This document discusses how a brain-computer interface (BCI) works. It explains that a BCI detects sensorimotor rhythms in the mu and beta frequency bands in the sensorimotor cortex using EEG or ECoG. It describes how motor imagery causes event-related desynchronization and synchronization of these rhythms. A BCI also requires training users to generate distinguishable brain patterns through motor imagery without external stimuli. Signal processing techniques are used to extract relevant features and classify patterns for device control.

1. HOW DOES A BCI WORK?
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2. 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)
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3. 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
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4. 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
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5. SIGNAL PROCESSING
- Simplifies signals: e.g. filtering
Preprocessing - Improves the signal-to-noise ratio (SNR)
- Extract features relevant to control parameters using
Feature
mathematical methods
Extraction - e.g. Amplitudes, Frequencies, Firing rates, ...
- Detection: detects specific patterns from ordinary
Detection/ patterns
Classification - Classification: classifies a given pattern to one of many
variables
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6. 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
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7. BCI PERFORMANCE
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8. 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 (?)
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9. CONFUSION MATRIX
Estimated As Estimated As
Positives Negatives
True Positives False Negatives Sensitivity =
Positives
(TP) (FN) TP / (TP + FN)
False Positives True Negatives Specificity =
Negatives
(FP) (TN) TN / (TN + FP)
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10. BCI APPLICATIONS
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11. 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
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12. 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
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13. SMART HOME CONTROL
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14. NAVIGATION IN VR
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15. 2D CURSOR CONTROL
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16. CONTROL LEVEL
Low-level High-level
Process-oriented Goal-oriented control
control
e.g. move a cursor to a
e.g. move a cursor in target #4
45 degree with a
speed of 2cm/s at this All the details are
time instant (for 50ms) managed by an
actuator
More specific
More general
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17. 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, a
smart tray with tactile surface and weight sensors, and a computing unit consist-
ing of three independent industrial PCs. FRIEND II can perform certain operations
Rolland III FRIEND II
Fig. 8 Semi-autonomous assistive devices developed at the University of Bremen that include
high level control: Intelligent wheelchair Rolland III, and rehabilitation robot FRIEND II (modified
from [35])
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22. 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, ...
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23. 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, ...)
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24. 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
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