Bri503 lecture05

HOW DOES A BCI WORK?

Monday, March 19, 2012

MOTOR IMAGERY

Sensorimotor rhythms (SMR)

Detected in the sensorimotor area

Somatosensory cortex

Motor cortex

Mu rhythms: 8-12 Hz

Beta rhythms: 12-30Hz

ECoG can also use gamma rhythms (30-80Hz)


MOTOR IMAGERY

ERD (Event Related Desynchronization)

Planning and execution of hand/finger movements block (desynchronize) mu rhythms

ERS (Event Related Synchronization)

Inhibition of movements synchronizes mu rhythms

Foot and tongue movements enhance mu rhythms

ERD/ERS is generally observed for the contralateral movements


MOTOR IMAGERY

Does not need external stimuli

Does need long training

Users learn the best imagery

Closed-loop feedback plays an important role in learning


SIGNAL PROCESSING

Preprocessing

Feature Extraction

Detection/Classification

- Simplifies signals: e.g. filtering- Improves the signal-to-noise ratio (SNR)

- Extract features relevant to control parameters using mathematical methods- e.g. Amplitudes, Frequencies, Firing rates, ...

- Detection: detects specific patterns from ordinary patterns- Classification: classifies a given pattern to one of many variables


SIGNAL PROCESSING

Synchronous BCIs

Cue-paced

Operates a BCI with a fixed time frame

Asynchronous BCIs

Self-paced

Operates a BCI whenever the user wants


BCI PERFORMANCE


PERFORMANCE MEASURES

Classification Rate (= 1 - Error Rate)

# correct / total attempts

Letters / minute for a speller

Information transfer rate (ITR)

Depends on classification accuracy, performance time, and # classes

Bits / minute

Ball park: 30 bits/min ~ 90 bits/min (?)


CONFUSION MATRIX

Estimated As Positives

Estimated As Negatives

Positives True Positives (TP)

False Negatives (FN)

Sensitivity = TP / (TP + FN)

Negatives False Positives (FP)

True Negatives (TN)

Specificity = TN / (TN + FP)


BCI APPLICATIONS


BCI OUTPUT

Discrete output (Discrete State Variables)

Output = one of N possible values

e.g. “go” / “stop”

Continuous output (Continuous State Variables)

Output = continuous values, probably within a finite or infinite range

e.g. position on a 2D space


Brain–Computer Interfaces: A Gentle Introduction 17

Fig. 7 Examples of BCI applications. (a) Environmental control with a P300 BCI (see chapter“The First Commercial Brain–Computer Interface Environment”), (b) P300 Speller (see chapter“BCIs in the Laboratory and at Home: The Wadsworth Research Program”), (c) Phone numberdialling with an SSVEP BCI (see chapter “Practical Designs of Brain–Computer Interfaces Basedon the Modulation of EEG Rhythms”), (d) Computer game Pong for two players, E) Navigation ina virtual reality environment (see chapter “The Graz Brain–Computer Interface”), (f) Restorationof grasp function of paraplegic patients by BCI controlled functional electrical stimulation (seechapter “Non invasive BCIs for neuroprostheses control of the paralysed hand”)

letter and writing the message “water, please” or just “water”. Since this is a wishthe patient may have quite often, it would be useful to have a special symbol orcommand for this message. In this way, the patient can convey this particular mes-sage much faster, ideally with just one mental task. Many more short cuts mightallow other tasks, but these short cuts lack the flexibility of writing individual mes-sages. Therefore, an ideal BCI would allow a combination of simple commands to


SMART HOME CONTROL


NAVIGATION IN VR


2D CURSOR CONTROL


CONTROL LEVEL

Low-level

Process-oriented control

e.g. move a cursor in 45 degree with a speed of 2cm/s at this time instant (for 50ms)

More specific

High-level

Goal-oriented control

e.g. move a cursor to a target #4

All the details are managed by an actuator

More general


18 B. Graimann et al.

convey information flexibly and short cuts that allow specific, common, complexcommands.

In other words, the BCI should allow a combination of process-oriented (or low-level) control and goal-oriented (or high level) control [41, 42]. Low-level controlmeans the user has to manage all the intricate interactions involved in achieving atask or goal, such as spelling the individual letters for a message. In contrast, goal-oriented or high-level control means the users simply communicate their goal to theapplication. Such applications need to be sufficiently intelligent to autonomouslyperform all necessary sub-tasks to achieve the goal. In any interface, users shouldnot be required to control unnecessary low-level details of system operation.

This is especially important with BCIs. Allowing low-level control of awheelchair or robot arm, for example, would not only be slow and frustratingbut potentially dangerous. Figure 8 presents two such examples of very complexapplications.

The semi-autonomous wheelchair Rolland III can deal with different input mod-alities, such as low-level joystick control or high-level discrete control. Autonomousand semi-autonomous navigation is supported. The rehabilitation robot FRIEND II(Functional Robot Arm with User Friendly Interface for disabled People) is a semi-autonomous system designed to assist disabled people in activities of daily living.It is system based on a conventional wheelchair equipped with a stereo camera sys-tem, a robot arm with 7 degrees-of-freedom, a gripper with force/torque sensor, asmart tray with tactile surface and weight sensors, and a computing unit consist-ing of three independent industrial PCs. FRIEND II can perform certain operations

Fig. 8 Semi-autonomous assistive devices developed at the University of Bremen that includehigh level control: Intelligent wheelchair Rolland III, and rehabilitation robot FRIEND II (modifiedfrom [35])

Rolland III FRIEND II


CURRENT LIMITS OF BCIS

Reliability

Nonstationary brain signals

Sensitive to noise

Bandwidth

Low information transfer per second

Less competitive than conventional techniques

Healthy subjects: keyboards, mouse, speech, ...

Disabled subjects: eye-tracker, head mouse, ...


WILL NORMAL PEOPLE USE BCIS?

Yes if,

combined with intelligent systems

bandwidth continues to improve

used as an augmented interface

more accessible by general researchers (e.g. HCI fields, robotics fields, ...)


APPLICATION IN NEUROFEEDBACK

A BCI can be thought as the most advanced neurofeedback system

A BCI can be applied to neurorehabilitation fields where neurofeedback is necessary

ADHD

Autism

Epilepsy

Stroke


Brain–C

omputerInterfaces:A

Gentle

Introduction23

Fig.10

Brain–com

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