1) A brain-computer interface (BCI) enables communication without muscular activity by detecting brain signals. 2) BCI applications include communication assistance, controlling devices like wheelchairs or robotic arms, and entertainment like games. 3) BCI systems work by acquiring brain signals, preprocessing them, extracting features, classifying intentions, and translating them into device commands.

1. Brain Computer Interface
By
Mohammed AbdelAal

2. Definition
A Brain-Computer Interface (BCI) is a
communication system that does not require
any peripheral muscular activity. BCI systems
enable a subject to send commands to an
electronic device only by means of brain
activity. Such interfaces can be considered as
being the only way of communication for
people affected by a number of motor
disabilities.

3. Applications
● Communication
– Spelling Programs
● Motor Restoration
– Controlling Robot Arm
● Environment Control
– Controlling TV
– Controlling OS
● Locomotion
– Wheel Chair
● Entertainment
– Play Games
● Neuromarketing
– Emotion Recognition

4. Wheel Chair

5. Controlling Robot Arm

6. Spelling Programs

7. Entertainment

8. Neuromarketing

9. BCI Stages
● Signal acquisition: capture the brain signals and may
also perform noise reduction and artifact processing.
● Preprocessing: prepare the signals in a suitable form
for further processing.
● Feature extraction: map the brain signals onto a vector
containing effective and discriminant features.
● Classification: classify the signals taking the feature
vectors into account, and decipher the user’s intentions.
● Application interface: translate the classified signals
into meaningful commands for any connected device.

10. BCI Stages

11. Brain Anatomy and Functions
● Every part of the brain do a specific function

12. Signal Acquisition Stage
● Electrophysiological (direct): it is generated by electro-
chemical transmitters exchanging information between
the neurons. The neurons generate ionic currents which
flow within and across neuronal assemblies.
● Hemodynamic (Indirect): the blood releases glucose to
active neurons at a greater rate than in the area of
inactive neurons.

13. Electrophysiological
● Invasive: intracranial microarrays are implanted in the
gray mater, that involves significant health risks.
● Partially-invasive: micro-electrods are implanted inside
the skull but rest outside the brain rather than within the
grey matter. They produce better resolution signals than
non-invasive BCIs and have a lower risks than fully
invasive BCIs.
● Non-invasive: signals are captured by external devices
(eg. Placing electrodes on surface of scalp), so there are
no risks in this methods but it produce poor resolution
signals.

14. 1- Electroencephalography (EEG)
EEG is a non-invasive technique that records electrical
activity along the scalp. EEG measures voltage fluctuations
resulting from ionic current flows within the neurons of the
brain.
● Advantages:
– Non-invasive
– Lower costs
– Portable
– High temporal resolution
● Disadvantages:
– Low spatial resolution
– High noise ratio

15. 2- Magnetoencephalography (MEG)
MEG is a non-invasive imaging technique that registers the
brain’s magnetic activity by means of magnetic induction.
● Advantages:
– Non-invasive
– Better spatial resolution (vs
EEG)
– Lower noise ratio (vs EEG)
● Disadvantages:
– Too expensive
– Non-portable (too bulky)

16. 3- Electrocorticography (ECoG)
ECoG is a technique that measures electrical activity in
the cerebral cortex by means of electrodes placed directly
on the surface of the brain.
● Advantages:
– Higher temporal and
spatial resolution
– Higher amplitudes
– Lower noise ratio
● Disadvantages:
– Invasive

17. 4- Intracortical Neuron Recording
Intracortical neuron recording is a neuroimaging technique
that measures electrical activity inside the gray matter of
the brain.
● Advantages:
– Higher temporal and spatial resolution
● Disadvantages:
– Invasive
– Signal quality may be affected by the reaction of cerebral
tissue to the implanted recording micro-electrode and by
changes in the sensitivity of the micro-electrode.
– Periodic re calibrations of electrode sensitivity may be
necessary.

18. 5- Functional Magnetic Resonance Imaging (fMRI)
fMRI is a non-invasive neuroimaging technique which
detects changes in local cerebral blood volume, cerebral
blood flow and oxygenation levels during neural activation
by means of electromagnetic fields.
● Advantages:
– Non-invasive
– High spatial resolution
● Disadvantages:
– Too expensive
– Non-portable (too bulky)
– Low time resolution

19. 6- Near Infrared Spectroscopy (NIRS)
NIRS is an optical spectroscopy method that employs
infrared light to characterize non-invasively acquired
fluctuations in cerebral metabolism during neural activity.
● Advantages:
– Non-invasive
– portable
– Low cost
● Disadvantages:
– Low time resolution
– Low spatial resolution

20. Summary of neuroimaging methods
Neuroimaging
method
Activity
measured
Direct/
Indirect
Measurement
Temporal
resolution
Spatial resolution Risk Portability
EEG Electrical Direct ~0.05 s ~10 mm Non-invasive Portable
MEG Magnetic Direct ~0.05 s ~5 mm Non-invasive Non-portable
ECoG Electrical Direct ~0.003 s ~1 mm Invasive Portable
Intracortical
neuron
recording
Electrical Direct ~0.003 s
~0.5 mm (LFP)
~0.1 mm (MUA)
~0.05 mm (SUA)
Invasive Portable
fMRI Metabolic Indirect ~1 s ~1 mm Non-invasive Non-portable
NIRS Metabolic Indirect ~1 s ~5 mm Non-invasive Portable

21. EEG: 10-20 System
EEG signals are easily recorded in a non-invasive manner through
electrodes placed on the scalp, for which that reason it is by far the
most widespread recording modality. The electrodes placed over the
scalp are commonly based on the International 10–20 system, which
has been standardized by the American EEG Society.

22. EEG signals

23. EEG raw data

24. EEG rhythmic activity frequency bands
Band Frequency Normally
Delta (δ) up to 4 Hz babies, and adults in deep sleep state
Theta (θ) 4 – 7 Hz children, and adults in drowsy, meditative
or sleep states
Alpha (α) 8 – 12 Hz closing the eyes and the body is relaxed
Beta (β) 12 – 30 Hz thinking and concentration with no motor
activity
Gamma (γ) 30 – 100 Hz - maximal muscle contraction
- perception of both visual and auditory
stimuli
- affected by artifacts such as EMG or
EOG
Mu (μ) 7 – 13 Hz the body is physically at rest

25. Artifacts in BCIs
● Physiological artifacts are usually due to muscular,
ocular and heart activity, known as electromyography
(EMG), electrooculography (EOG), and
electrocardiography (ECG) artifacts respectively.
● Technical artifacts are mainly attributed to power-line
noises or changes in electrode impedances, which can
usually be avoided by proper filtering or shielding.

26. Preprocessing Stage
Data from brain signals can be quite high-
dimensional, and potentially full of artifacts. So,
the aim of this stage is to enhance the quality
of the recorded brain signal and to prepare it
for further processing stages.

27. Features Extraction Stage
The aim of this stage is to identify and
generate a set of representative features which
target specific aspects of brain activity.

28. Features Extraction Stage (Challenges)
● Noise and outliers: BCI features are noisy or contain outliers
because EEG signals have a poor signal-to-noise ratio.
● High dimensionality: In BCI systems, feature vectors are
often of high dimensionality.
● Time information: BCI features should contain time
information as brain activity patterns are generally related to
specific time variations of EEG.
● Non-stationarity: BCI features are non-stationary since EEG
signals may rapidly vary over time and more especially over
sessions.
● Small training sets: The training sets are relatively small,
since the training process is time consuming and demanding
for the subjects.

29. Features Extraction Stage (Methods)
● Principal Component Analysis (PCA)
● Independent Component Analysis (ICA)
● Recursive Feature Elimination (RFE)
● Auto-Regressive Components (AR)
● Matched Filtering (MF)
● Wavelet Transform (WT)
● Common Spatial Pattern (CSP)

30. Classification Stage
The aim of the classification step in a BCI
system is recognition of a user’s intentions on
the basis of a feature vector that characterizes
the brain activity provided by the feature step.

31. Classification Stage (Methods)
● Support Vector Machine (SVM)
● Linear Discriminant Analysis (LDA)
● Artificial Neural Network (ANN)
● K-Nearest Neighbor (k-NN)
● Hidden Markov Models (HMM)
● Bayesian Statistical
● Combinations of classifiers
– Boosting
– Voting
– Stacking

32. Types of BCIs
According to the nature of the signals used as input:
● Exogenous BCI: uses the neuron activity elicited in the
brain by an external stimulus such as visually or auditory
evoked potentials.
● Endogenous BCI: user can operate the BCI at free will
(like moving a cursor to any point in a two-dimensional
space)

33. Types of BCIs
According to the input data processing modality:
● Synchronous BCI: analyze brain signals during
predefined time windows, and any brain signal outside
the predefined window is ignored.
● Asynchronous BCI: continuously analyze brain signals
no matter when the user acts.

34. Types of BCIs
According to the analysis time:
● Offline analysis: brain signals are acquired then
analyzed in later time.
● Online analysis: EEG device is connected to BCI
system directly, and brain signals are acquired and
analyzed in the same time.

35. Datasets on the Internet
● BCI Competitions: II (2003), III (2004), and IV (2008)
● Multimedia Signal Processing Group (MMSPG)
● Neurotycho.org: a project that aims to share neural data
● Statistical Parametric Mapping (SPM): software for the
analysis of brain imaging data sequences
● LINI: share experimental data recorded and used by the
neuroimaging laboratory of UAM
● DEAP dataset: emotion recognition
● MAHNOB HCI Tagging Database: emotion & media tagging
● PhysioNet.org: EEG motor movement/imagery dataset
● Project BCI - EEG motor activity data set

36. BCI tools and frameworks
● EEGLAB: MATLAB toolbox
● BCILAB: MATLAB toolbox and EEGLAB plugin
● BioSig: open source software library for biomedical signal
processing (C++, Octave and MATLAB)
● BCI2000: general-purpose system for BCI research
● OpenViBE: software platform dedicated to designing,
testing and using BCI
● xBCI: platform for building an online BCI system

37. Commercial BCI systems
● Imec EEG
Handset
● Neurosky
Mindwave
headset
● Emotive EPOC
headset

38. Challenges to employing BCI control for
real-world tasks
● The information transfer rate provided by BCIs is too low for
natural interactive conversation, even for experienced subjects and
well-tuned BCI systems
● The high error rate further complicates the interaction
● BCI systems cannot be used autonomously by disabled people,
because BCI systems require assistants to apply electrodes or
signal-receiving devices before the disabled person can
communicate
● A BCI user may be able to turn the BCI system off by means of
brain activity as input, but usually cannot turn it back on again,
which is termed the “Midas touch” problem
● Handling BCI applications demands a high cognitive load that can
usually be achieved by users in quiet laboratory environment, but
not in the real world

39. References
● Luis Fernando Nicolas-Alonso and Jaime Gomez-Gil:
"Brain Computer Interfaces, a Review," Sensors, vol. 12,
no. 2, pp. 1211-1279, 2012.
● Fabien Lotte; M Congedo; A Lécuyer; F Lamarche and B
Arnaldi: "A Review of Classification Algorithms for EEG-
based Brain-Computer Interfaces," Journal of Neural
Engineering, vol. 4, no. 2, 2007.