An Overview of BMIs. Luca Rossini. Workshop on Brain Machine Interfaces for Space Applications

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1 An Overview of BMIs Luca Rossini Workshop on Brain Machine Interfaces for Space Applications European Space Research and Technology Centre, European Space Agency Noordvijk, 30 th November 2009

2 Definition BMIs may be defined as any system devised to measure brain activity and, from it alone, to translate a person s intentions into commands to various devices. Devices can be: external (e.g., a computer) or internal (e.g., an implanted neuromuscular stimulation system)

3 BMI A new action pathway Mental activity focused on limb movement results in signals going to spinal cord, peripheral nerves, and finally to muscles. A BMI does not take advantage of any of these elements BUT the sole intention It does it on the basis of exclusively brain signals

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5 BMIs and the User s Ability Successful use of BMIs as developed to date requires that the user maintains his/her ability to learn and retain new abilities in controlling not the usual neuromuscular channels but the EEG pattern that is recognized as relevant by the BMI.

6 Movement Imagery Control of movement depends on the coordinated action of a large number of anatomically separate but interconnected areas of the brain and spinal cord that operate in parallel to determine the final movement outcome. There is no one point where the final command is represented apart from at its final convergence at the spinal motoneurone. In the case of voluntary movements of the hand and arm, however, a close second point of convergence is the primary motor cortex which is the source of much of the command for grasping and reaching movements. Recording activity here may give a good representation of the final movement that is intended by the brain. Other types of movement, i.e. those involving postural control of legs and trunk, are more likely to depend on integration of many inputs from other subcortical areas of the motor system that may be less accessible to noninvasive recording devices.

7 Evoked Potential Evoked potential: An evoked potential (or "evoked response") is an electrical potential recorded from a human or animal following presentation of a stimulus, as distinct from spontaneous potentials as detected by electroencephalograms or electromyograms.

8 Visual Evoked Potential (VEP) Caused by sensory stimulation of a subject's visual field. Commonly used visual stimuli are flashing lights, or checkerboards on a video screen that flicker between black on white to white on black (invert contrast). Visual evoked potentials are very useful in detecting blindness in patients that cannot communicate, such as babies or animals. If repeated stimulation of the visual field causes no changes in EEG potentials, then the subject's brain is probably not receiving any signals from his/her eyes. Visual evoked potentials are furthermore used in the investigation of basic functions of visual perception.

9 Event Related Potential (ERP) Measured brain response that is directly the result of a thought or perception (stereotyped electrophysiological response to an internal or external stimulus). While evoked potentials reflect the processing of the physical stimulus, event-related potentials are caused by the "higher" processes memory, expectation, attention, or changes in the mental state Event-related desynchronization (ERD) and event-related synchronization (ERS) describe transient changes in on-going oscillatory EEG activity. ERD means a relative power decrease and ERS means a power increase in specific spectral components over defined brain areas.

10 P300 Wave Event related potential (ERP) which can be recorded via electroencephalography (EEG) Positive deflection in voltage at a latency of roughly 300 ms in the EEG from the stimulus. The signal is typically measured most strongly by the electrodes covering the parietal lobe. The presence, magnitude, topography and time of this signal are often used as metrics of cognitive function in decision making processes.

11 Training-free The ability to generate an increased P300 wave in response to flashing of the chosen letter is independent from training. It only requires the user to be able to identify the chosen letter as the target of his/her attention and to ignore all the remaining letters. Still, some degree of learning will take place even in this scenario. P300 signal progressively adapts as the user is repeatedly exposed to the visual protocol.

12 Neurofeedback and Operate Conditioning Most clinical applications of BMI-research rest on the tradition of neurofeedback: presenting the user with realtime feedback on brainwave activity, as measured by sensors on the scalp, typically in the form of a video display, sound or vibration. The aim is to provide real-time information to the Central Nervous System (CNS) as to its current activity. An operant conditioning approach can be used to train users to control a cursor. It is sufficient to provide an appropriated feed-back (i.e., seeing the moving cursor) to let the brain progressively learn which components of its signals are relevant in controlling the BMI device.

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14 Brain Signals electroencephalographic signals (EEG); magnetoencephalographic signals (MEG); functional magnetic resonance imaging (fmri); positron emission tomography (PET); optical imaging (NIRS, near-infrared systems); implanted methods of recording electrical activity, e.g., via electrocorticograms (ECoG) and intracerebral electrodes.

15 Brain Signals EEG Recording and Measuring EEG system consist of a number of delicate electrodes, a set of differential amplifier (one for each channel) followed by filters, samplers, analog to digital converters, digital signal processors, and storing systems.

16 Near InfraRed Spectroscopy (NIRS) Brain function is equated to the amount of infrared light reflected by brain tissue. Assessment of brain function through the intact skull by detecting changes in blood hemoglobin concentrations associated with neural activity Portable Devices Invasive component in the strictest sense of the world: Significant near-infrared light is absorbed by the brain tissue, the long term effects of which have not been investigated yet. NIRS have a long response delay (i.e., ~10s or more, which is long compared to a few hundred ms in EEG-based systems) due to the slow nature of the monitored hemodynamic. Video 2.

17 PET, fmri and MEG PET requires the administration of radioactive substances to the human user fmri still requires very bulky and heavy Further, fmri, like NIRS, yields long response delays. Finally, MEG systems are very large as well, although they are non-invasive and yield better spatial resolution than EEG systems. Currently, only EEG has the two properties that are ideal for BMIs: non-invasiveness and portability.

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19 Feature Selection

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22 Device Commands The output from a BMI has no restrictions and currently includes robotic arm movements and wheel chair control Most BMIs to date present their output on a computer screen in the form of letters, icons, or arrows Some studies have investigated the possibility of controlling neuroprostheses and/or orthoses via a BMI

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24 BMI Types BMIs have been categorized in many ways in recent years. While not all BMI researchers use the same terminology, most subdivisions of BMIs fall under one of the following: Invasive vs. non-invasive Dependent vs. independent Spontaneous vs. evoked vs. event-related Synchronous vs. asynchronous These have so far been studied mostly in mutually exclusive fashion (e.g., either spontaneous or event-related signals), but future BMIs may combine some or all of the above in one system.

25 Conclusion The most advanced systems currently conceived aim to serve as general purpose computer interfaces, replacing the use of mouse and keyboards in dedicated environments (Krusienski and Wolpaw, 2009). Systems have been proposed for use with off-the-shelve software, and in particular in connection to virtual navigation systems or 3D virtual object manipulation software (Scherer et al., 2009). BMI systems have been optimized to interface with specific tools in order to reach the best possible performance for controlling domotic environments (Babiloni et al., 2009).