Brain Computer Interfaces (BCI) Communication Training of brain activity

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1 Brain Computer Interfaces (BCI) Communication Training of brain activity

2 Brain Computer Interfaces (BCI) picture rights: Gerwin Schalk, Wadsworth Center, NY

3 Components of a Brain Computer Interface

4 Applications of BCI Communication devices for paralyzed people

5 Applications of BCI Prosthetics

6 Applications of BCI Robotics / remote device control (e. g. space flight, military)

7 Applications of BCI Gaming

8 Applications of BCI Learning how to control brain activity

9 BCI Signals Visually evoked potentials (VEP) P300 (Positivity at 300 ms) Slow cortical potentials (SCP) Event-related synchronization / desynchronization (ERD / ERS) Mu and beta rhythms

10 VEP

11 P300

12 SCP

13 ERD/ERS

14 ERD/ERS

15 BCI Assumptions a) intended actions are fully represented in the cerebral cortex b) neuronal action potentials can provide the best picture of an intended action c) the best BCI is one that records action potentials and decodes them d) ongoing mutual adaptation by the BCI user and the BCI system is not very important

16 BCI Challenges Adaptation Actions are the products of many areas (from the cortex to the spinal cord) the contributions of each area change continually as the central nervous system (CNS) adapts to optimize performance BCIs must track and guide these adaptations to achieve and maintain good performance.

17 Brain Signal Present-day BCIs determine the intent of the user by measuring the brain s electric activity: (a) electrical signals recorded from the scalp (electroencephalography [EEG]) (b) electrodes surgically implanted on the cortical surface (ECoG) or within the brain (neuronal action potentials [spikes] or local field potentials [LFPs]). metabolic activity: (a) functional magnetic resonance imaging (fmri) (b) functional near-infrared spectroscopy (fnirs) (c) positron emission tomography (PET)

18 LFP: electrical current from nearby dendritic synaptic activity. A voltage is produced by the summed synaptic current flowing across the resistance of the local extracellular space. Berens et al., 2010, Local field potentials, BOLD and spiking activity relationships and physiological mechanisms

19 Non-invasive brain imaging Electroencephalography (EEG) Magnetoencephalography (MEG) Near Infrared Spectroscopy (NIRS) functional Magnetic Resonance Imaging

20 BCI Challenges Brain Signal It is not yet clear which category of brain signals will prove most effective for BCI applications! In human studies to date, low-resolution electroencephalography-based BCIs perform as well as high-resolution cortical neuron-based BCIs. BCIs allow their users to develop new skills in which the users control brain signals rather than muscles. The central task of BCI research is to determine which brain signals users can best control, to maximize that control, and to translate it accurately and reliably into actions that accomplish the users intentions. Wolpaw JR, 2010, Brain-computer interface research comes of age: traditional assumptions meet emerging realities., J Mot Behav.;42(6):351-3

21 A PRIMER ON THE BRAIN AND NERVOUS SYSTEM Further Reading Brain Facts - A Primer on the Brain and Nervous System, 2008, Society for Neuroscience Brain Facts Brain Explorer - Neurotransmitters control/neurological Neurotransmitters.shtml Fair et al., Development of distinct control networks through segregation and integration, 2007, PNAS Learning and Memory A. Pascual-Leone et al., 2005, The Plastic Human Cortex, Annu. Rev. Neurosci. Wolpaw JR, 2010, Brain-computer interface research comes of age: traditional assumptions meet emerging realities., J Mot Behav.;42(6):351-3 Berens et al., 2010, Local field potentials, BOLD and spiking activity relationships and physiological mechanisms

22 Electroencephalogram (EEG) EEG: recording of the electrical activity of the brain from the scalp. History: Hans Berger ( ) German psychiatrist First to prove (1929) the existence of electric potentials in the human brain using an amplifying machine (EEG). Before this, the English physician Richard Caton ( ) had proven the existence of similar potentials in dogs.

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24 Patient of Hans Berger EEG device (mid 1900 s) EEG traces measured by Hans Berger

25 from Herbert Henri Jasper Canadian psychologist, physiologist, anatomist, chemist and neurologist

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27 EEG characteristics 1. spontaneous electrical activity of the brain recorded with electrodes on the scalp (or sometimes on the cortical surface) 2. generally recorded from a number of sites simultaneously using electrodes distributed over the scalp 3. due to summated activity of a large group of neurons 4. useful for studying the basic features of sleep; clinically useful to define epilepsy and coma; also, confirmatory in brain death 5. used in biofeedback conditioning for control of autonomic functions Biofeedback: use of electronic instruments to make a subject aware of certain involuntary physiological parameters with the object of becoming able to subject these parameters to voluntary control through psychological conditioning

28 The Signal: What do EEG and MEG measure? originates mainly from cerebral cortex pyramidal cells because of their orientation relative to the cortical surface caused by current flow due to summated activity of EPSPs and IPSPs at dendritic synapses (not due to action potentials) EPSP: Excitatory postsynaptic potential / IPSP: Inhibitory postsynaptic potential only if thousands of cells contribute their small voltages does the signal become large enough to see at the surface Credit:

29 EEG waveforms Classification: by frequency and shape of the waves (listed in order of decreasing frequency) Rule of thumb: the more intense cortical mental activity, the smaller the amplitude and the higher the frequency of EEG waves; the less intense the mental activity, the more synchronous the activation of cortical neurons, leading to slower waves of larger amplitude

30 1. Beta waves (14-25 Hz) a) associated with being alert (eyes open) b) fast rhythm, indicates an active cortex c) small amplitude, best seen during intense mental activity 2. Alpha waves (8-13 Hz) a) associated with quiet, waking states (eyes closed) b) alpha blocking - when eyes open c) amplitude largest in occipital regions 3. Theta waves (4-7 Hz) a) if awake, the subject reports felling drowsy b) occurs during early sleep (stages 1 and 2) 4. Delta waves (below 3.5 Hz) a) large amplitude, slow waves b) occur in deep sleep c) can occur in coma 5. Note: also, non-cyclic EEG waves classified by pattern, e.g. spike and dome, spindle, etc. Credit:

31 The Signal: What do EEG and MEG measure? Credit: Excitatory postsynaptic potentials are generated at the apical dendritic tree of a cortical pyramidal cell and trigger the generation of electrical current. Large cortical pyramidal nerve cells are organized in macro-assemblies with their dendrites normally oriented to the local cortical surface. Functional networks made of these cortical cell assemblies and distributed at possibly mutliple brain locations are thus the putative main generators of MEG and EEG signals.

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