Brain Computer Interfaces (BCI) Communication Training of brain activity
Brain Computer Interfaces (BCI) picture rights: Gerwin Schalk, Wadsworth Center, NY
Components of a Brain Computer Interface
Applications of BCI Communication devices for paralyzed people
Applications of BCI Prosthetics
Applications of BCI Robotics / remote device control (e. g. space flight, military)
Applications of BCI Gaming
Applications of BCI Learning how to control brain activity
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
VEP
P300
SCP
ERD/ERS
ERD/ERS
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
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.
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)
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
Non-invasive brain imaging Electroencephalography (EEG) Magnetoencephalography (MEG) Near Infrared Spectroscopy (NIRS) functional Magnetic Resonance Imaging
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
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 www.brainexplorer.org/neurological 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
Electroencephalogram (EEG) EEG: recording of the electrical activity of the brain from the scalp. History: Hans Berger (1873-1941) 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 (1842-1926) had proven the existence of similar potentials in dogs.
Patient of Hans Berger EEG device (mid 1900 s) EEG traces measured by Hans Berger
from Herbert Henri Jasper 1906-1999 Canadian psychologist, physiologist, anatomist, chemist and neurologist
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
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: http://http://www.acbrown.com/neuro/lectures/rass/nrrasselct.htm
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
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: http://http://www.acbrown.com/neuro/lectures/rass/nrrasselct.htm
The Signal: What do EEG and MEG measure? Credit: http://neuroimage.usc.edu/researchmegeegmodeling.html 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.