Skip to main content
Key Specialties
Cardiology Diabetology Endocrinology Gastroenterology Geriatrics and Gerontology Gynecology and Obstetrics Hematology Infectious Disease Internal Medicine Nephrology Neurology Oncology Pediatrics Respiratory Medicine Rheumatology Surgery
Congress Coverage
2024 ACC Congress 2023 ESMO Congress 2023 EASD Congress 2023 ERS Congress 2023 ESC Congress 2023 EHA Congress 2023 ASCO Annual Meeting
Clinical Collections
Advances in Alzheimer’s

At a glance: The ONWARDS insulin icodec trials

A round-up of the ONWARDS phase 3 clinical trials evaluating the novel weekly insulin icodec in people with diabetes.
ADVANCED SEARCH
Log in
Top
Brain Topography
Published in: Brain Topography 2/2019

Open Access 01-03-2019 | Electroencephalography | Original Paper

Single-Trial Recognition of Imagined Forces and Speeds of Hand Clenching Based on Brain Topography and Brain Network

Authors: Xin Xiong, Yunfa Fu, Jian Chen, Lijun Liu, Xiabing Zhang

Published in: Brain Topography | Issue 2/2019

Login to get access

Abstract

To provide optional force and speed control parameters for brain–computer interfaces (BCIs), an effective feature extraction method of imagined force and speed of hand clenching based on electroencephalography (EEG) was explored. Twenty subjects were recruited to participate in the experiment. They were instructed to perform three different actual/imagined hand clenching force tasks (4 kg, 10 kg, and 16 kg) and three different hand clenching speed tasks (0.5 Hz, 1 Hz, and 2 Hz). Topographical maps parameters and brain network parameters of EEG were calculated as new features of imagined force and speed of hand clenching, which were classified by three classifiers: linear discrimination analysis, extreme learning machines and support vector machines. Topographical maps parameters were better for recognition of the hand clenching force task, while brain network parameters were better for recognition of the hand clenching speed task. After a combination of five types of features (energy, power spectrum of the autoregressive model, wavelet packet coefficients, topographical maps parameters and brain network parameters), the recognition rate of the hand clenching force task was 97%, and that of the hand clenching speed task was as high as 100%. The brain topographical and the brain network parameters are expected to improve the accuracy of decoding the EEG signal of imagined force and speed of hand clenching. A more efficient brain network may facilitate the recognition of force/speed of hand clenching. Combined features could significantly improve the single-trial recognition rate of imagined forces and speeds of hand clenching. The current study provides a new idea for the imagined force and speed of hand clenching that offers more control intention instructions for BCIs.
Appendix
Available only for authorised users
Literature
Ahn M, Jun SC (2015) Performance variation in motor imagery brain-computer interface: a brief review. J Neurosci Methods 243:103PubMedCrossRef
Ang KK, Chua KS, Phua KS, Wang C, Chin ZY (2015) A randomized controlled trial of EEG- based motor imagery brain-computer interface robotic rehabilitation for stroke. Clin EEG Neurosci 46:310PubMedCrossRef
Antonova I, Bänninger A, Dierks T, Griskova- Bulanova I, Koenig T (2015) Differential recruitment of brain networks during visuospatial and color processing: evidence from ERP microstates. Neuroscience 305:128–138PubMedCrossRef
Aschersleben G (2002) Temporal control of movements in sensorimotor synchronization. Brain Cognit 48:66–79CrossRef
Baccalá LA, Sameshima K (2001) Partial directed coherence: a new concept in neural structure determination. Biol Cybern 84:463–474CrossRef
Baron DC, Ramirez JM, Baker M, Alarcon-Aquino V, Carrera O (2012) A BCI motor imagery experiment based on parametric feature extraction and fisher criterion. Proceedings of the 22nd international conference on electrical communications and computers. 257–264
Baykara E, Ruf C, Fioravanti C, Käthner I, Simon N (2016) Effects of training and motivation on auditory P300 brain-computer interface performance. Clin Neurophysiol 127:379PubMedCrossRef
Biasucci A, Chavarriaga R, Hamner B, Leeb R (2011) Combining discriminant and topographic information in BCI: preliminary results on stroke patients. Int IEEE/EMBS Conf Neural Eng 1359(1): 290–293
Brodbeck V, Kuhn A, Wegner FV, Morzelewski A (2012) EEG microstates of wakefulness and NREM sleep. Neuroimage 62:2129–2139PubMedCrossRef
Brunner C, Delorme A, Makeig S (2013) EEGLAB: an open source MATLAB toolbox for electrophy-siological research. Biomed Eng 58:3234
Carlson T, Millan J (2013a) Brain-controlled wheelchairs: a robotic architecture. IEEE Robot Automat Mag 1:65–73CrossRef
Carlson T, Tonin L, Perdikis S, Leeb R, Milla´n J (2013b) A hybrid BCI for enhanced control of a telepresence robot. Proceeding of the 35th Annual International IEEE EMBS Conference: 3097–3100
Chen RG, Zhang L, Wang XL, Wang XM, Ming D, Zhou P (2015) Research on Brain network under self-adaptive multi-dimensional N-back cognition tasks. Space Med Med Eng 28:391–396
Doud AJ, Lucas JP, Pisansky MT, He B (2011) Continuous three-dimensional control of a virtual helicopter using a motor imagery based brain-computer interface. PLoS ONE 10
Douw L, Schoonheim MM, Landi D, Meer MLVD, Geurts JJG (2011) Cognition is related to resting-state small-world network topology: an magnetoencephalographic study. Neuroscience 175(1):169–177PubMedCrossRef
Edelman BJ, Baxter B, He B (2016) EEG source imaging enhances the decoding of complex right-hand motor imagery tasks. IEEE Trans Biomed Eng 63(1):4–14PubMedCrossRef
Farina D, Nascimento OF, Lucas MF, Doncarli C (2007) Optimization of wavelets for classification of movement related cortical potentials generated by variation of force-related parameters. J Neurosci Methods 162:357–363PubMedCrossRef
Fu YF, Xiong X, Jiang CH (2017) Imagined hand clenching force and speed modulate brain activity and are classified by NIRS combined with EEG. IEEE Trans Neural Syst Rehabil Eng 25:1641–1652PubMedCrossRef
Gang W, Sun Z, Tao R, Tao R (2016) Epileptic seizure detection based on partial directed coherence analysis. IEEE J Biomed Health Inform 20:873–879CrossRef
Gerloff C, Richard J, Hadley J, Schulman AE, Honda M (1998) Functional coupling and regional activation of human cortical motor areas during simple, internally paced and externally paced finger movements. Brain 121: 1513–1531
Gu Y, Dremstrup K, Farina D (2009a) Single trial discrimination of type and speed of wrist movements from EEG recordings. Clin Neurophysiol 120:1596–1600PubMedCrossRef
Gu Y, Farina D, Murguialday AR, Dreamstrup K, Montoya P (2009b) Offline identification of imagined speed of wrist movements in paralyzed ALS patients from single-trial EEG. Front Neurosci 3:62PubMedPubMedCentral
Gu Y, do Nascimento OF, Lucase MF, Farina D (2009c) Identification of task parameters from movement ralated cortical potentials. Med Biol Eng Comput 47:1257–1264PubMedCrossRef
He B, Gao S, Yuan H, Wolpaw JR (2013) Brain-computer interface. In: B. He (ed). Neural engineering. 2nd edn. Springer, Boston, 87–151
He B, Baxter B, Edelman BJ, Cline CC, Ye W W W (2015) Noninvasive brain-computer interfaces based on sensorimotor rhythms. Proc IEEE 6: 907–925
Hsu WY (2010) EEG-based motor imagery classification using neuro-fuzzy prediction and wavelet fractal features. J Neurosci Methods 189(2):295–302PubMedCrossRef
Huang GB, Zhou H, Ding X (2012) Extreme learning machine for regression and multiclass classification. IEEE Trans Syst Man Cybern 42:513–529CrossRef
Jiang TZ, Liu Y, Li YH (2009) Brain networks: from anatomy to dynamics. Chin Bull Life Sci 21:181–188
Jochumsen M, Niazi IK, Mrachaczkersting N, Farina D, Dremstrup K (2013) Detection and classification of movement-related cortical potentials associated with task force and speed. J Neural Eng 5:056015CrossRef
Khanna A, Pascualleone A, Farzan F (2014) Reliability of resting-state microstate features in electroencephalography. PLoS ONE 12:1–12
Khanna A, Pascualleone A, Michel CM, Farzan F (2015) Microstates in resting-state EEG: current status and future directions. Neurosci Biobehav Rev 49:105–113PubMedCrossRef
Koenig T, Stein M, Grieder M, Kottlow M (2014) A tutorial on data-driven methods for statistically assessing ERP topographies. Brain Topogr 27:72–83PubMedCrossRef
Kopp B, Kunkel A, Muller G, Muhlnickel W, Flor H (2000) Steady-state movement related potentials evoked by fast repetitive movements. Brain Topogr 13:21–28CrossRef
LaFleur K (2013) Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain-computer interface. J. Neural Eng 10(4): 046003PubMedCrossRef
Laufs H, Cassady K, Doud A, Shades K, Rogin E (2012) Functional imaging of seizures and epilepsy: evolution from zones to networks. Curr Opin Neurol 25(2):194–200PubMedCrossRef
Lehmann D, Ozaki H, Pal I (1987) EEG alpha map series: brain micro-states by space-oriented adaptive segmentation. Electroencephalogr Clin Neurophysiol 67:271–288PubMedCrossRef
Lehmann D, Pascual-Marqui RD, Strik WK, Koenig T (2010) Core networks for visual-concrete and abstract thought content: a brain electric microstate analysis. Neuroimage 49:1073–1079PubMedCrossRef
Li Y, Gao X, Liu H, Gao S (2004) Classification of single-trial electroencephalogram during finger movement. IEEE Trans Biomed Eng 51:1019–1025PubMedCrossRef
Li F, Chen B, Li H, Zhang T, Wang F (2016) The time-varying networks in P300: a task-evoked EEG study. IEEE Trans Neural Syst Rehabilit Eng 24(7):725–733CrossRef
Liang X, Wang JH, He Y (2010) Human connectome: Structural and functional brain networks. Chin Sci Bull 55:1565–1583CrossRef
Ma N, Wang XM, Tian M, Liu J, Qi HZ, Ming D, Zhou P (2016) Brain network research under somatosensory vibration stimulation based on partial directed coherence. Space Med Med Eng 29:334–338
Minguillon J, Pirondini E, Coscia M, Leeb R (2014) Modular organization of reaching and grasping movements investigated using EEG microstates. Conf Proc IEEE Eng Med Biol Soc 2093–2096
Mognon A, Jovicich J, Bruzzone L, Buiatti M (2011) ADJUST: an automatic EEG artifact detector based on the joint use of spatial and temporal features. Psychophysiology 2:229–240CrossRef
Morlet J, Arens G, Fourgeau E (1982) Wave propagation and sampling theory—part I: sampling theory and complex waves. Geophysics 2:222–236CrossRef
Nascimento OF, Farina D (2008) Movement-related cortical potentials allow discrimination of rate of torque development in imaginary isometric plantar flexion. IEEE Trans Biomed Eng 55:2675–2678PubMedCrossRef
Pascualmarqui RD, Michel CM, Lehmann D (1995) Segmentation of Brain Electrical Activity into microstates: model estimation and validation. IEEE Trans Biomed Eng 42:658–665CrossRef
Pfurtscheller G, Lopes FH (1999) Event-related EEG/MEG synchronization and desynchronization: basic principles. Clin Neurophysiol 110:1842–1857PubMedCrossRef
Pirondini E, Coscia M, Minguillon J, Millán JDR, Ville DVD (2017) EEG topographies provide subject-specific correlates of motor control. Sci Rep 7(1):13229PubMedPubMedCentralCrossRef
Qin Y, Xu P, Yao DZ (2010) A comparative study of different references for EEG default mode network: the use of the infinity reference. Clin Neurophysiol 121:1981–1991PubMedCrossRef
Remsik A, Young B, Vermilyea R, Kiekhoefer L, Abrams J (2016) A review of the progression and future implications of brain-computer interface therapies for restoration of distal upper extremity motor function after stroke. Expert Rev Med Devices 13:445–454PubMedPubMedCentralCrossRef
Rubinov M, Sporns O (2010) Complex network measures of brain connectivity: uses and interpretations. Neuroimage 52:1059–1069PubMedCrossRef
Schlegel F, Lehmann D, Faber PL (2012) EEG Microstates during resting represent personality differences. Brain Topogr 25:20–26PubMedCrossRef
Schlögl A, Flotzinger D, Pfurtscheller G (1997) Adaptive autoregressive modeling used for single-trial EEG classification. Biomed Technik 42(6):162–167CrossRef
Sun HW, Fu YF, Xiong X, Yang J, Liu CW, Yu ZT (2015) Identification of EEG induced by motor imagery based on hilbert-huang transform. Acta Autom Sin 41:1686–1692
Tanaka K, Matsunaga K, Wang HO (2005) Electroencephalogram-based control of an electric wheelchair. IEEE Trans Robot 4:762–766CrossRef
van den Heuvel MP, Stam CJ, Kahn RS, Pol HEH (2009) Efficiency of functional brain networks and intellectual performance. J Neurosci 29(23):7619–7624PubMedPubMedCentralCrossRef
Vinck M, Oostenveld R, van Wingerden M, Battaglia F, Pennartz CM (2011) An improved index of phasesynchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias. Neuroimage 55:1548–1565PubMedCrossRef
Wang L, Xu G, Yang S, Guo M, Yan W, Wang J (2012) Motor imagery BCI research based on sample entropy and SVM. ICEF: 6th International Conference Electromagnetic Field Problems and Applications: 1–4
Wang CF, Sun CC, Zhang X, Wang YJ, Qi HZ, He F, Zhao X, Zhang Y, Wan BK, Du JG, Ming D (2015) The brain network research of post stroke depression based on partial directed coherence (PDC). Chin J Biomed Eng 34:385–391
Xu BG, Peng S, Song AG (2011) Upper-limb rehabilitation robot based on motor imagery EEG. Robot 33:307–313CrossRef
Xu P, Xiong XC, Xue Q, Tian Y, Peng Y (2014) Recognizing mild cognitive impairment based on network connectivity analysis of resting EEG with zero reference. Physiol Meas 35(7):1279–1298PubMedCrossRef
Yang H, Hu S (2013) EEG classification for BCI based on CSP and SVM-GA. Appl Mech Meter 459:228–231CrossRef
Yin E, Zhou ZT, Jiang J, Yu Y, Hu D (2015a) A dynamically optimized SSVEP brain-computer interface (BCI) speller. IEEE Trans Biomed Eng 62:1447PubMedCrossRef
Yin XX, Xu BL, Jiang CH, Fu YF (2015b) A hybrid BCI based on EEG and fNIRS signals improves the performance of decoding motor imagery of both force and speed of hand clenching. J Neural Eng 12:036004PubMedCrossRef
Yom-Tov E, Inbar GF (2002) Feature selection for the classification of movements from single movement-related potentials. IEEE Trans Neural Syst Rehabil Eng 10:170–177PubMedCrossRef
Yuan H, He B (2014) Brain-computer interfaces using sensorimotor rhythms: current state and future perspectives. IEEE Trans Biomed Eng 5:1425–1435CrossRef
Yuan H, Perdoni C, He B (2010) Relationship between speed and EEG activity during imagined and executed hand movements. J Neural Eng 7:2CrossRef
Zhang R, Yao D, Valdés-Sosa P et al (2015a) Efficient resting-state EEG network facilitates motor imagery performance. J Neural Eng 12:066024PubMedCrossRef
Zhang R, Yao DZ, Valdés-Sosa P (2015b) Efficient resting-state EEG network facilitates motor imagery performance. J Neural Eng 12(6):066024PubMedCrossRef
Zhang YS, Xu P, Guo DQ, Yao DZ (2013) Prediction of SSVEP-based BCI performance by the resting-state EEG network. J Neural Eng 10(6):066017PubMedCrossRef
Zhang Y, Zhou G, Jin J, Zhao Q, Wang X (2016) Sparse bayesian classification of EEG for brain- computer interface. IEEE Trans Neural Netw Learn Syst 27:2256PubMedCrossRef
Zhou Y, Luo ZZ (2013) Recognition of motor imagery based on normalized AR power spectrum. J Huazhong Univ Sci Technol 41:85–88
Zhou ZX, Wan BK (2012) Wavelet packet-based independent component analysis for feature extraction from motor imagery EEG of complex movements. Clin Neurophysiol 123(9):1779–1788PubMedCrossRef
Zhou G, Liu P, He (2012) Interindividual reaction time variability is related to resting-state network topology: an electroencephalogram study. Neuroscience 202(2):276–282PubMedCrossRef
Metadata
Title
Single-Trial Recognition of Imagined Forces and Speeds of Hand Clenching Based on Brain Topography and Brain Network
Authors
Xin Xiong
Yunfa Fu
Jian Chen
Lijun Liu
Xiabing Zhang
Publication date
01-03-2019
Publisher
Springer US
Published in
Brain Topography / Issue 2/2019
Print ISSN: 0896-0267
Electronic ISSN: 1573-6792
DOI
https://doi.org/10.1007/s10548-018-00696-3

Other articles of this Issue 2/2019

Brain Topography 2/2019 Go to the issue

Original Paper

A Finite-Difference Solution for the EEG Forward Problem in Inhomogeneous Anisotropic Media

Original Paper

Aberrant Intrinsic Brain Activity in Patients with Autism Spectrum Disorder: Insights from EEG Microstates

Original Paper

Abnormal Functional Connectivity Density in Post-Stroke Aphasia

Original Paper

Repetitive Transcranial Magnetic Stimulation Over the Left Posterior Middle Temporal Gyrus Reduces Wrist Velocity During Emblematic Hand Gesture Imitation

Original Paper

Brain Network Reconfiguration During Motor Imagery Revealed by a Large-Scale Network Analysis of Scalp EEG

Review

Multi-Scale Neural Sources of EEG: Genuine, Equivalent, and Representative. A Tutorial Review