Michele Barsotti, profile picture
Uploaded byMichele Barsotti
PDF, PPTX3,262 views

Hs2014 bci mi

The document outlines a presentation on using brain-computer interfaces (BCIs) for upper limb neurorehabilitation. It discusses acquiring brain activity signals using electroencephalography (EEG), features of EEG signals, brain anatomy related to movement, motor imagery as a mental strategy for BCIs, and decoding movement intentions from EEG data analysis of event-related potentials and spectral perturbations. The goal is to control an exoskeleton through motor imagery-based BCI for rehabilitation applications.

Embed presentation

Download as PDF, PPTX
Haptics Symposium 2014 Haptics Symposium 2014 Exoskeleton control by Motor Imagery BCI for upper limb neurorehabilitation Michele Barsotti, Antonio Frisoli m.barsotti@sssup.it a.frisoli@sssup.it Brain Computer Interfaces & Haptics February 23-26, 2014, Houston Texas Haptics Symposium 2014
Haptics Symposium 2014 Haptics Symposium 2014 • Brain activity acquisition methods • General Brain Computer Interfaces • Brain anatomy • Movements EEG correlates • Decoding movement intention by analyzing EEG • Feedbacks for motor-imagery-BCI • Implementing a MI-BCI paradigm OUTLINE
Haptics Symposium 2014 Haptics Symposium 2014 BRAIN ACTIVITY ACQUISITION
Haptics Symposium 2014 Haptics Symposium 2014 ACQUIRING BRAIN ACTIVITY Temporal Resolution [s] SpatialResolution[cm] BASED ON THE BLOOD FLOW VARIATION BASED ON THE MAGNETIC- ELECTRICAL ACTIVITY
Haptics Symposium 2014 Haptics Symposium 2014 ACQUIRING BRAIN ACTIVITY Temporal Resolution [s] SpatialResolution[cm] ElectroCorticoGraphy (ECoG) Very good spatial and temporal resolution (firing of a single neuron) INVASIVE Surgical intervention
Haptics Symposium 2014 Haptics Symposium 2014 ACQUIRING BRAIN ACTIVITY Temporal Resolution [s] SpatialResolution[cm] • Most widely used strategy for BCI applications • Good Temporal Resolution • Several portable, cheap systems exist •Motion artifacts and interferences can be greatly reduced by employing active electrodes EEG is the record of electrical activity of brain by placing the electrodes on the scalp.
Haptics Symposium 2014 Haptics Symposium 2014 EEG SIGNALs FEATURES  AMPLITUDE RANGE:  Wake EEG:: Vpp = 100µV  Sleep EEG: Vpp = 300µV  FREQUENCY RANGE:  From 0.01 to 100 Hz  COMMON EEG ARTIFACTs:  Eye blinking (eye movement)  Muscular activity (EMG)  Ambient Noise + (50Hz-60Hz)  Electrodes Movement  Zero Mean
Haptics Symposium 2014 Haptics Symposium 2014 NATURAL EEG RHITMIC ACTIVITY Band [Hz] Normaly Location Gamma 32 + Displays during cross-modal sensory processing and short-term memory Somatosensory cortex Beta 16 - 32 active thinking, focus, hi alert, anxious both sides, symmetrical distribution, most evident frontally; low-amplitude waves Alpha 8 - 16 relaxed/reflecting closing the eyes inhibition control posterior regions of head, both sides, higher in amplitude on non-dominant side. Mu 8 - 12 Shows rest-state motor neurons Sensorimotor cortex Theta 4 - 8 higher in young children drowsiness in adults and teens idling Found in locations not related to task at hand Delta up to 4 adult slow-wave sleep Has been found during some continuous-attention tasks frontally in adults, posteriorly in children; high-amplitude waves
Haptics Symposium 2014 Haptics Symposium 2014 NATURAL EEG RHITMIC ACTIVITY Band [Hz] Normaly Location Gamma 32 + Displays during cross-modal sensory processing and short-term memory Somatosensory cortex Beta 16 - 32 active thinking, focus, hi alert, anxious both sides, symmetrical distribution, most evident frontally; low-amplitude waves Alpha 8 - 16 relaxed/reflecting closing the eyes inhibition control posterior regions of head, both sides, higher in amplitude on non-dominant side. Mu 8 - 12 Shows rest-state motor neurons Sensorimotor cortex Theta 4 - 8 higher in young children drowsiness in adults and teens idling Found in locations not related to task at hand Delta up to 4 adult slow-wave sleep Has been found during some continuous-attention tasks frontally in adults, posteriorly in children; high-amplitude waves
Haptics Symposium 2014 Haptics Symposium 2014 GENERAL BRAIN COMPUTER INTERFACES
Haptics Symposium 2014 Haptics Symposium 2014 GENERAL BCI FRAMEWORK SIGNAL PROCESSING FEATURES EXTRACTION 1 CSP FC3 FCZ FC4 C5 C3 C1 CZ C2 C4 C6 CP3 CPZ CP4 -0.2 -0.1 0 -0.2 -0.1 0 CSP 13 CSP FC3 FCZ FC4 C5 C3 C1 CZ C2 C4 C6 CP3 CPZ CP4VAR MAX VAR MIN SIGNAL CLASSIFICATION APPLICATION OUTPUT BIOFEEDBACK USER MENTAL STRATEGY BRAIN SIGNALs ACQUISITION
Haptics Symposium 2014 Haptics Symposium 2014 Feedback for subject training Machine learning  BCIs represent a set of techniques to allow direct control of a software or device via brain activity – without the need of a motor output  The most common BCI approach exploits voluntary modulation of EEG activity, although more invasive approaches have been explored  These techniques have successfully been employed to aid disabled patients  Recently BCIs have also been investigated as a rehabilitation tool GENERAL BCI FRAMEWORK
Haptics Symposium 2014 Haptics Symposium 2014 BCI CATEGORIES INVASIVE NON-INVASIVE Without penetrating the skalp, mostly EEG, rarely magnetoencephalogram (MEG) or functional magnetic resonance imaging fMRI - Several portable, cheap systems exist - Motion artifacts and interferences can be greatly reduced by employing active electrodes DEPENDING ON THE ACQUISITION SYSTEM Implanted sensors (electrode array, needle electrodes, electrocorticogram ECoG) -Control of 2-3 DoF, with good accuracy. -Implants have only been tested for months after surgery --Highly expensive
Haptics Symposium 2014 Haptics Symposium 2014 BCI Invasive Non invasive Single recording site Multiple recording sites ECoG EEG MEG fMRI Classification: signal acquisition
Haptics Symposium 2014 Haptics Symposium 2014  Insertion of arrays of microelectrodes in cortical tissue  Control of 2-3 DoF, with good accuracy.  Implants have only been tested for months after surgery  Highly expensive Hochberg et al., Nature, 2006 Invasive vs. non-invasive BCI  Invasive BCI  Non-invasive BCI  EEG systems range from low to high density (2 to 256 eletrodes)  Several portable, cheap systems exist  Motion artifacts and interferences can be greatly reduced by employing active electrodes
Haptics Symposium 2014 Haptics Symposium 2014 BCI CATEGORIES INDEPENDENT DEPENDENT A Dependent BCI does not use the brain’s normal output pathways to carry the message, but activity in these pathway is needed to generate the brain activity that does carry it. Independent from peripheral nerves and muscles, using only central nervous system (CNS) activity DEPENDING ON THE MENTAL STRATEGY
Haptics Symposium 2014 Haptics Symposium 2014 BCI CATEGORIES ENDOGENOUS EXOGENOUS Evoked Potentials: Users modulate brain responses to external stimuli SSVEP p300 Unstimulated Brain Signals: Users can voluntarily produce the required signals (Motor Imagery, Computational Task) DEPENDING ON THE MENTAL STRATEGY
Haptics Symposium 2014 Haptics Symposium 2014 BCI CATEGORIES ASYNCHRONOUS Commands can only be emitted synchronously with external pace. The system detects when the user wants to emit a command DEPENDING ON THE COMMAND-TIMING SYNCHRONOUS The differences in EEG response following different stimuli are used to discriminate what subjects want Subjects are asked to perform visual imagery tasks and the local changes in EEG power spectra are recorded
Haptics Symposium 2014 Haptics Symposium 2014  SSVEP  VEP  MOTOR IMAGERY  ERP (i.e.P300) BCI CATEGORIES - SUMMARY EXOGENOUSENDOGENOUSDEPENDENTINDEPENDENT
Haptics Symposium 2014 Haptics Symposium 2014 BRAIN ANATOMY & EEG MOVEMENTS CORRELATES
Haptics Symposium 2014 Haptics Symposium 2014 BRAIN ANATOMY [Martini, 2006]
Haptics Symposium 2014 Haptics Symposium 2014 The Primary Somatic Sensory Cortex (Parietal Lobe) and the Primary Motor Cortex (Temporal Lobe) are the most important regions for BCI research. I III IV V II BRAIN ANATOMY: THE CEREBRAL CORTEX
Haptics Symposium 2014 Haptics Symposium 2014 The Primary Somatic Sensory Cortex (Parietal Lobe) and the Primary Motor Cortex (Temporal Lobe) are the most important regions for BCI research. I III IV V II BRAIN ANATOMY: THE CEREBRAL CORTEX M1 S1
Haptics Symposium 2014 Haptics Symposium 2014 The Primary Somatic Sensory Cortex (Parietal Lobe) and the Primary Motor Cortex (Temporal Lobe) are the most important regions for BCI research. I III IV V II BRAIN ANATOMY: THE CEREBRAL CORTEX M1 S1
Haptics Symposium 2014 Haptics Symposium 2014 TYPES OF MOVEMENT Three types of movements may occur in respect of to ascending and descending signals via different pathways and at different levels: • Reflexes movement : are performed subconsciously and can occur at an exclusively spinal level • Rhythmic movement: stereotyped action involving repetitions of the same movements The control is at the spinal level without involvement of higher cortical control • Voluntary movement: usually goal directed and therefore fully conscious. It arises in the motor cortex and is executed by the spinal cord.
Haptics Symposium 2014 Haptics Symposium 2014 TYPES OF MOVEMENT Three types of movements may occur in respect of to ascending and descending signals via different pathways and at different levels: • Reflexes movement : are performed subconsciously and can occur at an exclusively spinal level • Rhythmic movement: stereotyped action involving repetitions of the same movements The control is at the spinal level without involvement of higher cortical control • Voluntary movement: usually goal directed and therefore fully conscious. It arises in the motor cortex and is executed by the spinal cord. When a voluntary movement is started, neurons in the M1 send commands to upper and lower motor neurons. The M1 needs to be stimulated by neurons from the premotor cortex and the supplementary motor area (SMA), which support and coordinate the M1, in order to initiate a voluntary movement
Haptics Symposium 2014 Haptics Symposium 2014 Motor imagery is a mental process by which an individual rehearses or simulates a given action. MOTOR IMAGERY Performing motor imagery or attempting a movement (i.e. for patients) influences the brain activity as the voluntary movements do.
Haptics Symposium 2014 Haptics Symposium 2014 Why MOTOR IMAGERY is suitable for BCI? • No need of external stimulus (it could be asynchronous) • Not depend in any way on the brain’s normal output/input pathways (independent) • Possibility to provide different commands depending on which body part is evolved in the simulated action • Mental practice of motor actions via BCI training affect neuro- rehabilitation in a positive way. • the power in μ (8-12 Hz) and β (12-24Hz) EEG rhythms are affected by motor imagery: Event Related Spectral Perturbation (ERSP) • Users learn to perform motor imagery tasks • Can be employed event if the motor areas are impaired • Works mostly for digital control, has a fast response
Haptics Symposium 2014 Haptics Symposium 2014 DECODING MOVEMENT INTENTIONS BY ANALIZING EEG
Haptics Symposium 2014 Haptics Symposium 2014  Event Related Potential (ERP):  - Repeatedly present discrete stimulus, average raw EEG responses across presentations. Characteristic feature (eg. P300)  Event Related Spectral Perturbation (ERSP):  Frequency band changes - Average spectral features across presentation. - Characteristic suppression/increase in power (ERD/ERS: Event Related De-Synchronization). EEG PHENOMENAL USABLE FOR BCI  Event Related Spectral Perturbation (ERSP) and Event Related Potential ERP are the measured brain response that are the direct result of a specific sensory, cognitive, or motor event.
Haptics Symposium 2014 Haptics Symposium 2014 time Frequency[Hz] epochtimechannel  X epochfrequencytimechannel  X AVERAGING time Amplitude[µV] ERP ERSP • The ERPs and ERSP should be extracted from the background noise mediating many recordings (Epochs or Trials)
Haptics Symposium 2014 Haptics Symposium 2014 Amplitude[µV] EEG background noise ~ 1/sqrt(N) Costant Signal ERP Repetition (N) Post-Stimulus EEG Costant Signal Background Noise average ERP average Signal average Noise AVERAGING THEORY S/N ratio increases as a function of the square root of the number of trials.
Haptics Symposium 2014 Haptics Symposium 2014 MOTOR IMAGERY CORRELATES IN EEG Performing (or imagining) a motor action influences the EEG with two main phenomena:
Haptics Symposium 2014 Haptics Symposium 2014 SLOW CORTICAL POTENTIALS [Kornhuber and Deecke (1965) ] • Know as BereitschaftPotential (readiness potential) or Movement Related Cortical Potentials (MRCPs). • Slow oscillations preceding the movement • Localized over the supplementary motor area (SMA) • Steps for MRCP detection • Spatial filter, • LP frequency filter • Template extraction from the training data • matching with the ongoing eeg MOTOR IMAGERY CORRELATES IN EEG •Frequency close to the DC -> very challenging to detect in single trial
Haptics Symposium 2014 Haptics Symposium 2014 SENSORIMOTOR RHYTHMS [Pfurtscheller and Lopes da Silva, (1999)] • the power in μ (8-12 Hz) and β (12-24 Hz) EEG rhythms are affected by motor imagery. •Know also as Event Related De/Synchronization (ERD,ERS) •Steps for MRCP detection • Spatial filter, • Band Pass frequency filter • Feature extraction • LDA classifier • High average classification accuracy (>80%) MOTOR IMAGERY CORRELATES IN EEG
Haptics Symposium 2014 Haptics Symposium 2014 ERD extraction: example with motor imagery Collecting Trials from a specific electrode Bandpass on the specific frequency Squaring Signals Averaging over Trials Smoothing [Pfurtscheller and Lopes da Silva, (1999)]
Haptics Symposium 2014 Haptics Symposium 2014 β ERD 13-30 Hz µ ERD 8-12 Hz Event Related DeSynchronization ERD Motor Imagery of right hand movement EVENT RELATED SPECTRAL PERTURBATION SENSORIMOTOR RHYTHMS
Haptics Symposium 2014 Haptics Symposium 2014 MOTOR IMAGERY: SIGLE TRIAL DETECTION The important features of the motor imagery are:  The frequency band.  The spatial localization A priori knowledgment:  The frequency band are mu (8 -13Hz) and beta (15-30 Hz).  The spatial localization is over the sensory motor Very high intersubject variability! Need of optimized spatial filters
Haptics Symposium 2014 Haptics Symposium 2014 The aim of spatial filtering is to improve the signal-to-noise ratio by creating a virtual channel which is a (linear, in the following cases) combination of the input channels of the filter. A spatial filters can optimize the data extracted from an high number of electrodes reducing the dimension of the features'space to only few significant dimensions. N-channel input (ex. 16 ch) 1-optimized channel output 1 CSP FC3 FCZ FC4 C5 C3 C1 CZ C2 C4 C6 CP3 CPZ CP4 C SPATIAL FILTERING y(t) = a*ch1(t) + b*ch2(t) ....
Haptics Symposium 2014 Haptics Symposium 2014 Optimized Spatial filter: Common Spatial Pattern – CSP VAR MAX VAR MIN VAR MIN RAW CHANNELS FIRST AND LAST CSP FILTER PROJECTED DATA REST MOVERESTMOVE Trial i Trial i+1 Trial i Trial i+1 VAR MAX [Pfurtscheller 1999] Common Spatial Pattern (CSP) is a supervised spatial filtering method for two-class discrimination problems, which finds directions that maximize variance for one class and at the same time minimize variance for the other class. 1 CSP FC3 FCZ FC4 C5 C3 C1 CZ C2 C4 C6 CP3 CPZ CP4 -0.2 -0.1 0 -0.2 -0.1 0 CSP 13 CSP FC3 FCZ FC4 C5 C3 C1 CZ C2 C4 C6 CP3 CPZ CP4 1 CSP FC3 FCZ FC4 C5 C3 C1 CZ C2 C4 C6 CP3 CPZ CP4 -0.2 -0.1 0 -0.2 -0.1 0 CSP 13 CSP FC3 FCZ FC4 C5 C3 C1 CZ C2 C4 C6 CP3 CPZ CP4
Haptics Symposium 2014 Haptics Symposium 2014 WHITENING MATRIX TRIALS CLASS A TRIALS CLASS B COVARIANCE CLASS A COVARIANCE CLASS B i Ti A i A Ti A i A A XXtrace XX R )(  i Ti B i B Ti B i B B XXtrace XX R )(  BAc RRR  T CCCC UUR  T CC UW 1  T AA WWRS  T BB WWRS  T AA UUS  T BB UUS  IBA  WUP T  PXZ  COMPOSITE COVARIANCE Transformed Covariance A Transformed Covariance B EIGENVECTOR PROJECTION MATRIX EIGENVALUES Common Spatial Pattern – Algorithms
Haptics Symposium 2014 Haptics Symposium 2014 • The scalp-plot of the Common Spatial Pattern can be also used to give a physiological interpretation of the data Common Spatial Pattern: advantages • Since variance of band-pass filtered signals is equal to band-power, CSP filters are well suited to discriminate mental states characterized by spectral perturbations (ERD and motor imagery based BCIs).
Haptics Symposium 2014 Haptics Symposium 2014 The log-scaled band-power values in the mu and beta band of the resulting two projected channels, can be used as a two- dimensional feature of the brain activity. Classification is performed using a linear discriminant classifier (LDA) or a support vector machine (SVM) CLASSIFICATION
Haptics Symposium 2014 Haptics Symposium 2014 CSP VARIANTS  CSP – Pfurtscheller 1998  FWM – Liu 2010  CSSSP – Blankertz 2006  CSSP – Lemm 2005  SPEC-CSP – Tomioka 2006  SB-CSP – Novi 2008  FB-CSP – Ang 2008  dCSP – Wang 2010  SSCSP – Arvaneh 2011  I-CSP – Blankertz 2008 With Frequency Optimization Furhter Spatial Optimization
Haptics Symposium 2014 Haptics Symposium 2014 FEEDBACKs for motor imagery - BCI
Haptics Symposium 2014 Haptics Symposium 2014 FEEDBACK FOR MOTOR IMAGERY The biofeedback provided as a response to the mental activity can improves the usability of motor imagery BCI. The congruency of the provided feedback with the mental task is expected to ease the performance of motor imagery. Game Illusion Virtual reality Exoskeleton VISUAL PROPRIOCEPTIVE
Haptics Symposium 2014 Haptics Symposium 2014 motor imagery – BCI in neurological rehabilitation
Haptics Symposium 2014 Haptics Symposium 2014 BCI in neurological rehabilitation Daly & Wolpaw, Lancet, 2008
Haptics Symposium 2014 Haptics Symposium 2014 Daly & Wolpaw, Lancet, 2008Goal: The subject should be able to control muscle activity through brain activity BCI in neurological rehabilitation
Haptics Symposium 2014 Haptics Symposium 2014 Daly & Wolpaw, Lancet, 2008 Strategy 1: Train subjects to modulate brain activity via visualization and voluntary control of relevant features BCI in neurological rehabilitation
Haptics Symposium 2014 Haptics Symposium 2014 Daly & Wolpaw, Lancet, 2008 Strategy 2: Train subjects by using brain activity to aid motion with assistive devices BCI in neurological rehabilitation
Haptics Symposium 2014 Haptics Symposium 2014 A new multimodal architecture for gaze-independent brain–computer interface (BCI)-driven control of a robotic upper limb exoskeleton for stroke rehabilitation to provide active assistance in the execution of reaching tasks in a real setting scenario. Object 1 Object 2 Work plane Kinect Eye tracker BC I
Haptics Symposium 2014 Haptics Symposium 2014 VAR MIN OPTIMAL CHANNELS MOVE VAR MAX REST MOVE REST VAR MAX VAR MIN ORIGINAL CHANNELS CSP FILTERS SVM CLASSIFIER TRAINING PHASE VISUAL CONDITION ROBOT CONDITION  Involving the BCI module only and the visual feedback of a virtual arm controlled through motor  The subject performed a test session with the complete system: Kinect – EyeTracker – BCI – ArmExos
Haptics Symposium 2014 Haptics Symposium 2014 BCI-REHABILITATION PROTOCOL BCI EEG acquisition & processing L-EXOS proprioceptive feedback MONITOR visual feedback • TRAINING PHASE: visual and proprioceptive feedback are provided accordingly to the task • EXERCISE PHASE: the real-time classification output of the BCI was used for driving the proprioceptive and visual feedback ALL PATIENTS WERE ABLE TO CONTROL THE BCI SYSTEM AFTER THE FIRST TWO SESSION 5 right hemiparetic stroke patients enrolled SESSION STRUCTURE: • MOVEMENT: the patient have to perform motor imagery of his impaired arm • REST: the patient have to hold a resting mental state TASKs REQUIRED:
Haptics Symposium 2014 Haptics Symposium 2014 BCI paradigm based on Motor Imagery
Haptics Symposium 2014 Haptics Symposium 2014 EEG acquisition Signal filtering and conditioning Features extraction Features classification Online operations: User Offline BCI training Frequency bands and artifact removal parameters Spatial Filter parameters Classifier weights Real-time feedback MOTOR IMAGERY BCI: WORKFLOW
Haptics Symposium 2014 Haptics Symposium 2014 EEG CONFIGURATION  EEG channels: minimal configuration Frontal ground electrode Reference ear lobe electrode Electrodes covering the motor cortex Electrode for eye-blink detection and removal  Feature extraction The power in the mu (8-12 Hz) and beta (16-24 Hz) bands is computed over 500 ms windows.
Haptics Symposium 2014 Haptics Symposium 2014 TRAINING PHASE Training paradigm Subjects are asked to perform several motor imagery trials. 1. Feature classification Acquired data is classified into two or more classes via machine learning techniques, to optimize feature classification 2. Subject training The subject is trained again with the output of the feature classifier as a feedback signal, in order to optimize its motion imagery TRIAL STRUCTURE
Haptics Symposium 2014 Haptics Symposium 2014 DATA PROCESSING TRAINING • Import data with the channel location • Subdivide data into epochs for the two classes • Remove artifactuated epochs • Train the Common Spatial filter • Extract Features • Train the classifier it is possible to predict the BCI performance by a visual inspection of both the time-frequency plot of the CSP-projected channels and the features plot
Haptics Symposium 2014 Haptics Symposium 2014 DATA PROCESSING: Visual Inspection
Haptics Symposium 2014 Haptics Symposium 2014 Time Frequency plot raw channels Time [ms] Frequency[Hz] C3 -2000 0 2000 4000 10 20 30 -2 0 2 Time [ms] Frequency[Hz] CZ -2000 0 2000 4000 10 20 30 -2 0 2 -2 0 2 CHANNELS ERD MAPS - MOVE Time [ms] Frequency[Hz] C4 -2000 0 2000 4000 10 20 30 Time [ms] Frequency[Hz] C3 -2000 0 2000 4000 10 20 30 -2 -1 0 1 2 Time [ms] Frequency[Hz] CZ -2000 0 2000 4000 10 20 30 -2 -1 0 1 2 -2 -1 0 1 2 CHANNELS ERD MAPS - REST Time [ms] Frequency[Hz] C4 -2000 0 2000 4000 10 20 30 Click on electrodes to toggle name/number Click on electrodes to toggle name/number Click on electrodes to toggle name/number
Haptics Symposium 2014 Haptics Symposium 2014 Time Frequency plot CSP projected channels Time [ms] Frequency[Hz] MOVE - First CSP -2000 -1000 0 1000 2000 3000 4000 10 20 30 -5 0 5 Time [ms] Frequency[Hz] MOVE - Last CSP -2000 -1000 0 1000 2000 3000 4000 10 20 30 -2 0 2 Time [ms] Frequency[Hz] REST - First CSP -2000 -1000 0 1000 2000 3000 4000 10 20 30 -2 0 2 -2 0 2 Time [ms] Frequency[Hz] REST - Last CSP -2000 -1000 0 1000 2000 3000 4000 10 20 30 FC3 FCZ FC4 C5 C3 C1 CZ C2 C4 C6 CP3 CPZ CP4 FC3 FCZ FC4 C5 C3 C1 CZ C2 C4 C6 CP3 CPZ CP4 First CSP First CSP Last CSP Last CSP REST trials MOVE trials
Haptics Symposium 2014 Haptics Symposium 2014 0 1000 2000 3000 0 20 40 60 80 100 Time [ms] CorrectRate[%] CLASSIFIER PERFORMANCE 'Rest' ->89.65% 'Move'->99.95% 'Total' ->95.10% 1.8 2 2.2 2.4 2.6 2 2.5 3 3.5 1st CSP - Log Features 2ndCSP-LogFeatures 0 1 Support Vectors PREDICTING RESULTS Analysis of the BCI output calculated with parameters extracted from the same dataset Plot of each trial in the features space
Haptics Symposium 2014 Haptics Symposium 2014 MODEL EEG amp CSP and LDA weights Spatial Filtering Features Extraction Classifier User Interface
Haptics Symposium 2014 Haptics Symposium 2014 SHOWING RESULTs [Frisoli et al. 2012]
Haptics Symposium 2014 Haptics Symposium 2014 email: a.frisoli@sssup.it m.barsotti@sssup.it thank you!

More Related Content

PPTX
Basic Theories of Neurotechnology
byHayato Waki
 
PPTX
Brain computer interface
bySharat045
 
PPTX
BCI
byKhushboo Shaukat
 
PPTX
Eeg presentation
bySquishey Bruns
 
PDF
Inroduction to BCI
byMohamed Abdelaal
 
PPTX
Senior Project Student's Presentation on Design of EMG Signal Recording System
byMd Kafiul Islam
 
PPT
EEG
byLokender Yadav
 
PPTX
Gesture recognition
byPrachiWadekar
 
Basic Theories of Neurotechnology
byHayato Waki
 
Brain computer interface
bySharat045
 
BCI
byKhushboo Shaukat
 
Eeg presentation
bySquishey Bruns
 
Inroduction to BCI
byMohamed Abdelaal
 
Senior Project Student's Presentation on Design of EMG Signal Recording System
byMd Kafiul Islam
 
EEG
byLokender Yadav
 
Gesture recognition
byPrachiWadekar
 

What's hot

PPTX
BRAIN COMPUTER INTERFACE(BCI)
byjosnapv
 
PPT
Brain computer interface
byPresentaionslive.blogspot.com
 
PPTX
BRAIN COMPUTER INTERFACE (BCI)
byAyush Sharma
 
PPTX
Brain Computer Interfaces(BCI)
byDr. Uday Saikia
 
PPTX
EEG Based BCI Applications with Deep Learning
byRiddhi Jain
 
PPTX
Brain Machine Interface
byRehan Fazal
 
PPTX
Hand gesture recognition
byBhawana Singh
 
PPTX
Brain machine interface
byAnup72
 
PPTX
BRAIN GATE (in nutshell)
bydarshann
 
PPTX
MagnetoEncephaloGraphy- By Anuj Malik
byAEC PRIVATE LIMITED.
 
PPTX
Evm electronic voting machine
byEdgefxkits & Solutions
 
PPTX
Electronic skin
byAbinashranaSingh
 
PPT
Voice and touchscreen operated wheelchair ppt
bySyed Saleem Ahmed
 
PDF
Biomedical signal modeling
byRoland Silvestre
 
PDF
Biorobotics
byLe Scienze Web News
 
PDF
A Design Of Omni-Directional Mobile Robot Based On Mecanum Wheels
byIJRESJOURNAL
 
PPTX
enhancement of ecg signal using wavelet transfform
byU Reshmi
 
PDF
Cohen v1
byselvijegan
 
PDF
Artificial Vision & Neuroprosthetics
byJeanna Leaves
 
PPT
Embedded System Basics
byDr M Muruganandam Masilamani
 
BRAIN COMPUTER INTERFACE(BCI)
byjosnapv
 
Brain computer interface
byPresentaionslive.blogspot.com
 
BRAIN COMPUTER INTERFACE (BCI)
byAyush Sharma
 
Brain Computer Interfaces(BCI)
byDr. Uday Saikia
 
EEG Based BCI Applications with Deep Learning
byRiddhi Jain
 
Brain Machine Interface
byRehan Fazal
 
Hand gesture recognition
byBhawana Singh
 
Brain machine interface
byAnup72
 
BRAIN GATE (in nutshell)
bydarshann
 
MagnetoEncephaloGraphy- By Anuj Malik
byAEC PRIVATE LIMITED.
 
Evm electronic voting machine
byEdgefxkits & Solutions
 
Electronic skin
byAbinashranaSingh
 
Voice and touchscreen operated wheelchair ppt
bySyed Saleem Ahmed
 
Biomedical signal modeling
byRoland Silvestre
 
Biorobotics
byLe Scienze Web News
 
A Design Of Omni-Directional Mobile Robot Based On Mecanum Wheels
byIJRESJOURNAL
 
enhancement of ecg signal using wavelet transfform
byU Reshmi
 
Cohen v1
byselvijegan
 
Artificial Vision & Neuroprosthetics
byJeanna Leaves
 
Embedded System Basics
byDr M Muruganandam Masilamani
 

Viewers also liked

PPTX
Brain Computer Interface (BCI) - seminar PPT
bySHAMJITH KM
 
PDF
EEG based Motor Imagery Classification using SVM and MLP
byDr. Rajdeep Chatterjee
 
PPTX
Final thesis presentation on bci
byRedwan Islam
 
PPT
EEG - Montages, Equipment and Basic Physics
byRahul Kumar
 
PPT
V isual evoked potentials
byNeurology resident slides
 
PDF
Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...
byTatsuya Yokota
 
PPTX
Eeg wave pattern
byRoopchand Ps
 
PPTX
EDX: Evoked potentilas
byShahram Sadeqi
 
PPTX
EEG Epilepsy
byZY The Ripper
 
PDF
TEDx Manchester: AI & The Future of Work
byVolker Hirsch
 
PDF
Support Vector Machines
byadil raja
 
PPTX
Passo a passo para baixar slides
byDênia Cavalcante
 
PDF
Make Me Think. A Brief Introduction to BCI.
byNikita Lukianets
 
PPTX
Real-Time Streaming Data Analysis with HTM
byNumenta
 
PDF
Conferencia del Dr. Niels Birbaumer en #Perspectives2015
byTECNALIA Research & Innovation
 
PPTX
What the Brain says about Machine Intelligence
byNumenta
 
DOCX
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of ...
byIEEEBEBTECHSTUDENTPROJECTS
 
PDF
"Kate, a Platform for Machine Intelligence" by Wayne Imaino, IBM Research
bydiannepatricia
 
PPTX
Dataset ii ia
byAshwani Jha
 
PDF
When Classifier Selection meets Information Theory: A Unifying View
byMohamed Farouk
 
Brain Computer Interface (BCI) - seminar PPT
bySHAMJITH KM
 
EEG based Motor Imagery Classification using SVM and MLP
byDr. Rajdeep Chatterjee
 
Final thesis presentation on bci
byRedwan Islam
 
EEG - Montages, Equipment and Basic Physics
byRahul Kumar
 
V isual evoked potentials
byNeurology resident slides
 
Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...
byTatsuya Yokota
 
Eeg wave pattern
byRoopchand Ps
 
EDX: Evoked potentilas
byShahram Sadeqi
 
EEG Epilepsy
byZY The Ripper
 
TEDx Manchester: AI & The Future of Work
byVolker Hirsch
 
Support Vector Machines
byadil raja
 
Passo a passo para baixar slides
byDênia Cavalcante
 
Make Me Think. A Brief Introduction to BCI.
byNikita Lukianets
 
Real-Time Streaming Data Analysis with HTM
byNumenta
 
Conferencia del Dr. Niels Birbaumer en #Perspectives2015
byTECNALIA Research & Innovation
 
What the Brain says about Machine Intelligence
byNumenta
 
IEEE 2014 MATLAB IMAGE PROCESSING PROJECTS Independent component ensemble of ...
byIEEEBEBTECHSTUDENTPROJECTS
 
"Kate, a Platform for Machine Intelligence" by Wayne Imaino, IBM Research
bydiannepatricia
 
Dataset ii ia
byAshwani Jha
 
When Classifier Selection meets Information Theory: A Unifying View
byMohamed Farouk
 

Similar to Hs2014 bci mi

PPTX
Seminar
byPriyanshi Pandey
 
PPTX
Brain computer interface
byNeurologyKota
 
PPTX
Brain Computer Interface ppt
byAman Kumar
 
PPTX
1. INTRODUCTION TO BCI-How BCIwork.pptx
byjayanthisree1
 
PPTX
Brain Computer Interface (BCI) Presentation
byraju1024manna
 
PPTX
Artificial Intelligence and Brain-Computer Interfaces (BCI) Using EEG
bySaran A K
 
PPTX
INTRODUCTION TO BCI-Brain Computer and Interface.pptx
byGopinathD17
 
PPT
RECENT ADVANCES IN BRAIN-COMPUTER INTERFACES
byTouradj Ebrahimi
 
PPTX
BRAIN COMPUTER INTERFACE
byRushi Prajapati
 
PDF
Brain–machine interfaces: past, present and future - WWW.OLOSCIENCE.COM
byFausto Intilla
 
PPTX
Brain-Computer-Interfaces-A-Technological-Mind-Bridge.pptx
byShobySunny2
 
PPTX
brain computer interface system prentation
bywebdevtalks9
 
PDF
UNIT 5_7 Brain-Computer Interfaces (BCI).pdf
byPrabhatMishraAbvp
 
PPTX
Brain-Computer Interface
byErandi Praboda
 
PPTX
brain computer interface to control windows os presentation
bydigil vinoy
 
PPT
Brain-Computer Interface: Rebuilding radically new output of the Human Brain
byArina Kochetova
 
PPTX
Seminar 1
byOmar Tohamy
 
PDF
Rob livesley intro to bci
byataccessgroup
 
PPTX
Brain measure interface.pptx
byVigneshwariNatarajan2
 
PDF
A Review on Motor Imagery Signal Classification for BCI
byCSCJournals
 
Seminar
byPriyanshi Pandey
 
Brain computer interface
byNeurologyKota
 
Brain Computer Interface ppt
byAman Kumar
 
1. INTRODUCTION TO BCI-How BCIwork.pptx
byjayanthisree1
 
Brain Computer Interface (BCI) Presentation
byraju1024manna
 
Artificial Intelligence and Brain-Computer Interfaces (BCI) Using EEG
bySaran A K
 
INTRODUCTION TO BCI-Brain Computer and Interface.pptx
byGopinathD17
 
RECENT ADVANCES IN BRAIN-COMPUTER INTERFACES
byTouradj Ebrahimi
 
BRAIN COMPUTER INTERFACE
byRushi Prajapati
 
Brain–machine interfaces: past, present and future - WWW.OLOSCIENCE.COM
byFausto Intilla
 
Brain-Computer-Interfaces-A-Technological-Mind-Bridge.pptx
byShobySunny2
 
brain computer interface system prentation
bywebdevtalks9
 
UNIT 5_7 Brain-Computer Interfaces (BCI).pdf
byPrabhatMishraAbvp
 
Brain-Computer Interface
byErandi Praboda
 
brain computer interface to control windows os presentation
bydigil vinoy
 
Brain-Computer Interface: Rebuilding radically new output of the Human Brain
byArina Kochetova
 
Seminar 1
byOmar Tohamy
 
Rob livesley intro to bci
byataccessgroup
 
Brain measure interface.pptx
byVigneshwariNatarajan2
 
A Review on Motor Imagery Signal Classification for BCI
byCSCJournals
 

Recently uploaded

PPTX
Qui aut sit esse qu Qui aut sit esse quQui aut sit esse quQui aut sit esse qu...
bysheikhshahib123
 
PPT
new Introduction to PACS.ppt Picture Archieving and communication and medicine
byssuserf3d7af
 
PPTX
DEEP LEARNING UNIT 2 all about deep learning
bypavanforsocials
 
PPTX
Technology Stack Evolution: Analyzing Programming Language & Database Trends ...
byzouhairfezzaz54
 
PPT
Theory Of Computation-viruses Theory Of Computation-viruses
bySandeepGupta229023
 
PPTX
IEC60079-10-1 -Annex F -Hazardous Area Classification -HAC-Annex F.pptx
byasifmirzamakki
 
PPTX
Design and Thermal Analysis of Plate Type Heat Exchanger
byKhageshKumarDansena
 
PPT
861828706-chapter-7 introductionto UrbanHydrology.ppt
byHussenMohammed18
 
PPTX
Lean Maintenance: Streamlining Operations for Efficiency & Reliability
byMaintWiz Technologies Private Limited
 
PPT
Pointer in C: declaration and initialization
byMinakshee Patil
 
PPTX
Inventory Excellence: Achieve Efficient Inventory Management with CMMS
byMaintWiz Technologies Private Limited
 
PPTX
Importance of Instrument Calibration — Improve Accuracy, Reliability & Mainte...
byMaintWiz Technologies Private Limited
 
PDF
Presentation-on-Energy-Transition-in-Bangladesh-Employment-and-Skills.pdf
bysyedSajib8
 
PPTX
علي نفط.pptx هندسة النفط هندسة النفط والغاز
byengpe23e27
 
PPTX
Natural Gas fundamentals and GRU for associated gas trap.pptx
byengineerhassan
 
PPTX
How to Create an Effective Monthly Maintenance Plan for Reliable Plant Operat...
byMaintWiz Technologies Private Limited
 
PPTX
MECCA Empire – Hotel Shuttle System for the 2026 FIFA World Cup
byay2432
 
PPTX
The Importance of Maintenance Budgets — Maximize Reliability & Control Costs ...
byMaintWiz Technologies Private Limited
 
PPTX
TPM Metrics & Measurement: Drive Performance Excellence with TPM | MaintWiz
byMaintWiz Technologies Private Limited
 
PDF
Narrows Planning Collective Transportation Capstone.pdf
bycj2392
 
Qui aut sit esse qu Qui aut sit esse quQui aut sit esse quQui aut sit esse qu...
bysheikhshahib123
 
new Introduction to PACS.ppt Picture Archieving and communication and medicine
byssuserf3d7af
 
DEEP LEARNING UNIT 2 all about deep learning
bypavanforsocials
 
Technology Stack Evolution: Analyzing Programming Language & Database Trends ...
byzouhairfezzaz54
 
Theory Of Computation-viruses Theory Of Computation-viruses
bySandeepGupta229023
 
IEC60079-10-1 -Annex F -Hazardous Area Classification -HAC-Annex F.pptx
byasifmirzamakki
 
Design and Thermal Analysis of Plate Type Heat Exchanger
byKhageshKumarDansena
 
861828706-chapter-7 introductionto UrbanHydrology.ppt
byHussenMohammed18
 
Lean Maintenance: Streamlining Operations for Efficiency & Reliability
byMaintWiz Technologies Private Limited
 
Pointer in C: declaration and initialization
byMinakshee Patil
 
Inventory Excellence: Achieve Efficient Inventory Management with CMMS
byMaintWiz Technologies Private Limited
 
Importance of Instrument Calibration — Improve Accuracy, Reliability & Mainte...
byMaintWiz Technologies Private Limited
 
Presentation-on-Energy-Transition-in-Bangladesh-Employment-and-Skills.pdf
bysyedSajib8
 
علي نفط.pptx هندسة النفط هندسة النفط والغاز
byengpe23e27
 
Natural Gas fundamentals and GRU for associated gas trap.pptx
byengineerhassan
 
How to Create an Effective Monthly Maintenance Plan for Reliable Plant Operat...
byMaintWiz Technologies Private Limited
 
MECCA Empire – Hotel Shuttle System for the 2026 FIFA World Cup
byay2432
 
The Importance of Maintenance Budgets — Maximize Reliability & Control Costs ...
byMaintWiz Technologies Private Limited
 
TPM Metrics & Measurement: Drive Performance Excellence with TPM | MaintWiz
byMaintWiz Technologies Private Limited
 
Narrows Planning Collective Transportation Capstone.pdf
bycj2392
 

Hs2014 bci mi