Functional Dissociation of Ongoing Oscillatory Brain States Revealed by a Custom-Developed Brain Computer Interface

Der Zustand eines Nervenzell-Verbandes, welcher einem eintreffenden Reiz vorangeht, kann die Verarbeitung nachfolgend dargebotener Reize beeinflussen. Der Zustand solch eines Zellverbandes kann bezuglich der Frequenz der zugrundeliegenden oszillatorischen neuronalen Aktivitat voneinander abweichen. Die Hypothese besteht, dass die oszillatorische Aktivitat bestimmter Frequenzbereiche, wie z.B. Alpha- (8-12 Hz) und Gamma- (30-45 Hz) Band Oszillationen, eine funktionale Rolle bei der Verarbeitung visueller Objekte besitzen. Allerdings ist die genaue Rolle der pra-Stimulus Alpha- oder Gamma- Band Oszillationen bei der visuellen Objektverarbeitung nicht vollstandig geklart. Aus diesem Grund konnte eine selektive Modulation dieser pra-Stimulus Aktivitat dazu beitragen, die funktionale Rolle dieser Oszillationen zu klaren. Dabei nahmen wir an, dass eine Erhohung der Gamma-Band Aktivitat im visuellen Kortex mit Hilfe der Brain-Computer-Interface (BCI)-Methode zu einer anschliesenden Verbesserung der visuellen Objektverarbeitung fuhrt. In Abgrenzung zu fruheren Studien, in denen die Korrelation zwischen der pra-Stimulus Oszillationen und der visuellen Leistung bestimmt wurde, versuchten wir, den Versuchsteilnehmern die direkte Kontrolle ihrer oszillatorischen Hirnaktivitat zu ermoglichen. Zu diesem Zweck entwickelten wir eine nicht-invasive BCI-Methode. Durch diese lernt der Versuchsteilnehmer, die eigene Alpha- oder Gamma- Band Aktivitat im visuellen Kortex selektiv zu erhohen. Wahrend des Trainings wurde die oszillatorische Hirnaktivitat geschatzt und fur den Versuchsteilnehmer auf dem Bildschirm visualisiert, um eine bewusste Modulation der Alpha- oder Gamma- Band Oszillationen zu ermoglichen. Danach wurden visuelle Reize wahrend bestimmter Zustande des Gehirns prasentiert. Wahrend der Testphase, die im Anschluss an die Trainingsphase erfolgte, wurde die Alpha- oder Gamma- Band Aktivitat „online“ klassifiziert. Visuelle Reize wurden wahrend vordefinierten Stufen der Aktivitat prasentiert, um den Einfluss dieser Frequenzmodulation auf die nachfolgende visuelle Objektverarbeitung zu untersuchen. Wahrend der Entwicklung einer BCI-Methode auf der Basis von Gamma-Band Oszillationen, mussten einige wichtige Aspekte berucksichtigt werden. Dazu gehoren das Auftreten von Artefakten, das experimentelle Design und die topographische Prazision des BCI-Trainings. Aus diesem Grund wurde die BCI-Methode mit einer „online“ Artefakt-Kontrolle zur Artefakt-Unterdruckung ausgestattet. Weiterhin wurde ein spezielles Display-Design entworfen, um den Versuchsteilnehmer nicht abzulenken und um ihn zugleich zu motivieren. Um das Training auf eine spezielle neuronale Region zu beschranken, wurde eine quellen-basierte BCI-Methode eingefuhrt. In einer Reihe von Experimenten analysierten wir zunachst die Genauigkeit der BCI-Methode und untersuchten daraufhin die spezifische Wirkung des Gamma-Band-Trainings auf die visuelle Objektverarbeitung. Schlieslich verglichen wir die spezifische Wirkung des Gamma-Band-Trainings auf die visuelle Objektverarbeitung mit dem eindeutig definierteren Alpha-Band. Im Hinblick auf den Frequenzbereich und die Lokalisation lernten die Versuchsteilnehmer mit einem hohen Grad an Genauigkeit eine selektive Modulation der Alpha- und Gamma- Band Oszillationen im visuellen Kortex. In Phasen erhohter Gamma-Band Aktivitat wurde die visuelle Objektverarbeitung verbessert. Die funktionale Spezifitat der Gamma-Band Oszillationen wurde durch einen direkten Vergleich zu den Alpha-Band Oszillationen nachgewiesen Die BCI-Methode ermoglicht eine selektive Modulation der Gamma-Band Oszillationen im visuellen Kortex und belegt die funktionale Relevanz der Gamma-Band Aktivitat fur die visuelle Objektverarbeitung. The state of a neural assembly in the human brain preceding an incoming stimulus is assumed to modulate the processing of subsequently presented stimuli. The nature of this state can differ with respect to the frequency of ongoing oscillatory activity. Oscillatory activity of specific frequency range such as alpha (8-12 Hz) and gamma (30-45 Hz) band oscillations is hypothesized to play a functional role in visual object processing. However, the precise role of prestimulus alpha or gamma band oscillations for visual object processing is not completely understood. Therefore, a selective modulation of this prestimulus activity could clarify the functional role of these oscillations. We hypothesized that an increase in gamma band activity as compared to an increase in alpha band activity over the visual cortex by BCI manipulation would enhance subsequent visual object processing. In contrast to previous studies in which oscillations of prestimulus activity were correlated with visual performance, we attempted to put the volunteers directly in control of the oscillatory brain activity. To this end, we designed and implemented a non-invasive brain computer interface (BCI) method to train volunteers to selectively increase their alpha or gamma band activity in the occipital cortex. During training, oscillatory brain activity was estimated online and fed back to the volunteers to enable a deliberate modulation of alpha or gamma band oscillations. The visual stimuli were presented during specific brain states in an individually adapted manner. During the testing phase which followed the training phase, alpha or gamma band activity was classified online and at predefined levels of activity, visual objects embedded in noise were presented in order to assess the influence of frequency modulation on subsequent visual object processing. In the process of developing a BCI method based on gamma band oscillations, several important aspects had to be considered, including presence of artifacts, experimental design and topographical precision of BCI training. We therefore perfected our BCI method with online artifact control for artifact suppression, a special visual display design to avoid distraction yet motivate volunteers, and a source-based BCI method to limit training to a distinct neural area in the visual cortex. In a series of experiments, we first evaluated the accuracy of the BCI method and then explored the specific effect of gamma band training on visual object perception. Finally, we compared the specific effect of gamma band training to the well defined alpha band. Our results demonstrated that volunteers learned to selectively modulate alpha or gamma band oscillations in the visual cortex with a high level of specificity regarding frequency range and localization. During phases of increased gamma band activity, visual object processing was improved. The functional specificity of gamma band oscillations was demonstrated by a direct comparison to alpha band oscillations. Hence, the BCI method allows a selective manipulation of gamma band activity in the visual cortex and supports a prominent role of prestimulus gamma band activity for visual object processing.

[1]  Brendan Z. Allison,et al.  The Hybrid BCI , 2010, Frontiers in Neuroscience.

[2]  Christian Büchel,et al.  The functional and temporal characteristics of top-down modulation in visual selection. , 2005, Cerebral cortex.

[3]  M. Fuchs,et al.  Linear and nonlinear current density reconstructions. , 1999, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[4]  Keh-Shew Lu,et al.  DIGITAL FILTER DESIGN , 1973 .

[5]  C. Büchel,et al.  Functional Dissociation of Hippocampal Mechanism during Implicit Learning Based on the Domain of Associations , 2011, The Journal of Neuroscience.

[6]  Nicu Sebe,et al.  Multimodal Human Computer Interaction: A Survey , 2005, ICCV-HCI.

[7]  G. Borasio,et al.  Breaking the news in amyotrophic lateral sclerosis , 1998, Journal of the Neurological Sciences.

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

[9]  T. Pedley,et al.  Beta and Mu Rhythms , 1990, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[10]  Christoph Braun,et al.  Coherence of gamma-band EEG activity as a basis for associative learning , 1999, Nature.

[11]  Chalapathy Neti,et al.  Recent advances in the automatic recognition of audiovisual speech , 2003, Proc. IEEE.

[12]  J. Lisman,et al.  Oscillations in the alpha band (9-12 Hz) increase with memory load during retention in a short-term memory task. , 2002, Cerebral cortex.

[13]  Victor A. F. Lamme,et al.  Source (or Part of the following Source): Type Article Title Internal State of Monkey Primary Visual Cortex (v1) Predicts Figure Ground Perception Author(s) Internal State of Monkey Primary Visual Cortex (v1) Predicts Figure–ground Perception Materials and Methods , 2022 .

[14]  W. Singer,et al.  Synchronization of Neural Activity across Cortical Areas Correlates with Conscious Perception , 2007, The Journal of Neuroscience.

[15]  Erich E. Sutter,et al.  The brain response interface: communication through visually-induced electrical brain responses , 1992 .

[16]  E. Donchin,et al.  A P300-based brain–computer interface: Initial tests by ALS patients , 2006, Clinical Neurophysiology.

[17]  M. Sterman Effects of Sensorimotor EEG Feedback Training on Sleep and Clinical Manifestations of Epilepsy , 1977 .

[18]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[19]  W. Klimesch,et al.  Visual discrimination performance is related to decreased alpha amplitude but increased phase locking , 2005, Neuroscience Letters.

[20]  Fernando Henrique Lopes da Silva,et al.  The hemodynamic response of the alpha rhythm: An EEG/fMRI study , 2007, NeuroImage.

[21]  R. Oostenveld,et al.  Neuronal Dynamics Underlying High- and Low-Frequency EEG Oscillations Contribute Independently to the Human BOLD Signal , 2011, Neuron.

[22]  W. Singer,et al.  Oscillatory responses in cat visual cortex exhibit inter-columnar synchronization which reflects global stimulus properties , 1989, Nature.

[23]  K. Linkenkaer-Hansen,et al.  Prestimulus Oscillations Enhance Psychophysical Performance in Humans , 2004, The Journal of Neuroscience.

[24]  W. Penfield,et al.  THE BRAIN'S RECORD OF AUDITORY AND VISUAL EXPERIENCE. A FINAL SUMMARY AND DISCUSSION. , 1963, Brain : a journal of neurology.

[25]  Abderrahmane Kheddar,et al.  Tactile interfaces: a state-of-the-art survey , 2004 .

[26]  Steven W. Smith,et al.  The Scientist and Engineer's Guide to Digital Signal Processing , 1997 .

[27]  H. Heinrich,et al.  Is neurofeedback an efficacious treatment for ADHD? A randomised controlled clinical trial. , 2009, Journal of child psychology and psychiatry, and allied disciplines.

[28]  C. Gerloff,et al.  Enhancing cognitive performance with repetitive transcranial magnetic stimulation at human individual alpha frequency , 2003, The European journal of neuroscience.

[29]  Marco Congedo,et al.  THE EFFECTS OF NEUROFEEDBACK TRAINING IN THE COGNITIVE DIVISION OF THE ANTERIOR CINGULATE GYRUS , 2007, The International journal of neuroscience.

[30]  J J Vidal,et al.  Toward direct brain-computer communication. , 1973, Annual review of biophysics and bioengineering.

[31]  Andrew T Duchowski,et al.  A breadth-first survey of eye-tracking applications , 2002, Behavior research methods, instruments, & computers : a journal of the Psychonomic Society, Inc.

[32]  N. Birbaumer,et al.  The thought translation device: a neurophysiological approach to communication in total motor paralysis , 1999, Experimental Brain Research.

[33]  M. Congedo,et al.  Low-resolution electromagnetic tomography neurofeedback , 2004, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[34]  D. Vernon Can Neurofeedback Training Enhance Performance? An Evaluation of the Evidence with Implications for Future Research , 2005, Applied psychophysiology and biofeedback.

[35]  D. Lehmann,et al.  Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. , 1994, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[36]  L. Squire,et al.  The medial temporal lobe memory system , 1991, Science.

[37]  A. Zinober Matrices: Methods and Applications , 1992 .

[38]  B. Nolan Boosting slow oscillations during sleep potentiates memory , 2008 .

[39]  G. Pfurtscheller,et al.  Imagery of motor actions: differential effects of kinesthetic and visual-motor mode of imagery in single-trial EEG. , 2005, Brain research. Cognitive brain research.

[40]  R. Ward,et al.  EMG and EOG artifacts in brain computer interface systems: A survey , 2007, Clinical Neurophysiology.

[41]  R. Ilmoniemi,et al.  Interpreting magnetic fields of the brain: minimum norm estimates , 2006, Medical and Biological Engineering and Computing.

[42]  S. Edelman,et al.  Cue-Invariant Activation in Object-Related Areas of the Human Occipital Lobe , 1998, Neuron.

[43]  R D Pascual-Marqui,et al.  Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. , 2002, Methods and findings in experimental and clinical pharmacology.

[44]  Manuel Schabus,et al.  Increasing Individual Upper Alpha Power by Neurofeedback Improves Cognitive Performance in Human Subjects , 2005, Applied psychophysiology and biofeedback.

[45]  E Donchin,et al.  A new method for off-line removal of ocular artifact. , 1983, Electroencephalography and clinical neurophysiology.

[46]  T. Egner,et al.  Foundation and Practice of Neurofeedback for the Treatment of Epilepsy , 2006, Applied psychophysiology and biofeedback.

[47]  G Calhoun,et al.  Brain-computer interfaces based on the steady-state visual-evoked response. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[48]  J C Lind,et al.  Low‐resolution electrical tomography of the brain during psychometrically matched verbal and spatial cognitive tasks , 2001, Human brain mapping.

[49]  E. Düzel,et al.  Medial temporal theta state before an event predicts episodic encoding success in humans , 2009, Proceedings of the National Academy of Sciences.

[50]  Mark A. Clements,et al.  Automatic Speechreading with Applications to Human-Computer Interfaces , 2002, EURASIP J. Adv. Signal Process..

[51]  Leonid Zhukov,et al.  Lead-field Bases for Electroencephalography Source Imaging , 2000, Annals of Biomedical Engineering.

[52]  Karl J. Friston,et al.  Anatomically Informed Basis Functions for EEG Source Localization: Combining Functional and Anatomical Constraints , 2002, NeuroImage.

[53]  Roger D. Traub,et al.  Simulation of Gamma Rhythms in Networks of Interneurons and Pyramidal Cells , 1997, Journal of Computational Neuroscience.

[54]  Y. Takane,et al.  Generalized Inverse Matrices , 2011 .

[55]  D. McCandless Fundamental neuroscience , 1997, Metabolic Brain Disease.

[56]  B. Welford Note on a Method for Calculating Corrected Sums of Squares and Products , 1962 .

[57]  Christian Kothe,et al.  Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general , 2011, Journal of neural engineering.

[58]  J. Lubar,et al.  EEG and behavioral changes in a hyperkinetic child concurrent with training of the sensorimotor rhythm (SMR) , 1976, Biofeedback and self-regulation.

[59]  J. Lisman The theta/gamma discrete phase code occuring during the hippocampal phase precession may be a more general brain coding scheme , 2005, Hippocampus.

[60]  Larry B. Silver,et al.  Attention Deficit Hyperactivity Disorder. A Handbook for Diagnosis and Treatment , 1991 .

[61]  Arne D. Ekstrom,et al.  Prestimulus theta activity predicts correct source memory retrieval , 2011, Proceedings of the National Academy of Sciences.

[62]  K. Reinikainen,et al.  Selective attention enhances the auditory 40-Hz transient response in humans , 1993, Nature.

[63]  H. Flor,et al.  The thought translation device (TTD) for completely paralyzed patients. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[64]  Dezhong Yao,et al.  A Self-Coherence Enhancement Algorithm and its Application to Enhancing Three-Dimensional Source Estimation from EEGs , 2001, Annals of Biomedical Engineering.

[65]  Sanjit K. Mitra,et al.  Handbook for Digital Signal Processing , 1993 .

[66]  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.

[67]  Leslie G. Ungerleider,et al.  Object vision and spatial vision: two cortical pathways , 1983, Trends in Neurosciences.

[68]  I. Nelken,et al.  Transient Induced Gamma-Band Response in EEG as a Manifestation of Miniature Saccades , 2008, Neuron.

[69]  J. P. Ary,et al.  Location of Sources of Evoked Scalp Potentials: Corrections for Skull and Scalp Thicknesses , 1981, IEEE Transactions on Biomedical Engineering.

[70]  T. Egner,et al.  The effect of training distinct neurofeedback protocols on aspects of cognitive performance. , 2003, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[71]  J. Gross,et al.  On the Role of Prestimulus Alpha Rhythms over Occipito-Parietal Areas in Visual Input Regulation: Correlation or Causation? , 2010, The Journal of Neuroscience.

[72]  K. Deisseroth,et al.  Parvalbumin neurons and gamma rhythms enhance cortical circuit performance , 2009, Nature.

[73]  Simon Hanslmayr,et al.  Prestimulus oscillations predict visual perception performance between and within subjects , 2007, NeuroImage.

[74]  Christopher Habel,et al.  Verbal Assistance in Tactile-Map Explorations: A Case for Visual Representations and Reasoning , 2010, Visual Representations and Reasoning.

[75]  M. Alexander,et al.  Principles of Neural Science , 1981 .

[76]  C. K. Yuen,et al.  Theory and Application of Digital Signal Processing , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[77]  C. Tallon-Baudry,et al.  How Ongoing Fluctuations in Human Visual Cortex Predict Perceptual Awareness: Baseline Shift versus Decision Bias , 2009, The Journal of Neuroscience.

[78]  T. Ergenoğlu,et al.  Alpha rhythm of the EEG modulates visual detection performance in humans. , 2004, Brain research. Cognitive brain research.

[79]  Thierry Pun,et al.  Design and Evaluation of Multimodal System for the Non-visual Exploration of Digital Pictures , 2003, INTERACT.

[80]  Z Kourtzi,et al.  Representation of Perceived Object Shape by the Human Lateral Occipital Complex , 2001, Science.

[81]  G. Pfurtscheller,et al.  Brain-Computer Interfaces for Communication and Control. , 2011, Communications of the ACM.

[82]  Christian Büchel,et al.  Neural Coupling Binds Visual Tokens to Moving Stimuli , 2005, The Journal of Neuroscience.

[83]  J. Palva,et al.  Very Slow EEG Fluctuations Predict the Dynamics of Stimulus Detection and Oscillation Amplitudes in Humans , 2008, The Journal of Neuroscience.

[84]  W. A. Sarnacki,et al.  Electroencephalographic (EEG) control of three-dimensional movement , 2010, Journal of neural engineering.

[85]  Daniel J Fox,et al.  Neurofeedback: An Alternative and Efficacious Treatment for Attention Deficit Hyperactivity Disorder , 2005, Applied psychophysiology and biofeedback.

[86]  J. Tukey,et al.  An algorithm for the machine calculation of complex Fourier series , 1965 .

[87]  Karl J. Friston,et al.  Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem , 2002, NeuroImage.

[88]  Beat Fasel,et al.  Automati Fa ial Expression Analysis: A Survey , 1999 .

[89]  Manbir Singh,et al.  An Evaluation of Methods for Neuromagnetic Image Reconstruction , 1987, IEEE Transactions on Biomedical Engineering.

[90]  Stephen A. Brewster,et al.  Multimodal 'eyes-free' interaction techniques for wearable devices , 2003, CHI '03.

[91]  M. Fuchs,et al.  A standardized boundary element method volume conductor model , 2002, Clinical Neurophysiology.

[92]  Juergen Luettin,et al.  Audio-Visual Automatic Speech Recognition: An Overview , 2004 .

[93]  F. Piccione,et al.  P300-based brain computer interface: Reliability and performance in healthy and paralysed participants , 2006, Clinical Neurophysiology.

[94]  Tae Hong Park Introduction to digital signal processing - Computer Musically Speaking , 2009 .

[95]  Bernhard Hommel,et al.  Enhancing cognitive control through neurofeedback: A role of gamma-band activity in managing episodic retrieval , 2010, NeuroImage.

[96]  Renate Drechsler Ist Neurofeedbacktraining eine wirksame Therapiemethode zur Behandlung von ADHS? Ein Überblick über aktuelle Befunde , 2011 .

[97]  U Hegerl,et al.  Comparison between the analysis of the loudness dependency of the auditory N1/P2 component with LORETA and dipole source analysis in the prediction of treatment response to the selective serotonin reuptake inhibitor citalopram in major depression , 2002, Clinical Neurophysiology.

[98]  T. Egner,et al.  Validating the efficacy of neurofeedback for optimising performance. , 2006, Progress in brain research.

[99]  Simon P. Kelly,et al.  Visual spatial attention control in an independent brain-computer interface , 2005, IEEE Transactions on Biomedical Engineering.

[100]  Touradj Ebrahimi,et al.  Brain-computer interface in multimedia communication , 2003, IEEE Signal Process. Mag..

[101]  A. Villringer,et al.  How Ongoing Neuronal Oscillations Account for Evoked fMRI Variability , 2011, The Journal of Neuroscience.

[102]  J. Mäkelä,et al.  Magnetoencephalographic cortical rhythms. , 1997, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[103]  Farzin Deravi,et al.  A review of speech-based bimodal recognition , 2002, IEEE Trans. Multim..

[104]  Chen Yu,et al.  A multimodal learning interface for grounding spoken language in sensory perceptions , 2003, ICMI '03.

[105]  Sean M Montgomery,et al.  Entrainment of Neocortical Neurons and Gamma Oscillations by the Hippocampal Theta Rhythm , 2008, Neuron.

[106]  V. Krishnaveni,et al.  Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients , 2006, Journal of neural engineering.

[107]  H. R. Doig,et al.  Effect of saccade size on presaccadic spike potential amplitude. , 1989, Investigative ophthalmology & visual science.

[108]  E D Adrian,et al.  The interpretation of potential waves in the cortex , 1934, The Journal of physiology.

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

[110]  W. Singer,et al.  Dynamic predictions: Oscillations and synchrony in top–down processing , 2001, Nature Reviews Neuroscience.

[111]  A. B. Rami Shani,et al.  Matrices: Methods and Applications , 1992 .

[112]  Kathy P. Wheeler,et al.  Reviews of Modern Physics , 2013 .

[113]  Dennis J. McFarland,et al.  Brain–computer interfaces for communication and control , 2002, Clinical Neurophysiology.

[114]  Hellmuth Obrig,et al.  Correlates of alpha rhythm in functional magnetic resonance imaging and near infrared spectroscopy , 2003, NeuroImage.

[115]  S. Kastner,et al.  Two hierarchically organized neural systems for object information in human visual cortex , 2008, Nature Neuroscience.

[116]  Christine L Larson,et al.  Brain electrical tomography in depression: the importance of symptom severity, anxiety, and melancholic features , 2002, Biological Psychiatry.

[117]  Thilo Hinterberger,et al.  An Auditory Brain-Computer Interface Based on the Self-Regulation of Slow Cortical Potentials , 2005, Neurorehabilitation and neural repair.

[118]  H. Flor,et al.  A spelling device for the paralysed , 1999, Nature.

[119]  Z. Koles,et al.  Trends in EEG source localization. , 1998, Electroencephalography and clinical neurophysiology.

[120]  M. Posner,et al.  Attention and the detection of signals. , 1980, Journal of experimental psychology.

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

[122]  A. Schlögl,et al.  Artifact Processing in Computerized Analysis of Sleep EEG – A Review , 1999, Neuropsychobiology.

[123]  Vincent Walsh,et al.  Frequency-Dependent Electrical Stimulation of the Visual Cortex , 2008, Current Biology.

[124]  T. Koenig,et al.  Low resolution brain electromagnetic tomography (LORETA) functional imaging in acute, neuroleptic-naive, first-episode, productive schizophrenia , 1999, Psychiatry Research: Neuroimaging.

[125]  Matthias M. Müller,et al.  Selective visual-spatial attention alters induced gamma band responses in the human EEG , 1999, Clinical Neurophysiology.

[126]  Jessica A. Cardin,et al.  Driving fast-spiking cells induces gamma rhythm and controls sensory responses , 2009, Nature.

[127]  R. Malach,et al.  Object-related activity revealed by functional magnetic resonance imaging in human occipital cortex. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[128]  Robert Desimone,et al.  Parallel and Serial Neural Mechanisms for Visual Search in Macaque Area V4 , 2005, Science.

[129]  S. Mitra,et al.  Handbook for Digital Signal Processing , 1993 .

[130]  D. Hammond,et al.  Neurofeedback with anxiety and affective disorders. , 2005, Child and adolescent psychiatric clinics of North America.

[131]  J. Schoffelen,et al.  Prestimulus Oscillatory Activity in the Alpha Band Predicts Visual Discrimination Ability , 2008, The Journal of Neuroscience.

[132]  P. König,et al.  A Functional Gamma-Band Defined by Stimulus-Dependent Synchronization in Area 18 of Awake Behaving Cats , 2003, The Journal of Neuroscience.

[133]  B. Connors,et al.  Synchronous Activity of Inhibitory Networks in Neocortex Requires Electrical Synapses Containing Connexin36 , 2001, Neuron.

[134]  R. Traub,et al.  Neuronal fast oscillations as a target site for psychoactive drugs. , 2000, Pharmacology & therapeutics.

[135]  O. Bertrand,et al.  Oscillatory gamma activity in humans and its role in object representation , 1999, Trends in Cognitive Sciences.

[136]  D. McFarland,et al.  An auditory brain–computer interface (BCI) , 2008, Journal of Neuroscience Methods.

[137]  R. Traub,et al.  A mechanism for generation of long-range synchronous fast oscillations in the cortex , 1996, Nature.

[138]  G. Oriolo,et al.  Non-invasive brain–computer interface system: Towards its application as assistive technology , 2008, Brain Research Bulletin.

[139]  D. C. Howell Statistical Methods for Psychology , 1987 .

[140]  O. Bertrand,et al.  Attention modulates gamma-band oscillations differently in the human lateral occipital cortex and fusiform gyrus. , 2005, Cerebral cortex.

[141]  H. C. Burger,et al.  HEART-VECTOR AND LEADS. , 1946, British heart journal.

[142]  David Thomas,et al.  The Art in Computer Programming , 2001 .

[143]  B. Rockstroh Slow cortical potentials and behavior , 1989 .

[144]  B. Rockstroh,et al.  Slow potentials of the cerebral cortex and behavior. , 1990, Physiological reviews.

[145]  E. Niebur,et al.  Growth patterns in the developing brain detected by using continuum mechanical tensor maps , 2022 .

[146]  C. Büchel,et al.  Functional Dissociation of Ongoing Oscillatory Brain States , 2012, PloS one.

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

[148]  P. Rossini,et al.  Pre- and poststimulus alpha rhythms are related to conscious visual perception: a high-resolution EEG study. , 2005, Cerebral cortex.

[149]  Vincent J Monastra,et al.  Electroencephalographic Biofeedback in the Treatment of Attention-Deficit/Hyperactivity Disorder , 2005, Applied psychophysiology and biofeedback.

[150]  R. VanRullen,et al.  The Phase of Ongoing Oscillations Mediates the Causal Relation between Brain Excitation and Visual Perception , 2011, The Journal of Neuroscience.

[151]  Valer Jurcak,et al.  10/20, 10/10, and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems , 2007, NeuroImage.

[152]  Robert J. K. Jacob,et al.  Evaluation of eye gaze interaction , 2000, CHI.

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

[154]  R. Traub,et al.  Synchronized oscillations in interneuron networks driven by metabotropic glutamate receptor activation , 1995, Nature.

[155]  F. Varela,et al.  Perception's shadow: long-distance synchronization of human brain activity , 1999, Nature.

[156]  E Donchin,et al.  The mental prosthesis: assessing the speed of a P300-based brain-computer interface. , 2000, IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society.

[157]  Adam Wilson,et al.  A Procedure for Measuring Latencies in Brain–Computer Interfaces , 2010, IEEE Transactions on Biomedical Engineering.

[158]  Shlomit Yuval-Greenberg,et al.  Saccadic spike potentials in gamma-band EEG: Characterization, detection and suppression , 2010, NeuroImage.

[159]  Spyros G. Tzafestas,et al.  The autonomous mobile robot SENARIO: a sensor aided intelligent navigation system for powered wheelchairs , 1997, IEEE Robotics Autom. Mag..

[160]  S. P. Levine,et al.  Adaptive shared control of a smart wheelchair operated by voice control , 1997, Proceedings of the 1997 IEEE/RSJ International Conference on Intelligent Robot and Systems. Innovative Robotics for Real-World Applications. IROS '97.

[161]  Tieniu Tan,et al.  A survey on visual surveillance of object motion and behaviors , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[162]  R. Desimone,et al.  Modulation of Oscillatory Neuronal Synchronization by Selective Visual Attention , 2001, Science.