Sensory Feedback Interferes with Mu Rhythm Based Detection of Motor Commands from Electroencephalographic Signals

Background: Electroencephalogram (EEG)-based brain-computer interfaces (BCI) represent a promising component of restorative motor therapies in individuals with partial paralysis. However, in those patients, sensory functions such as proprioception are at least partly preserved. The aim of this study was to investigate whether afferent feedback interferes with the BCI-based detection of efferent motor commands during execution of movements. Methods: Brain activity of 13 able-bodied subjects (age: 29.1 ± 4.8 years; 11 males) was compared between a motor task (MT) consisting of an isometric, isotonic grip and a somatosensory electrical stimulation (SS) of the fingertips. Modulation of the mu rhythm (8–13 Hz) was investigated to identify changes specifically related to the generation of efferent commands. A linear discriminant analysis (LDA) was used to investigate the activation pattern on a single-trial basis. Classifiers were trained with MT vs. REST (periods without MT/SS) and tested with SS and vice versa to quantify the impact of afferent feedback on the classification results. Results: Few differences in the spatial pattern between MT and SS were found in the modulation of the mu rhythm. All were characterized by event-related desynchronization (ERD) peaks at electrodes C3, C4, and CP3. Execution of the MT was associated with a significantly stronger ERD in the majority of sensorimotor electrodes [C3 (p < 0.01); CP3 (p < 0.05); C4 (p < 0.01)]. Classification accuracy of MT vs. REST was significantly higher than SS vs. REST (77% and 63%; p < 10-8). Classifiers trained on MT vs. REST were able to classify SS trials significantly above chance even though no motor commands were present during SS. Classifiers trained on SS performed better in classifying MT instead of SS. Conclusion: Our results challenge the notion that the modulation of the mu rhythm is a robust phenomenon for detecting efferent commands when afferent feedback is present. Instead, they indicate that the mu ERD caused by the processing of afferent feedback generates ERD patterns in the sensorimotor cortex that are masking the ERD patterns caused by the generation of efferent commands. Thus, processing of afferent feedback represents a considerable source of false positives when the mu rhythm is used for the detection of efferent commands.

[1]  S. Gandevia,et al.  The proprioceptive senses: their roles in signaling body shape, body position and movement, and muscle force. , 2012, Physiological reviews.

[2]  G. Pfurtscheller,et al.  ‘Thought’ – control of functional electrical stimulation to restore hand grasp in a patient with tetraplegia , 2003, Neuroscience Letters.

[3]  J. Gordon,et al.  Impairments of reaching movements in patients without proprioception. I. Spatial errors. , 1995, Journal of neurophysiology.

[4]  J. Peters,et al.  Closing the sensorimotor loop: haptic feedback facilitates decoding of motor imagery , 2011, Journal of neural engineering.

[5]  Klaus Linkenkaer-Hansen,et al.  Dynamics of mu-rhythm suppression caused by median nerve stimulation: a magnetoencephalographic study in human subjects , 2000, Neuroscience Letters.

[6]  C Grozea,et al.  On the feasibility of using motor imagery EEG-based brain–computer interface in chronic tetraplegics for assistive robotic arm control: a clinical test and long-term post-trial follow-up , 2012, Spinal Cord.

[7]  Cuntai Guan,et al.  Clinical study of neurorehabilitation in stroke using EEG-based motor imagery brain-computer interface with robotic feedback , 2010, 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology.

[8]  J. Palva,et al.  New vistas for α-frequency band oscillations , 2007, Trends in Neurosciences.

[9]  Karim Jerbi,et al.  Exceeding chance level by chance: The caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracy , 2015, Journal of Neuroscience Methods.

[10]  Christian Grefkes,et al.  Cortical reorganization after stroke: how much and how functional? , 2015 .

[11]  M. Nuttin,et al.  A brain-actuated wheelchair: Asynchronous and non-invasive Brain–computer interfaces for continuous control of robots , 2008, Clinical Neurophysiology.

[12]  G. Pfurtscheller Functional Topography During Sensorimotor Activation Studied with Event‐Related Desynchronization Mapping , 1989, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[13]  Rajesh P. N. Rao,et al.  Correction for Miller et al., Cortical activity during motor execution, motor imagery, and imagery-based online feedback , 2010, Proceedings of the National Academy of Sciences.

[14]  Bin He,et al.  Brain–Computer Interfaces Using Sensorimotor Rhythms: Current State and Future Perspectives , 2014, IEEE Transactions on Biomedical Engineering.

[15]  Jeffery J. Summers,et al.  Neural plasticity and bilateral movements: A rehabilitation approach for chronic stroke , 2005, Progress in Neurobiology.

[16]  J. Wolpaw,et al.  Brain–computer interfaces in neurological rehabilitation , 2008, The Lancet Neurology.

[17]  Michael L Boninger,et al.  Functional priorities, assistive technology, and brain-computer interfaces after spinal cord injury. , 2013, Journal of rehabilitation research and development.

[18]  Jack L. Gallant,et al.  Encoding and decoding in fMRI , 2011, NeuroImage.

[19]  R. Fisher THE USE OF MULTIPLE MEASUREMENTS IN TAXONOMIC PROBLEMS , 1936 .

[20]  R. Nudo,et al.  Neural Substrates for the Effects of Rehabilitative Training on Motor Recovery After Ischemic Infarct , 1996, Science.

[21]  Jonathan R Wolpaw,et al.  Control of a two-dimensional movement signal by a noninvasive brain-computer interface in humans. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[22]  R G Radwin,et al.  A conductive polymer sensor for measuring external finger forces. , 1991, Journal of biomechanics.

[23]  F. Varela,et al.  Neuromagnetic imaging of cortical oscillations accompanying tactile stimulation. , 2003, Brain research. Cognitive brain research.

[24]  Stephan Waldert,et al.  Invasive vs. Non-Invasive Neuronal Signals for Brain-Machine Interfaces: Will One Prevail? , 2016, Front. Neurosci..

[25]  C. Nombela,et al.  IS MU A NORMAL RHYTHM , 2013 .

[26]  A. Villringer,et al.  Rolandic alpha and beta EEG rhythms' strengths are inversely related to fMRI‐BOLD signal in primary somatosensory and motor cortex , 2009, Human brain mapping.

[27]  Mark Hallett,et al.  Plasticity of the human motor cortex and recovery from stroke , 2001, Brain Research Reviews.

[28]  S. Coyle,et al.  Brain–computer interfaces: a review , 2003 .

[29]  D J McFarland,et al.  An EEG-based brain-computer interface for cursor control. , 1991, Electroencephalography and clinical neurophysiology.

[30]  J. Wolpaw,et al.  Mu and Beta Rhythm Topographies During Motor Imagery and Actual Movements , 2004, Brain Topography.

[31]  W. Klimesch Alpha-band oscillations, attention, and controlled access to stored information , 2012, Trends in Cognitive Sciences.

[32]  R. Peterka Sensorimotor integration in human postural control. , 2002, Journal of neurophysiology.

[33]  Stuart N Baker,et al.  Degraded EEG decoding of wrist movements in absence of kinaesthetic feedback , 2014, Human brain mapping.

[34]  L. Cohen,et al.  Brain–machine interfaces in neurorehabilitation of stroke , 2015, Neurobiology of Disease.

[35]  A. Riehle,et al.  The ups and downs of beta oscillations in sensorimotor cortex , 2013, Experimental Neurology.

[36]  N. Birbaumer,et al.  Brain-computer communication: self-regulation of slow cortical potentials for verbal communication. , 2001, Archives of physical medicine and rehabilitation.

[37]  G. Prasad,et al.  Applying a brain-computer interface to support motor imagery practice in people with stroke for upper limb recovery: a feasibility study , 2010, Journal of NeuroEngineering and Rehabilitation.

[38]  R. Hari,et al.  Spatiotemporal characteristics of sensorimotor neuromagnetic rhythms related to thumb movement , 1994, Neuroscience.

[39]  R P Lesser,et al.  Functional significance of the mu rhythm of human cortex: an electrophysiologic study with subdural electrodes. , 1993, Electroencephalography and clinical neurophysiology.

[40]  Alpha phase modulates the effectiveness and directionality of cortical communication , 2015, BMC Neuroscience.

[41]  A. Hough Pride and a Daily Marathon , 1992 .

[42]  J. Gordon,et al.  Impairments of reaching movements in patients without proprioception. II. Effects of visual information on accuracy. , 1995, Journal of neurophysiology.

[43]  G. Pfurtscheller,et al.  Event-related synchronization (ERS) in the alpha band--an electrophysiological correlate of cortical idling: a review. , 1996, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[44]  Scott M Lephart,et al.  The Sensorimotor System, Part II: The Role of Proprioception in Motor Control and Functional Joint Stability. , 2002, Journal of athletic training.

[45]  J R Wolpaw,et al.  Spatial filter selection for EEG-based communication. , 1997, Electroencephalography and clinical neurophysiology.

[46]  M Congedo,et al.  A review of classification algorithms for EEG-based brain–computer interfaces , 2007, Journal of neural engineering.

[47]  M. Diamond,et al.  Primary Motor and Sensory Cortex Activation during Motor Performance and Motor Imagery: A Functional Magnetic Resonance Imaging Study , 1996, The Journal of Neuroscience.

[48]  Vera Kaiser,et al.  Hybrid brain-computer interfaces and hybrid neuroprostheses for restoration of upper limb functions in individuals with high-level spinal cord injury , 2013, Artif. Intell. Medicine.

[49]  Clemens Brunner,et al.  Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks , 2006, NeuroImage.

[50]  G. Pfurtscheller,et al.  Event-related synchronization of mu rhythm in the EEG over the cortical hand area in man , 1994, Neuroscience Letters.

[51]  V. Jousmäki,et al.  Modulation of Human Cortical Rolandic Rhythms during Natural Sensorimotor Tasks , 1997, NeuroImage.

[52]  J. W. Kuhlman,et al.  Functional topography of the human mu rhythm. , 1978, Electroencephalography and clinical neurophysiology.

[53]  G. Pfurtscheller Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest. , 1992, Electroencephalography and clinical neurophysiology.

[54]  S. Scott Optimal feedback control and the neural basis of volitional motor control , 2004, Nature Reviews Neuroscience.

[55]  Bin He,et al.  Negative covariation between task-related responses in alpha/beta-band activity and BOLD in human sensorimotor cortex: An EEG and fMRI study of motor imagery and movements , 2010, NeuroImage.

[56]  Lucas C. Parra,et al.  Recipes for the linear analysis of EEG , 2005, NeuroImage.

[57]  Riitta Salmelin,et al.  Tactile information from the human hand reaches the ipsilateral primary somatosensory cortex , 1995, Neuroscience Letters.

[58]  Thomas Brochier,et al.  Modulations of EEG Beta Power during Planning and Execution of Grasping Movements , 2013, PloS one.

[59]  F. L. D. Silva,et al.  Event-related EEG/MEG synchronization and desynchronization: basic principles , 1999, Clinical Neurophysiology.

[60]  Febo Cincotti,et al.  Human Movement-Related Potentials vs Desynchronization of EEG Alpha Rhythm: A High-Resolution EEG Study , 1999, NeuroImage.

[61]  J. Krakauer Motor learning: its relevance to stroke recovery and neurorehabilitation. , 2006, Current opinion in neurology.

[62]  Rajesh P. N. Rao,et al.  Cortical activity during motor execution, motor imagery, and imagery-based online feedback , 2010, Proceedings of the National Academy of Sciences.