Mindfulness Improves Brain Computer Interface Performance by Increasing Control over Neural Activity in the Alpha Band

Brain-computer interfaces (BCIs) are promising tools for assisting patients with paralysis, but suffer from long training times and variable user proficiency. Mind-body awareness training (MBAT) can improve BCI learning, but how it does so remains unknown. Here we show that MBAT allows participants to learn to volitionally increase alpha band neural activity during BCI tasks that incorporate intentional rest. We trained individuals in mindfulness-based stress reduction (MBSR; a standardized MBAT intervention) and compared performance and brain activity before and after training between randomly assigned trained and untrained control groups. The MBAT group showed reliably faster learning of BCI than the control group throughout training. Alpha-band activity in EEG signals, recorded in the volitional resting state during task performance, showed a parallel increase over sessions, and predicted final BCI performance. The level of alpha-band activity during the intentional resting state correlated reliably with individuals’ mindfulness practice as well as performance on a sustained attention task. Collectively, these results show that MBAT modifies a specific neural signal used by BCI. MBAT, by increasing patients’ control over their brain activity during rest, may increase the effectiveness of BCI in the large population who could benefit from alternatives to direct motor control.

[1]  D. Mantini,et al.  Hemodynamic Correlates of Electrophysiological Activity in the Default Mode Network , 2019, Front. Neurosci..

[2]  Kareem A. Zaghloul,et al.  Large-Scale Communication in the Human Brain Is Rhythmically Modulated through Alpha Coherence , 2019, Current Biology.

[3]  Simon Hanslmayr,et al.  Modulating Human Memory via Entrainment of Brain Oscillations , 2019, Trends in Neurosciences.

[4]  Christopher C. Cline,et al.  Noninvasive neuroimaging enhances continuous neural tracking for robotic device control , 2019, Science Robotics.

[5]  S. Tong,et al.  Brain-Heart Interactions Underlying Traditional Tibetan Buddhist Meditation. , 2019, Cerebral cortex.

[6]  Camarin E. Rolle,et al.  Closed-loop Digital Meditation Improves Sustained Attention in Young Adults , 2019, Nature Human Behaviour.

[7]  Larry Griffin,et al.  Stochastic simulations reveal few green wave surfing populations among spring migrating herbivorous waterfowl , 2019, Nature Communications.

[8]  P. Sachs,et al.  SMARCAD1 ATPase activity is required to silence endogenous retroviruses in embryonic stem cells , 2019, Nature Communications.

[9]  P. Sajda,et al.  Regulation of arousal via online neurofeedback improves human performance in a demanding sensory-motor task , 2018, Proceedings of the National Academy of Sciences.

[10]  Hubert R. Dinse,et al.  Somatosensory alpha oscillations gate perceptual learning efficiency , 2019, Nature Communications.

[11]  R. VanRullen,et al.  The Hidden Spatial Dimension of Alpha: 10-Hz Perceptual Echoes Propagate as Periodic Traveling Waves in the Human Brain , 2019, Cell reports.

[12]  Bertrand Carré,et al.  Evidence of depolarization and ellipticity of high harmonics driven by ultrashort bichromatic circularly polarized fields , 2018, Nature Communications.

[13]  Earl K. Miller,et al.  Working Memory 2.0 , 2018, Neuron.

[14]  Ronald J. Bonnstetter Book Review - Altered Traits: Science Reveals how meditation changes your mind, brain and body , 2018, NeuroRegulation.

[15]  P. Sajda,et al.  Regulation of arousal via on-line neurofeedback improves human performance in a demanding sensory-motor task , 2018, bioRxiv.

[16]  Jonathan R Wolpaw,et al.  Brain–computer interface use is a skill that user and system acquire together , 2018, PLoS biology.

[17]  F. Balcı,et al.  Differential Bilateral Primary Motor Cortex tDCS Fails to Modulate Choice Bias and Readiness in Perceptual Decision Making , 2018, Front. Neurosci..

[18]  Leonardo L. Gollo,et al.  Metastable brain waves , 2018, Nature Communications.

[19]  Marinella Cappelletti,et al.  Alpha Oscillations Are Causally Linked to Inhibitory Abilities in Ageing , 2018, The Journal of Neuroscience.

[20]  J. Millán,et al.  The Cybathlon BCI race: Successful longitudinal mutual learning with two tetraplegic users , 2018, PLoS biology.

[21]  Fady Girgis,et al.  Review of the Neural Oscillations Underlying Meditation , 2018, Front. Neurosci..

[22]  Julian Lim,et al.  Dynamic functional connectivity markers of objective trait mindfulness , 2018, NeuroImage.

[23]  D. Hasselquist,et al.  No evidence that carotenoid pigments boost either immune or antioxidant defenses in a songbird , 2018, Nature Communications.

[24]  M. Chee,et al.  Towards an Objective Measure of Mindfulness: Replicating and Extending the Features of the Breath-Counting Task , 2018, Mindfulness.

[25]  Andrew J. Watrous,et al.  Theta and Alpha Oscillations Are Traveling Waves in the Human Neocortex , 2017, Neuron.

[26]  C. Coletti,et al.  Peripheral Neuron Survival and Outgrowth on Graphene , 2017, Front. Neurosci..

[27]  J. Bodurka,et al.  Real‐time fMRI neurofeedback of the mediodorsal and anterior thalamus enhances correlation between thalamic BOLD activity and alpha EEG rhythm , 2017, Human brain mapping.

[28]  O. Shriki,et al.  Can We Predict Who Will Respond to Neurofeedback? A Review of the Inefficacy Problem and Existing Predictors for Successful EEG Neurofeedback Learning , 2017, Neuroscience.

[29]  Per B. Brockhoff,et al.  lmerTest Package: Tests in Linear Mixed Effects Models , 2017 .

[30]  Stephen D. Mayhew,et al.  Dynamic spatiotemporal variability of alpha-BOLD relationships during the resting-state and task-evoked responses , 2017, NeuroImage.

[31]  Diego Vidaurre,et al.  Spontaneous cortical activity transiently organises into frequency specific phase-coupling networks , 2017, bioRxiv.

[32]  Judson A. Brewer,et al.  Source-space EEG neurofeedback links subjective experience with brain activity during effortless awareness meditation , 2017, NeuroImage.

[33]  Wolfram Burgard,et al.  Deep learning with convolutional neural networks for EEG decoding and visualization , 2017, Human brain mapping.

[34]  J. Creswell,et al.  Brief Mindfulness Meditation Training Reduces Mind Wandering: The Critical Role of Acceptance , 2017, Emotion.

[35]  Jarrod A. Lewis-Peacock,et al.  Closed-loop brain training: the science of neurofeedback , 2017, Nature Reviews Neuroscience.

[36]  A. Delorme,et al.  Reduced mind wandering in experienced meditators and associated EEG correlates , 2016, Experimental Brain Research.

[37]  N. Ramsey,et al.  Fully Implanted Brain-Computer Interface in a Locked-In Patient with ALS. , 2016, The New England journal of medicine.

[38]  A. Kleinschmidt,et al.  Brain Networks and α-Oscillations: Structural and Functional Foundations of Cognitive Control , 2016, Trends in Cognitive Sciences.

[39]  Bin He,et al.  Sensorimotor Rhythm BCI with Simultaneous High Definition-Transcranial Direct Current Stimulation Alters Task Performance , 2016, Brain Stimulation.

[40]  N. Birbaumer,et al.  Brain–computer interfaces for communication and rehabilitation , 2016, Nature Reviews Neurology.

[41]  Rebecca M. Todd,et al.  Dynamics of neural recruitment surrounding the spontaneous arising of thoughts in experienced mindfulness practitioners , 2016, NeuroImage.

[42]  Alireza Gharabaghi,et al.  Brain State-Dependent Transcranial Magnetic Closed-Loop Stimulation Controlled by Sensorimotor Desynchronization Induces Robust Increase of Corticospinal Excitability , 2016, Brain Stimulation.

[43]  Rafael Malach,et al.  Covert neurofeedback without awareness shapes cortical network spontaneous connectivity , 2016, Proceedings of the National Academy of Sciences.

[44]  Matthew L. Dixon,et al.  Functional neuroanatomy of meditation: A review and meta-analysis of 78 functional neuroimaging investigations , 2016, Neuroscience & Biobehavioral Reviews.

[45]  Bin He,et al.  EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks , 2016, IEEE Transactions on Biomedical Engineering.

[46]  Niels Birbaumer,et al.  Enhancing Hebbian Learning to Control Brain Oscillatory Activity. , 2015, Cerebral cortex.

[47]  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.

[48]  B. Hommel,et al.  Meditation-induced states predict attentional control over time , 2015, Consciousness and Cognition.

[49]  Britta K. Hölzel,et al.  The neuroscience of mindfulness meditation , 2015, Nature Reviews Neuroscience.

[50]  R. Nathan Spreng,et al.  The wandering brain: Meta-analysis of functional neuroimaging studies of mind-wandering and related spontaneous thought processes , 2015, NeuroImage.

[51]  N. Yeung,et al.  The roles of cortical oscillations in sustained attention , 2015, Trends in Cognitive Sciences.

[52]  Britta K. Hölzel,et al.  The neuroscience of mindfulness meditation , 2015, Nature Reviews Neuroscience.

[53]  Bin He,et al.  The impact of mind-body awareness training on the early learning of a brain-computer interface. , 2014, Technology.

[54]  R. Davidson,et al.  A mind you can count on: validating breath counting as a behavioral measure of mindfulness , 2014, Front. Psychol..

[55]  Bart Gips,et al.  Temporal coding organized by coupled alpha and gamma oscillations prioritize visual processing , 2014, Trends in Neurosciences.

[56]  J. Buitelaar,et al.  Effects of mindfulness-based cognitive therapy on neurophysiological correlates of performance monitoring in adult attention-deficit/hyperactivity disorder , 2014, Clinical Neurophysiology.

[57]  D. Bates,et al.  Fitting Linear Mixed-Effects Models Using lme4 , 2014, 1406.5823.

[58]  Matthew L. Dixon,et al.  Is meditation associated with altered brain structure? A systematic review and meta-analysis of morphometric neuroimaging in meditation practitioners , 2014, Neuroscience & Biobehavioral Reviews.

[59]  P. Uhlhaas,et al.  Working memory and neural oscillations: alpha–gamma versus theta–gamma codes for distinct WM information? , 2014, Trends in Cognitive Sciences.

[60]  Sing-Yau Goh,et al.  Effect of mindfulness meditation on brain–computer interface performance , 2014, Consciousness and Cognition.

[61]  J. Wolpaw Brain-computer interfaces. , 2013, Handbook of clinical neurology.

[62]  C. Neuper,et al.  Neural substrates of cognitive control under the belief of getting neurofeedback training , 2013, Front. Hum. Neurosci..

[63]  Dustin Scheinost,et al.  Real-time fMRI links subjective experience with brain activity during focused attention , 2013, NeuroImage.

[64]  Christa Neuper,et al.  Control beliefs can predict the ability to up-regulate sensorimotor rhythm during neurofeedback training , 2013, Front. Hum. Neurosci..

[65]  Rajesh P. N. Rao,et al.  Distributed cortical adaptation during learning of a brain–computer interface task , 2013, Proceedings of the National Academy of Sciences.

[66]  K. Lafleur,et al.  Quadcopter control in three-dimensional space using a noninvasive motor imagery-based brain–computer interface , 2013, Journal of neural engineering.

[67]  Mingzhou Ding,et al.  Coupling between visual alpha oscillations and default mode activity , 2013, NeuroImage.

[68]  N. Japaridze,et al.  Dynamic imaging of coherent sources , 2013, Zeitschrift für Epileptologie.

[69]  Michael Petrides,et al.  Modulation of feedback related activity in the rostral anterior cingulate cortex during trial and error exploration , 2012, NeuroImage.

[70]  O. Jensen,et al.  Alpha Oscillations Serve to Protect Working Memory Maintenance against Anticipated Distracters , 2012, Current Biology.

[71]  Brandon G. King,et al.  Intensive training induces longitudinal changes in meditation state-related EEG oscillatory activity , 2012, Front. Hum. Neurosci..

[72]  J. Smallwood,et al.  Mindfulness and mind-wandering: finding convergence through opposing constructs. , 2012, Emotion.

[73]  Lawrence W. Barsalou,et al.  Mind wandering and attention during focused meditation: A fine-grained temporal analysis of fluctuating cognitive states , 2012, NeuroImage.

[74]  Joseph W. McKean,et al.  Rfit: Rank-based Estimation for Linear Models , 2012, R J..

[75]  J. Gray,et al.  Meditation experience is associated with differences in default mode network activity and connectivity , 2011, Proceedings of the National Academy of Sciences.

[76]  Huosheng Hu,et al.  A Self-Paced Motor Imagery Based Brain-Computer Interface for Robotic Wheelchair Control , 2011, Clinical EEG and neuroscience.

[77]  Ole Jensen,et al.  Alpha Oscillations Correlate with the Successful Inhibition of Unattended Stimuli , 2011, Journal of Cognitive Neuroscience.

[78]  Marisa O. Hollinshead,et al.  The organization of the human cerebral cortex estimated by intrinsic functional connectivity. , 2011, Journal of neurophysiology.

[79]  Diane M. Beck,et al.  Pulsed Out of Awareness: EEG Alpha Oscillations Represent a Pulsed-Inhibition of Ongoing Cortical Processing , 2011, Front. Psychology.

[80]  Claire Braboszcz,et al.  Lost in thoughts: Neural markers of low alertness during mind wandering , 2011, NeuroImage.

[81]  Cuntai Guan,et al.  Regularizing Common Spatial Patterns to Improve BCI Designs: Unified Theory and New Algorithms , 2011, IEEE Transactions on Biomedical Engineering.

[82]  Benedikt Zoefel,et al.  Neurofeedback training of the upper alpha frequency band in EEG improves cognitive performance , 2011, NeuroImage.

[83]  Robert Oostenveld,et al.  FieldTrip: Open Source Software for Advanced Analysis of MEG, EEG, and Invasive Electrophysiological Data , 2010, Comput. Intell. Neurosci..

[84]  A. Kleinschmidt,et al.  Intrinsic Connectivity Networks, Alpha Oscillations, and Tonic Alertness: A Simultaneous Electroencephalography/Functional Magnetic Resonance Imaging Study , 2010, The Journal of Neuroscience.

[85]  Klaus-Robert Müller,et al.  Neurophysiological predictor of SMR-based BCI performance , 2010, NeuroImage.

[86]  O. Jensen,et al.  Shaping Functional Architecture by Oscillatory Alpha Activity: Gating by Inhibition , 2010, Front. Hum. Neurosci..

[87]  Brandon G. King,et al.  Intensive Meditation Training Improves Perceptual Discrimination and Sustained Attention , 2010, Psychological science.

[88]  Brice Rebsamen,et al.  A brain controlled wheelchair to navigate in familiar environments. , 2010, IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[89]  A. Lutz,et al.  Mental Training Enhances Attentional Stability: Neural and Behavioral Evidence , 2009, The Journal of Neuroscience.

[90]  Richard J. Davidson,et al.  Theta Phase Synchrony and Conscious Target Perception: Impact of Intensive Mental Training , 2009, Journal of Cognitive Neuroscience.

[91]  K. Christoff,et al.  Experience sampling during fMRI reveals default network and executive system contributions to mind wandering , 2009, Proceedings of the National Academy of Sciences.

[92]  A. Craig,et al.  How do you feel — now? The anterior insula and human awareness , 2009, Nature Reviews Neuroscience.

[93]  Cuntai Guan,et al.  Filter Bank Common Spatial Pattern (FBCSP) in Brain-Computer Interface , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[94]  Kenneth Hugdahl,et al.  Prediction of human errors by maladaptive changes in event-related brain networks , 2008, Proceedings of the National Academy of Sciences.

[95]  K.-R. Muller,et al.  Optimizing Spatial filters for Robust EEG Single-Trial Analysis , 2008, IEEE Signal Processing Magazine.

[96]  M. Posner,et al.  Short-term meditation training improves attention and self-regulation , 2007, Proceedings of the National Academy of Sciences.

[97]  M. Corbetta,et al.  Electrophysiological signatures of resting state networks in the human brain , 2007, Proceedings of the National Academy of Sciences.

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

[99]  W. Klimesch,et al.  EEG alpha oscillations: The inhibition–timing hypothesis , 2007, Brain Research Reviews.

[100]  J. Polich,et al.  Meditation states and traits: EEG, ERP, and neuroimaging studies. , 2006, Psychological bulletin.

[101]  D. Ballard,et al.  Eye movements in natural behavior , 2005, Trends in Cognitive Sciences.

[102]  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.

[103]  A. Lutz,et al.  Long-term meditators self-induce high-amplitude gamma synchrony during mental practice. , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[104]  M. Murray,et al.  EEG source imaging , 2004, Clinical Neurophysiology.

[105]  N. Birbaumer,et al.  BCI2000: a general-purpose brain-computer interface (BCI) system , 2004, IEEE Transactions on Biomedical Engineering.

[106]  Arnaud Delorme,et al.  EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis , 2004, Journal of Neuroscience Methods.

[107]  Chris Eliasmith,et al.  Neural Engineering , 2020 .

[108]  Gert Pfurtscheller,et al.  Motor imagery and direct brain-computer communication , 2001, Proc. IEEE.

[109]  A. Schnitzler,et al.  Dynamic imaging of coherent sources: Studying neural interactions in the human brain. , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[110]  G L Shulman,et al.  INAUGURAL ARTICLE by a Recently Elected Academy Member:A default mode of brain function , 2001 .

[111]  G Pfurtscheller,et al.  Real-time EEG analysis with subject-specific spatial patterns for a brain-computer interface (BCI). , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

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

[113]  J. Kabat-Zinn,et al.  An outpatient program in behavioral medicine for chronic pain patients based on the practice of mindfulness meditation: theoretical considerations and preliminary results. , 1982, General hospital psychiatry.