjrwasfhy

EEG topographies provide subject-specific correlates of motor control

Elvira Pirondini, Martina Coscia, Jesus Minguillon, José del R. Millán, Dimitri Van De Ville, Silvestro Micera
2017 Scientific Reports  
We then show that the subject-specific microstates' dynamical organization correlates with the activation of muscle synergies and can be used to decode individual grasping movements with high accuracy.  ...  For this we first extracted subject-specific EEG microstates and muscle synergies during reaching-and-grasping movements in healthy volunteers.  ...  Finally, the high decoding accuracies that we observed, especially when compared to EEG decoding performances (see ref. 53 for a detailed summary of grasp decoding studies), are significant not only  ... 
doi:10.1038/s41598-017-13482-1 pmid:29038516 pmcid:PMC5643537 fatcat:h575bfwkz5emfam66xyl6wrwjy

Unimanual and Bimanual Reach-and-Grasp Actions Can Be Decoded From Human EEG

Andreas Schwarz, Joana Pereira, Reinmar Kobler, Gernot R. Muller-Putz
2019 IEEE Transactions on Biomedical Engineering  
(EEG).  ...  In this study we attempt to discriminate the neural correlates of unimanual (performed with left and right hand) from bimanual reach-and-grasp actions using the low-frequency time-domain electroencephalogram  ...  Höller for the development of the sensor system and assisting in the measurements, M. Burtscher (www.nu-art.at) for the creation of Figure 1 . The authors also thank P. Ofner, A.  ... 
doi:10.1109/tbme.2019.2942974 pmid:31545707 fatcat:pfgmvgxdu5fo3aojp6seq6joqa

Decoding natural reach-and-grasp actions from human EEG

Andreas Schwarz, Patrick Ofner, Joana Pereira, Andreea Ioana Sburlea, Gernot R Müller-Putz
2017 Journal of Neural Engineering  
Our results show peak accuracies around 75% on grand average, which suggest that at best three out of four reach-and-grasp commands could be decoded correctly.  ...  Of particular interest are two studies from Agashe et al [32, 37] who attempted low-frequency reach-to-grasp decoding and classification incorporating palmar and lateral-precision grasp on various objects  ... 
doi:10.1088/1741-2552/aa8911 pmid:28853420 fatcat:7cyisirpuffmfpqjxftogc6q6q

Brain-Machine Interfaces: From Basic Science to Neuroprostheses and Neurorehabilitation

Mikhail A. Lebedev, Miguel A. L. Nicolelis
2017 Physiological Reviews  
Furthermore, BMIs have also incorporated ways to deliver sensory feedback, generated from external actuators, back to the brain.  ...  Brain-machine interfaces (BMIs) combine methods, approaches, and concepts derived from neurophysiology, computer science, and engineering in an effort to establish real-time bidirectional links between  ...  While approaches to EEG decoding are somewhat different from those used for extracting motor commands from streams of neuronal spikes, the general principles of neuronal ensemble physiology (583, 586)  ... 
doi:10.1152/physrev.00027.2016 pmid:28275048 fatcat:26dy2lgh6nfppd3mjgoofyit6u

Deep Learning Based BCI Control of a Robotic Service Assistant Using Intelligent Goal Formulation [article]

Daniel Kuhner, Lukas D.J. Fiederer, Johannes Aldinger, Felix Burget, Martin Völker, Robin T. Schirrmeister, Chau Do, Joschka Boedecker, Bernhard Nebel, Tonio Ball, Wolfram Burgard
2018 bioRxiv   pre-print
Traditional control modalities as touch, speech or gesture commands are not necessarily suited for all users.  ...  As our results show, the system is capable of adapting to frequent changes in the environment and reliably accomplishes given tasks within a reasonable amount of time.  ...  commands cannot yet be achieved directly with non-invasive BCIs, we decode multiple surrogate mentaltasks from EEG using a deep ConvNet approach [15].  ... 
doi:10.1101/282848 fatcat:3fwto6tasbfl5gv4cdbqrj2lca

Decoding Movement From Electrocorticographic Activity: A Review

Ksenia Volkova, Mikhail A. Lebedev, Alexander Kaplan, Alexei Ossadtchi
2019 Frontiers in Neuroinformatics  
Several research groups have advanced toward the development of assistive devices driven by brain-computer interfaces (BCIs) that decode motor commands from multichannel ECoG recordings.  ...  During the last two decades, research utilizing ECoG has considerably grown, including the paradigms where behaviorally relevant information is extracted from ECoG activity with decoding algorithms of  ...  Pistohl et al. (2008) employed regularized LDA to decode two types of grasping movements from the ECoG recorded over the motor cortex.  ... 
doi:10.3389/fninf.2019.00074 pmid:31849632 pmcid:PMC6901702 fatcat:gadwyhntarddpl3sckoiuipgvy
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