Ch.1 Introduction to Brain-Computer Interfacing

Overview Fairytales: translating thoughts into actions without acting physically. Recent BCI technologies made it possible! Aims of BIC –Restore sensory functions –Restore motor functions Application areas: neuroprostheses, computer games, etc. Technologies for monitoring brain activity: electroencephalography (EEG), invasive electrodes, magnetoencephalography (MEG), positron emission tomography (PET), functional magnetic resonance imaging (fMRI), optical imaging (fNIRS) Portable and practical: EEG, fNIRS, invasive electrodes Technological bottleneck: sensors Advanced signal processing and machine learning play a key role. Other issues: robustness, online adaptation to nonstationarities, sensor fusion, classifier and filter parameter tuning

Hans Berger early 1900s

Electromagnetic Fields of Neural Activity =65m at f = 100Hz in head tissues Sensors: average distance to neural generators = 0.15m  Quasistatic assumption : “ Neglect propagation of EM waves ”

 Hans Berger (1929)  Reasonably low-cost  Widely used in clinical practice + Neuropsychology research units EEG EEG

MicroMed Electrical Geodesics NeuroScan EEG EEG

Magnetoencephalography (MEG) SQUID (Superconducting QUantum Interference Device) Records magnetic fields produced by electrical activity in the brain. Study neuronal activity by means of magnetic fields. Temporal resolution in msec. Spatial resolution in mm, better than that of EEG.

 SQUID- James Zimmerman (1968), David Cohen (1972)  Cost = MRI  About 50 centers worldwide – still counting... MEG MEG

PET Positron Emission Tomography (PET) is a technique for measuring the concentrations of positron-emitting radioisotopes within the tissue of living subjects. These measurements are made outside of the living subjects. PET can be broken down into several steps: –label a selected compound with a positron- emitting radionuclide –administer this compound to the subject of study –image the distribution of the positron activity as a function of time by emission tomography The main positron- emitting radionuclides used in PET include Carbon-11, Nitrogen-13, Oxygen-15, and Fluorine- 18, with half-lives of 20 min, 10 min, 2 min, and 110 min respectively.

PET System

PET Images

fMRI

BOLD & fMR Images

fNIRS

NIRS Signals & Images

Approaches to BCI Control Two separate approaches, but mostly mixed of these two. 1)Learning to voluntarily regulate brain activity by means of neurofeedback and operant learning principles. 2)Machine learning procedures that enable the interference of the statistical signature of specific brain states or intentions within a calibration session.

The Biofeedback Approach –Voluntary control of the brain response –Biofeedback is a procedure to acquire voluntary control over the autonomous parameter of the brain. –Subjects receive visual, auditory, or tactile information about their cardiovascular activities (heart rate, blood pressure), temperature, skin conductance, muscular activity, electrical brain activities, blood oxygenation responses. –Subjects are asked to either increase or decrease the activity of interest. –By means of the feedback signal, participants receive continuous information about the alteration of the activity.

The Machine Learning Approach –Detection of the relevant brain signal –Training is moved from subjects to learning algorithm –Decoding algorithms are individually adapted to the users that perform the task. –Learning algorithms require examples from which they can infer the underlying statistical structure of the respective brain state. –Subjects are first required to repeatedly procedure a certain brain state during a calibration session. –Machine learning algorithms extract spatiotemporal blueprints of these brain activities which are used in subsequent feedback session. –Challenge is the trial-to-trial variability. Advanced machine learning techniques are essential.

Integration of the Two Approaches –In practice, BCIs will neither rely solely on feedback learning of uses nor only on machine learning approaches. –Co-adaptation of the learning use and algorithm is inevitable. –BCI illiterates: typically about 20% of the users are unable to successfully classify the brain activation patters. –Further research work is needed.

Clinical Target Groups Individuals in need of a BCI for motor control and communication Examples –Amyotrophic lateral sclerosis –Cervical spinal cord injury –Brain stem stroke

BCI for Healthy Subjects Recent interest as a HCI technology Addition to keyboard, computer mouse, speech or gesture recognition devices Ch. 23, 24, 25 for first examples Brain signals read in real-time on a single trial basis could provide direct access to human brain states which can be used to adapt HMI. Monitoring tasks such as alertness monitoring, cognitive workload, alertness, task involvement, emotion or concentration. Current bottlenecks: sensor prices, error rate, price of EEG. Need fashionable, cheap, contactless EEG caps.

Brain Pong (Dornhege et al, 2006)

Recording Methods, Paradigms, and Systems for BCI Current BCIs differ in how the neural activity of the brain is recorded, how subjects are trained, how the signals are translated into device commends, which application is provided to the user. Noninvasive Recording Methods for BCI –Recorded from the Scalp (EEG) –Magnetic Activity of the Brain (MEG) –Blood Oxygen Level Dependent (BOLD, fMRI) –Blood Flow (NIRS)

EEG-BCIs Slow Cortical Potential BCI (SCP-BCI) Sensorimotor Rhythm BCI (SMR-BCI) P300 BCI (P300-BCI) Steady-State Visual Evoked Potential BCI (SSVEP-BCI)

Generic Noninvasive BCI Setup

Slow Cortical Potentials (SCPs)

SCP-BCI SCP-BCI requires users to achieve voluntary regulation of brain activity. The traditional S1-S2 paradigm –Detection of contingent negative variation (CNV): a negative SCP shift seen after a warning stimulus (S1) two to ten seconds before an imperative stimulus (S2) that requires users to perform a task.