• Share
  • Email
  • Embed
  • Like
  • Save
  • Private Content
Inroduction to BCI

Inroduction to BCI



An introduction and overview on BCI systems.

An introduction and overview on BCI systems.



Total Views
Views on SlideShare
Embed Views



1 Embed 3

https://www.facebook.com 3


Upload Details

Uploaded via as OpenOffice

Usage Rights

© All Rights Reserved

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
Post Comment
Edit your comment

    Inroduction to BCI Inroduction to BCI Presentation Transcript

    • Brain Computer Interface By Mohammed AbdelAal
    • 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.
    • Applications ● Communication – ● ● Spelling Programs Motor Restoration – ● – ● Controlling Robot Arm Environment Control – Controlling TV – Controlling OS Locomotion Entertainment – ● Wheel Chair Play Games Neuromarketing
    • Wheel Chair
    • Controlling Robot Arm
    • Spelling Programs
    • Entertainment
    • 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.
    • BCI Stages
    • Brain Anatomy and Functions
    • Signal Acquisition Stage ● ● Electrophysiological (direct): it is generated by electrochemical 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.
    • Electrophysiological ● Invasive: micro-electrode arrays are implanted inside the skull. ● Non-invasive: electrodes are placed on surface of scalp.
    • 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: – – Lower costs – Portable – ● Non-invasive High temporal resolution Disadvantages: – Low spatial resolution – High noise ratio
    • 2- Magnetoencephalography (MEG) MEG is a non-invasive imaging technique that registers the brain’s magnetic activity by means of magnetic induction. ● Advantages: – – Better spatial resolution (vs EEG) – ● Non-invasive Lower noise ratio (vs EEG) Disadvantages: – Too expensive – Non-portable (too bulky)
    • 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 amplitudes – ● Higher temporal and spatial resolution Lower noise ratio Disadvantages: – Invasive
    • 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.
    • 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
    • 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: – – portable – ● Non-invasive Low cost Disadvantages: – Low time resolution – Low spatial resolution
    • Summary of neuroimaging methods Neuroimaging method Activity measured Direct/ Indirect Measurement EEG Electrical Direct ~0.05 s MEG Magnetic Direct ECoG Electrical Direct Temporal Spatial resolution resolution Risk Portability ~10 mm Non-invasive Portable ~0.05 s ~5 mm Non-invasive Non-portable ~0.003 s ~1 mm Invasive Portable Invasive Portable Intracortical neuron recording Electrical Direct ~0.003 s ~0.5 mm (LFP) ~0.1 mm (MUA) ~0.05 mm (SUA) fMRI Metabolic Indirect ~1 s ~1 mm Non-invasive Non-portable NIRS Metabolic Indirect ~1 s ~5 mm Non-invasive Portable
    • 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.
    • EEG: Signals
    • EEG rhythmic activity frequency bands Band Delta (δ) Theta (θ) Frequency up to 4 Hz 4 – 7 Hz Alpha (α) Beta (β) 8 – 12 Hz 12 – 30 Hz Gamma (γ) 30 – 100 Hz Mu (μ) 7 – 13 Hz Normally babies, and adults in deep sleep state children, and adults in drowsy, meditative or sleep states closing the eyes and the body is relaxed thinking and concentration with no motor activity - maximal muscle contraction - perception of both visual and auditory stimuli - affected by artifacts such as EMG or EOG the body is physically at rest
    • 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.
    • 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.
    • 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.
    • 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.
    • Features Extraction Stage (Methods) ● Principal Component Analysis (PCA) ● Independent Component Analysis (ICA) ● Auto-Regressive Components (AR) ● Matched Filtering (MF) ● Wavelet Transform (WT) ● Common Spatial Pattern (CSP)
    • 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.
    • Classification Stage (Methods) ● Bayesian Statistical ● Linear Discriminant Analysis (LDA) ● Support Vector Machine (SVM) ● K-Nearest Neighbor (k-NN) ● Hidden Markov Models (HMM) ● Artificial Neural Network (ANN) ● Combinations of classifiers – Boosting – Voting – Stacking
    • 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)
    • 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.
    • 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.
    • Datasets on the Internet ● BCI Competition II (2003) ● BCI Competition III (2004) ● BCI Competition IV (2008) ● HeadIT Studies ● EEGLAB software tutorial ● PhysioNet.org: EEG motor movement/imagery dataset ● DEAP dataset: EEG emotion recognition ● ANT company: example data from a spatial EEG attention experiment ● Multimedia Signal Processing Group (MMSPG) ● University Hospital of Bonn ● EEGbase project ● LINI (Repositories of medical signals and images)
    • 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
    • Commercial BCI systems (a) Emotive EPOC headset, (b) NeuroSky Mind Set, (c) MyndPlay Brainband, (d) PLX devices XWave headset, (e) OCZ Neural Impulse Actuator
    • 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 welltuned 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 signalreceiving 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