2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 1 Developmentof ComplexCurriculaforMolecularBionicsand InfobionicsProgramswithina consortial* framework** Consortiumleader PETER PAZMANYCATHOLICUNIVERSITY Consortiummembers SEMMELWEIS UNIVERSITY, DIALOGCAMPUS PUBLISHER The Project has beenrealisedwiththesupportof theEuropean Union and has beenco-financedbytheEuropean SocialFund*** **Molekuláris bionika és Infobionika Szakok tananyagának komplex fejlesztése konzorciumi keretben ***A projekt az Európai Unió támogatásával, az Európai Szociális Alap társfinanszírozásával valósul meg. PETER PAZMANY CATHOLIC UNIVERSITY SEMMELWEIS UNIVERSITY sote_logo.jpg dk_fejlec.gif INFOBLOKK 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 2 Peter Pazmany Catholic University Faculty of Information Technology NEURAL INTERFACES AND PROSTHESES PHYSIOLOGICALBASISOF BRAIN-COMPUTER INTERFACE www.itk.ppke.hu Neurális interfészek és protézisek Agy-számítógép kapcsolat fiziológiai alapjai BALÁZS DOMBOVÁRI & GYÖRGY KARMOS LECTURE10 NeuralInterfacesAnd ProsthesesPhysiological BasisOf Brain-computer Interface www.itk.ppke.hu 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 3 INTHISLECTUREYOU’LLLEARN: NeuralInterfacesAnd ProsthesesPhysiological BasisOf Brain-computer Interface www.itk.ppke.hu 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 4 DEFINITION The brain electrophysiological signals can be used for communication with the external world as well as for manipulation of technical devices such prostheses and microprocessors. This type of biofeedback applications is named as Brain-Computer Interface (BCI). It is a multidisciplinary field comprising areas such as computer and information sciences, engineering, neuroscience, and psychology. Assistive Device: the component of the BCIthat directly interact with the objects or people in the environment. Feature extractor:the component of the BCIthat translates the inputbrain signal into a feature vector correlated to a neurological phenomenon. Feature Translator:the component of the BCIthat translates the feature vector into a useful control signal. NeuralInterfacesAnd ProsthesesPhysiological BasisOf Brain-computer Interface www.itk.ppke.hu 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 5 In general,a BCIsystemcomprisesfivestages: datacollection, pre-processing, featureextraction, decisionmakingorfeaturetranslationand devicecommand. GENERAL BUILD-UPOF A BCI SUBJECT DATA COLLECTION PRE-PROCESSING FEATURE EXTRACTION FEATURETRANSLATOR DEVICE COMMAND Feedback Thisis doneina PC 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 6 www.itk.ppke.hu AREAS OF BCIAPPLICATION There are diseases or pathological states when the muscle system of the patients is totally paralyzed. According to the early views BCIcan help the patients to keep contact with the environment or control assistive devices only if there is no other physiological function that can serve this. If there are other ways (for example by eye movements or by blinking) these have to be preferred. Nowadays as the BCItechnology became more advanced, BCImay be used as part of a hybrid assistive system using traditional inputs. Here the BCIcan be used as an additional input channel. (See Lecture 12) In the present course we deal only with those BCIdevices that serve neuroprostheticpurpose. Recently series of commercially available BCIdevices were developed for games etc. These are only shortly discussed in Lecture 12. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 7 www.itk.ppke.hu DISEASES IN WHICH BCIMAY HELP PATIENTS There are degenerative diseases of the motor neurons in the central nervous system that result extended paralysis of the muscles. These are the Amyotrophic lateral sclerosis (ALS) and Spinal Muscular Atrophy (SMA). Paralysis of muscles of the whole body also can be caused by stroke or brain hemorrhage in the ventral partsof the pons cerebri, destroying the corticospinalmotor pathways. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface Lebedev& Nicolelis,TINS, 2006, 29: 536-546 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 8 www.itk.ppke.hu LOCKED IN STATE Amyotrophic lateral sclerosis (ALS)(alsocalledasLouGehrig’s disease)is a progressive motor disease of unknown etiology that results in a complete destruction of the peripheral and central motor system affecting sensory or cognitive functions to a minor degree. There is neither a standard treatment available, nor is a cure. Patients with ALS have to decide to accept artificial respiration and feeding after the disease destroys respiratory and bulbar functions for the rest of their life or die of respiratory problems.If they opt for life and accept artificial respiration, the disease progresses until the patient loses control of the last muscular response, which is usually the eye muscle or the external sphincter. The resulting condition is called completely locked-in state (CLIS), if rudimentary control of at least one muscle is present we speak of a locked-in state (LIS). NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 9 www.itk.ppke.hu WHATKINDOF BRAINIMAGINGTECHNIQUESAREGOODFORBUILDING BCI? NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface C:\Documents and Settings\gkarmos\Desktop\Neural_10.gif EEG: electroencephalogram ECoG: electrocorticogram MEG: magnetoencephalogram fMRI: Functionalmagneticimaging LFP: local fieldpoetntial SUA: singleunit activity MUA: multiunitactivity 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 10 www.itk.ppke.hu WHAT KIND OF BRAIN IMAGING TECHNIQUES ARE GOOD FOR BUILDING BCI? NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface BMI, brainmachineinterface; EEG, electroencephalogram; LFP, local fieldpotential; M1, primarymotor cortex; PP, posteriorparietalcortex. Lebedev& Nicolelis,TINS, 2006 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 11 www.itk.ppke.hu BRAINELECTRICALSIGNALSUSEDFORBCI NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface MACRO POTENTIALS: Noninvasive: Electroencephalogram (EEG) Event-related potential (ERP),P300component Steadystate response (SSR),Visual SSR Event related desynchronization/synchronization Invasive: Electrocorticogram(ECoG) NEURONAL ACTIVITY: Invasive: Single unit activity (SUA) Multiunit activity (MUA) C:\Documents and Settings\gkarmos\Desktop\Azideg_1.gif C:\Documents and Settings\gkarmos\Desktop\Azideg_2.gif C:\Documents and Settings\gkarmos\Desktop\Presentation1.gif C:\Documents and Settings\gkarmos\Desktop\Azideg_1.gif C:\Documents and Settings\gkarmos\Desktop\Neural.gif C:\Documents and Settings\gkarmos\Desktop\Presentation1.gif C:\Documents and Settings\gkarmos\Desktop\Presentation1.gif 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 12 www.itk.ppke.hu ELECTROPHYSIOLOGICAL METHODS FOR BCICONSTRUCTION Attempts to solve the problem of communication in patients who are paralyzed have led to several strategies that involve direct communication between the brain and a computer. As we saw in the previous slide, the most usable techniques to build a BCIare electrophysiological methods from single cell recording through local field potential to scalp electroencephalogram. In the next few slides our aim is to show how electrophysiological signals are generated in the brain and which biopotentialchanges are suitable for BCIuse. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 13 www.itk.ppke.hu MEASURINGBRAINACTIVITY The brain is a dense network consisting of about 100 billion neurons. Each of these neurons communicates with about 10 thousands of others. Neurons communicate mostly via synapses by exchanging neurotransmitters or by sending electrical signals via gap junctions. The electrical activity of neurons can be divided into two parts: action potentials (AP) and postsynaptic potentials (PSP). The PSP-s are summated in the neuron and if the membrane depolarization reaches the threshold level at the axon hillock, the neuron fires and an AP is initiated in its axon. The electrical potentials recordable on the scalp surface are generated by low frequency summed inhibitory and excitatory PSPsof neocorticalpyramidal neurons that form electrical dipoles between the soma and apical dendrites. These create the local field potentials in the cortex and extend to the scalp surface where they are recorded as EEG oscillations. Neural Interfaces And Prostheses: Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 14 www.itk.ppke.hu THE PHYSIOLOGY OF THE HUMAN BRAIN (CONT.) Nerve cell APshave a much smaller potential field distribution and are much shorter in duration than PSPs. APstherefore do not contribute significantly to either scalp or clinical intracranial EEG recordings. Only large populations of simultaneous active neurons can generate electrical activity recordable on the scalp. Because of the electrical properties of the brain tissue the action potentials of the neurons do not spread to large distance in the extracellular space. Therefore the action potentials of the neuron called unit activity can be recorded only by small tip size microelectrodes inserted close to the cell. In most cases extracellular microelectrode records action potentials/spikes of more than one neuron. In this case the amplitude and shape of the unit spikes depend on the distance of the given neuron from the recording microelectrode. Neural Interfaces And Prostheses: Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 15 www.itk.ppke.hu THE PHYSIOLOGYOF THEHUMAN BRAIN(CONT.) ThecerebralcortexisthemostrelevantstructureinrelationtoEEGmeasurement.Itisresponsibleforhigherordercognitivetaskssuchasproblemsolving,languagecomprehensionandprocessingofcomplexsensoryinformation.Duetoitssurfaceposition,theelectricalactivityofthecerebralcortexhasthegreatestinfluenceonEEGrecordings.Thefunctionalactivityofthebrainishighlylocalized.Thisfacilitatesthecerebralcortextobedividedintoseveralareasresponsiblefordifferentbrainfunctions. Sincethearchitectureofthebrainisnon-uniformandthecortexisfunctionallyorganized,theEEGcanvarydependingonthelocationoftherecordingelectrodes. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 16 www.itk.ppke.hu PROPERTIES OF EEG TheEEGrepresentingbrainwavesoriginateingfromamultitudeofdifferentneuronalcommunitiesfromvariousregionsofthebrain.Theseneuronalcommunitiesproduceelectricalcontributionsorcomponentsthatcandifferbyanumberofcharacteristicssuchastopographiclocation,firingrate(frequency),amplitude,latencyetc. Thevolumetriceffectofthecerebrospinalfluid,skullandscalpresultinasmearingoftheseelectricalcomponentsthatresultinthescalprecordedEEGmacropotential.Similarcoherentelectricalactivitycanbepickedupinnearbyelectrodes. TheEEGactivityabovedifferentregionsofthescalpmayreflectlocalactivitybutmayalsoreflectactivityofdistantneocorticalareas.Thesearecalledclosedfieldandfarfieldactivities. Neural Interfaces And Prostheses: Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 17 www.itk.ppke.hu DESCRIPTORS OF EEG SIGNALS This section highlights the many descriptors that are used with EEG recorded signals or its decomposed components to help in the categorization and description of complex brain activity. Clinical electroencephalography uses a large number of these escriptors, particularly in the study of epilepsy, to facilitate accurate analysis. In relation to cognitive research the most important aspects of EEG activity are distribution, frequency, amplitude, morphology, periodicity but more importantly the behavioral and functional correlates. In summary, EEG requires a considerable level of experience to accurately identify and characterize the signals. The table of the next slide summarize the descriptors used in EEG analysis. Neural Interfaces And Prostheses: Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 18 www.itk.ppke.hu Descriptor of EEG signals Explanation Characterisation examples Morpology Shape of the wave Rhythmical (regular), Arrhythmical (irregular), Sinusoidal, Spindles Complexes, Spikes,Polyspikes, Sharp waves Repetition Defines the type ofwaveform occurrence Rhythmic, Semi rhythmic,Irregular Frequency How often a repetitive wave recurs Frequency Bands: Delta, Theta, Alpha, Beta Amplitude Measured in microvolts (µV) peak-to-peak or from the calibrated zero reference Clinical reference: Low ( < 20µV), Medium (20-50µV),High (>50µV), Amplitude assymmetry Distribution The occurrence of electrical activity recorded byelectrodes positioned over different parts of the head Widespread, Diffuse (generalised),Lateralised, Localised (Focal) Phase relation The relative timing and polarity of components of waves in one or morechannels e.g. Do the troughs and peaks line up? In-phase,Out of phase,Phase Angle Timing Relative occurrence of activity in time at different parts of the brain recorded by different channels Simultaneous (Synchronous),Independent (Asynchronous),Bilaterally synchronous Persistence How often a wave or pattern occurs during a recording session Index percentage (Proportion of time for which these waves appear in the recording),Poorly / Well sustained,High, moderate & low persistence Reactivity Refers to changes that can be produced in some normal and abnormal patterns by various maneuvers or functions EEG alteration in response to: Closing the eyes,Hyperventilation,Visual or sensory stimulation,Changes in levels of alertness, Movements or movement imagination NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 19 www.itk.ppke.hu Silver/silver-chloride nonpolarizableelectrodes are used for scalp EEG recording. A special gel is applied between the electrode and the skin to assure good conductance. According to the international standards the impedance of the scalp-electrode interface must be below 5 k.. The electrodes are placed to the scalp according to the „international 12-20 system”. Frequency range, amplified by the modern digital EEG amplifiers can be positioned between DC or 0,1 Hz and 10 kHz. Neural Interfaces And Prostheses: Physiological BasisOf Brain-computer Interface G:\My Documents\oktatás\TÁMOP\2010\Csuhajképek_files\Kepek\P_BCI_III.jpg BrainProducts EEG BCISystem G:\TÁMOP_PROSTH\BCI\ÁBRÁK\Media,79025,en.jpg Extended10-20 electrodesystem EEG TECHNOLOGY 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 20 www.itk.ppke.hu EEG SIGNALCLASSESFORBCISYSTEMS For the purposes of BCIsystem design, there exist various EEG signal properties that discriminate brain function and hence can be used as an input mechanism to offer control or communication. EEG signal properties for BCIsystems can be categorized into one of the following groups: 1. Rhythmic and slowbrain activity 2. Event-related potentials (ERPs) 3. Event-related desynchronization(ERD) and event-relatedsynchronization (ERS). NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 21 www.itk.ppke.hu RHYTHMICBRAINACTIVITY The human EEG potentials are manifested as aperiodic unpredictable oscillations with intermittent bursts of oscillations having spectral peaks in certain traditional bands: 1-4 Hz (delta, .), 4-8 Hz (theta, .), 8-13 Hz (alpha, .), 14-30 Hz (beta, ß) and >30Hz(gamma, .) EEG potential changes below 1 Hz is called slow potentials. The band range limits associated with the brain rhythms, particularly beta and gamma, can be subject to contradiction and are often further sub-divided into sub-bands that can further distinguish brain processes. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 22 www.itk.ppke.hu RHYTHMIC BRAIN ACTIVITY (cont.) Delta activity appears in adults only in deep sleep. It can be recorded in coma and may shows tumor or epi-or subdural hematoma. Theta activity can be recorded light sleep in adult. Computer processing shows that theta may appear in temporal leads during cognitive tasks. Alpha rhythm is characteristic in quiet wakefulness, with closed eyes, above the occipital area. This synchronized activity disappears at eye opening and the EEG amplitude decreases and the rhythm becomes desynchronized. Mu rhythm is an alpha like oscillation. It may appear above the motor cortex in motionless state. At self-paced movement it changes to desynchronized activity. Beta activity is low amplitude fast rhythm. It is characteristic to alert state. Beta may appear in bursts above the motor cortex at the cessation of a self-paced movement. Gamma activity is related to cognitive processes like attention and perceptional binding. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 23 www.itk.ppke.hu CHARACTERISTIC RHYTHMS OF THE EEG NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface G:\TÁMOP_PROSTH\BCI\ÁBRÁK\vigilance.gif 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 24 www.itk.ppke.hu EVENT-RELATED POTENTIALS (ERPS) ERPsare time locked bioelectrical brain potential oscillations, elicited by a sensory stimulus, or associated with execution of a motor, cognitive, or psychophysical task. Classification of ERPs: Type of event: Sensory evoked potential Motor potential Event-related synchronization /desynchronization Steady state response Induced response Classification of the components of ERP: By latency: Early-, Mid-latency-, Late-components By nature of the evoking effect: Exogenous components, Endogenous components NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 25 www.itk.ppke.hu EVENT-RELATEDPOTENTIALS(CONT.) Amplitudes of ERP components are often much smaller than spontaneous EEG components, typically a factor of10. They are subsequently unrecognizable from the raw EEG trace. They can be analyzed by ensemble averaging EEG epochs time-locked to repeated sensory, cognitive or motor events. The assumption is that the event-related activity, or signal of interest, has a more or less fixed time delay to the stimulus, while the spontaneous background EEG fluctuations is random relative to the time when the stimulus occurred. Averaging across the time-locked epochs highlights the underlying ERP by averaging out the random background EEG activity, thus improving the signal-to-noise ratio. These electrical signals reflect only the activity which is consistently associated with the stimulus processing in a time-locked manner. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface Electrophysiological Methods For The Study Of The Nervous-And Muscular-systems:Event-Related Potentials www.itk.ppke.hu 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 26 C:\Documents and Settings\gkarmos\Desktop\Electrophysiological Methods.bmp Itwilltakefourtimesasmanytrialstomakeitlooktwotimesbetter. N EFFECTOF AVERAGINGONTHE S/N OF ERPS EEG 4xave. 16xave. S 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 27 www.itk.ppke.hu EVENT-RELATED POTENTIALS (ERPS) Exogenous components of the ERPsare usually the early, short latency components. Their parameters are determined by the physical properties of the evoking stimuli. They can be used to test the functional propetiesof the sensory pathways. Endogenous components of the ERPsprovide a suitable methodology for studying the aspects of cognitive processes of both normal and abnormal nature, like in neurological or psychiatric disorders. Mental operations such as those involved in perception, selective attention, language processing and memory, proceed over time ranges in the order of tens of milliseconds. Whereas PET and MRI can localize regions of activation during a given mental task, ERPscan help in defining the time course of these activations. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface CHARACTERISTICS OF EVENT RELATED POTENTIALS SHOWN ON THE AUDITORY ERP 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 28 www.itk.ppke.hu NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface Early components e.g. auditory brainstem evoked potential (BAEP) Middle-latency components Late components e.g. slow auditory response C:\Documents and Settings\gkarmos\Desktop\picture2.bmp Exogenous- Mezogenous- Endogenous- components 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 29 www.itk.ppke.hu EVENT-RELATED POTENTIALS (ERPS) Exogenous components of the ERPsare usually the early, short latency components. Their parameters are determined by the physical properties of the evoking stimuli. They can be used to test the functional propetiesof the sensory pathways. (e.g. BAEP) Endogenous components of the ERPsprovide a suitable methodology for studying the aspects of cognitive processes of both normal and abnormal nature, like in neurological or psychiatric disorders. (e.g. P300) Mental operations such as those involved in perception, selective attention, language processing and memory, proceed over time ranges in the order of tens of milliseconds. Whereas PET and MRI can localize regions of activation during a given mental task, ERPscan help in defining the time course of these activations. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 30 www.itk.ppke.hu EVOKED POTENTIALS (EPS) Evoked potentials (EPs) are a subset of the ERPsthat occur in response to certain physical stimuli (auditory, visual, somatosensory etc.). They can be considered to result from a reorganization of the phases of the ongoing EEG signals. The EPs can have distinguishable properties related to different properties of the stimuli, for example, Visual Evoked Potential (VEP) over the visual cortex varies at the same frequency as the stimulating light. There are many successful EP based BCIsystems that utilize VEPs, or P300sas inputs. If repetition rate of the stimuli are increased above a certain rate EPs merge into a sinus like oscillation. It is called „steady state response” (SSR). Visual SSR(VSSR) also was demonstrated as succesfulsignal for BCI. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 31 www.itk.ppke.hu InERP researchoftenusedparadigmis thesocalled„oddballparadigm” The subject is presented with two types of stimuli. One is a frequently occurring, more common stimulus (called standard or non-target) interleaved by infrequently, rare (‘oddball’) stimuli. The ERPselicited by the standard and deviant stimuli are compared. The oddball paradigm can be passive, if the subject has no task to respond to either of the stimuli. In activeoddball paradigm the subject is asked to indicate the occurrence of the rare (target) stimuli by counting or by pressing a button. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface ODDBALL PARADIGM P300inauditoryoddbalparadigm Distributionof P300abovethescalp 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 32 www.itk.ppke.hu EVENT-RELATEDDESYNCHRONIZATIONAND SYNCHRONIZATION(ERD/ERS) In1977 Pfurtschellerand Aranibarfirst quantified event-related desynchronization(ERD). Itappearsabovethemotor areaof theneocortex atselfpacedmovements. ERDis amplitude attenuation and ERS is amplitude enhancement of a certain EEG rhythm. In order to measure ERDor an ERS, the power of a chosen frequency band is calculated before and after the event over a number of trials. The average power across a numberof trials is then measured in percentage relative to the power of the reference interval. The reference interval can be an arbitrary period prior to the event representing a period of inactivity or rest. The ERS is the power increase (in percent) and the ERD is the power decrease relative to the reference interval that is defined as 100%. ERD/ERS measurements selected over specific frequency ranges are typically used to produce a spatio-temporal map to visualize thefunctional behavior of the brain. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 33 www.itk.ppke.hu PROCESSINGOF ERDAND ERS NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface AlphaERDand betaERStorepeatedflexionof theindex finger. ERDappearsbeforeand duringmovement, ERSappearsaftertheterminationof themovement. Processingsteps: Bandpass-filteringSquaringAveragingCalculatingrelativepowerchange Pfurtschellerand Lopesda Silva, Clin. Neurophysiol. 1999 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 34 www.itk.ppke.hu LOCALIZATIONOF ERD/ERSABOVETHE MOTOR CORTEX NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface The localization of ERDas well as the ERS corresponds to the cortical representation of the given movement. ERD/ERS appear not only to the execution of a movement but also to the movement imagination. This means that it can be used in totalyparalizedpatients as input to BCIsystems. Pfurtschellerand Lopesda Silva, Clin. Neurophysiol. 1999 C:\Documents and Settings\gkarmos\Desktop\Presentation5.gif C:\Documents and Settings\gkarmos\Desktop\Presentation3.gif 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 35 www.itk.ppke.hu REFERENCES Pfurtscheller, G. and Lopesda Silva, F.H. Event-relatedEEG/MEG synchronizationand desynchronization: basicprinciples. Clin. Neurophysiol. 1999, 110: 1842–1857 Finn, W.E., LoPresti, P.G. (eds.): Handbookof NeuroprostheticMethods, (BiomedicalEngineeringSeries), CRCPress, 2003. Andersen, R.A., Musallam, S., Pesaran, B. Selectingthesignalsfora brainmachineinterface. Curr.Opin. Neurobiol. 2004, 14: 720–726. Horch, K.W., Grpreet, D. (eds.) Neuroprosthetics: Theoryand Practice, Series onBioengineering& BiomedicalEngineering-Vol. 2, World ScientificPub CoInc, 2004. Niedermayer, E., LopesDa Silva, F., (eds): Electroencephalograhy: Basic Principles, ClinicalApplications, and RelatedFields, (5thed.) LippincottWilliams and Wilkins, Philadelphia, 2005. LebedevMA, NicolelisMAL. 2006. Brain-machineinterfaces: past, presentand future. TrendsNeurosci. 2006, 29: 536–546. Donoghue,J.P. Bridging the brain to the world: a perspective on neural interface systems.Neuron. 2008,60:511-21. Hatsopoulos, N.G., Donoghue, J.P. The Science of NeuralInterfaceSystems. Annu. Rev. Neurosci., 2009, 32: 249-266. Andersen, R.A., Hwang,E.J., Mulliken, G.H. Cognitiveneuralprosthetics. Annu. Rev. Psychol., 2010, 61: 169-190. Graimann, B., Allison, B., Pfurtscheller, G. (eds.) Brain-ComputerInterfaces: RevolutionizingHuman-Computer Interaction, Springer, 2010. NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface 2011.09.14.. TÁMOP –4.1.2-08/2/A/KMR-2009-0006 36 www.itk.ppke.hu NeuralInterfacesAnd Prostheses Physiological BasisOf Brain-computer Interface REVIEW QUESTIONS: • What is brain-computer interface? • What is BCIfeature extractor? • What are the main application areas of the BCIs? • Describe the symptoms of the Amyotrophic lateral sclerosis. • What is „locked in state”? • List the types of electrical signals that are used in BCIs. • Which are the main descriptors of the EEG? • Describe the main types of ERPs. • What is the oddball paradigm? • What are the main features of the ERD/ERS?