Benjamin Blankertz Guido Dornhege Matthias Krauledat Klaus-Robert Müller Gabriel Curio

Size: px
Start display at page:

Download "Benjamin Blankertz Guido Dornhege Matthias Krauledat Klaus-Robert Müller Gabriel Curio"

Transcription

1 Benjamin Blankertz Guido Dornhege Matthias Krauledat Klaus-Robert Müller Gabriel Curio

2

3 During resting wakefulness distinct idle rhythms located over various brain areas: around 10 Hz (alpha range) over the pericentral sensorimotor cortices. at about 20 Hz over the motor cortex. The μ and β rhythms are blocked by movements of the corresponding body part, (active, passive, or reflexive).

4 Since changes in brain activity are observed over the motor and/or sensory cortex even when a subject is only imagining a movement/sensation in a specific limb -> This can be used for effective BCI control!

5 Ten subjects (all male; 1 left handed; age years, all staff members at the two involved institutions) None of the subjects had extensive training with BCI feedback Recorded with multi-channel EEG amplifiers using 128 channels band-pass filtered between 0.05 and 200 Hz and sampled at 1000 Hz. Surface EMG at both forearms and the right leg, as well as horizontal and vertical EOG signals, were recorded.

6 CALIBRATION SESSIONS POSITION CONTROLLED CURSOR RATE CONTROLLED CURSOR BASKET GAME

7 Visual stimuli indicated which of the following 3 motor imageries the subject should perform: (L) left hand, (R) right hand, or (F) right foot. Target cues were visible on the screen for a duration of 3.5 s, interleaved by periods of random length, 1.75 to 2.25 s, in which the subject could relax. (280 TRIALS for each subject) Data was then screened to adjust subject-specific parameters of the data processing methods Identified the two classes that gave best discrimination and trained a binary classifier (VERTICAL OR HORIZONTAL PARADIGMS ASSIGNED)

8 The initial classifier was behaving suboptimal. So a calibration phase was introduced at the beginning of the feedback sessions in which the subject controlled the cursor freely and the experimenter adjusted the bias and the scaling of the classifier

9 POSITION CONTROLLED CURSOR 25 fps Each trial begins with a black dot that has to be moved into the central area of the screen where the shape of the cursor changed to a cross. Next, the task was to steer the cursor into the highlighted target by imagining the corresponding unilateral hand movements. RATE CONTROLLED CURSOR Similar to position controlled but always starts in middle and position decided by old position, shifted by an amount proportional to the classifier output (more controlled: speed and direction)

10 Here the scene consisted of three targets, gray rectangles at the bottom of the screen and a magenta ball (cursor) that could be directed as it fell. The two outer rectangles were smaller than the middle one to account for the fact that they were easier to hit. In each trial, one of the targets was highlighted in blue, and the subject was trying to direct a cursor in the form of a magenta ball into this target.

11

12 Able to achieve good discrimination between two motor imagery tasks. Includes for all nine subjects the μ-band around 10 Hz, and extends up to the higher β- band for 6/9 subjects. Successfully implemented an individually optimized passband for every subject In this figure: distinguished left and right imagined movement in the first subject

13 One of the ten study participants was unable to produce distinguishable results with the BCI. Experimenters suggest this is probably due to BCI Illiteracy or an incompatibility of a specific person s brain with the Brain-Computer-Interface.

14 (1) robustly transfers the discrimination of mental states from the calibration to the feedback sessions (2) allows a very fast switching between mental states (3) provides reliable feedback directly after a short calibration measurement and machine learning without the need that the subject adapts to the system In 2005, Kübler demonstrated that ALS patients can operate a BCI by the voluntary control of sensorimotor rhythms.

15

The non-invasive Berlin Brain Computer Interface: Fast acquisition of effective performance in untrained subjects

The non-invasive Berlin Brain Computer Interface: Fast acquisition of effective performance in untrained subjects www.elsevier.com/locate/ynimg NeuroImage 37 (2007) 539 550 The non-invasive Berlin Brain Computer Interface: Fast acquisition of effective performance in untrained subjects Benjamin Blankertz, a, Guido

More information

The non-invasive Berlin Brain-Computer Interface: Fast Acquisition of Effective Performance in Untrained Subjects

The non-invasive Berlin Brain-Computer Interface: Fast Acquisition of Effective Performance in Untrained Subjects The non-invasive Berlin Brain-Computer Interface: Fast Acquisition of Effective Performance in Untrained Subjects Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Klaus-Robert Müller, Gabriel Curio

More information

Simultaneous Real-Time Detection of Motor Imagery and Error-Related Potentials for Improved BCI Accuracy

Simultaneous Real-Time Detection of Motor Imagery and Error-Related Potentials for Improved BCI Accuracy Simultaneous Real-Time Detection of Motor Imagery and Error-Related Potentials for Improved BCI Accuracy P. W. Ferrez 1,2 and J. del R. Millán 1,2 1 IDIAP Research Institute, Martigny, Switzerland 2 Ecole

More information

Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Klaus-Robert Müller, Gabriel Curio The Berlin Brain-Computer Interface: Report from the

Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Klaus-Robert Müller, Gabriel Curio The Berlin Brain-Computer Interface: Report from the Benjamin Blankertz, Guido Dornhege, Matthias Krauledat, Klaus-Robert Müller, Gabriel Curio The Berlin Brain-Computer Interface: Report from the Feedback Sessions FIRST Reports 1/2005 FIRST Reports Herausgegeben

More information

A Brain Computer Interface System For Auto Piloting Wheelchair

A Brain Computer Interface System For Auto Piloting Wheelchair A Brain Computer Interface System For Auto Piloting Wheelchair Reshmi G, N. Kumaravel & M. Sasikala Centre for Medical Electronics, Dept. of Electronics and Communication Engineering, College of Engineering,

More information

Predicting BCI Performance to Study BCI Illiteracy

Predicting BCI Performance to Study BCI Illiteracy Predicting BCI Performance to Study BCI Illiteracy Benjamin Blankertz a,b, Claudia Sannelli a, Sebastian Halder d, Eva-Maria Hammer d, Andrea Kübler e,d, Klaus-Robert Müller a, Gabriel Curio c, Thorsten

More information

Playing Pinball with non-invasive BCI

Playing Pinball with non-invasive BCI Playing Pinball with non-invasive BCI Michael W. Tangermann schroedm@cs.tu-berlin.de Konrad Grzeska konradg@cs.tu-berlin.de Carmen Vidaurre vidcar@cs.tu-berlin.de Matthias Krauledat kraulem@cs.tu-berlin.de

More information

A COGNITIVE BRAIN-COMPUTER INTERFACE PROTOTYPE FOR THE CONTINUOUS MONITORING OF VISUAL WORKING MEMORY LOAD.

A COGNITIVE BRAIN-COMPUTER INTERFACE PROTOTYPE FOR THE CONTINUOUS MONITORING OF VISUAL WORKING MEMORY LOAD. 2015 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, SEPT. 17 20, 2015, BOSTON, USA A COGNITIVE BRAIN-COMPUTER INTERFACE PROTOTYPE FOR THE CONTINUOUS MONITORING OF VISUAL WORKING

More information

Generating Artificial EEG Signals To Reduce BCI Calibration Time

Generating Artificial EEG Signals To Reduce BCI Calibration Time Generating Artificial EEG Signals To Reduce BCI Calibration Time Fabien Lotte To cite this version: Fabien Lotte. Generating Artificial EEG Signals To Reduce BCI Calibration Time. 5th International Brain-Computer

More information

Modifying the Classic Peak Picking Technique Using a Fuzzy Multi Agent to Have an Accurate P300-based BCI

Modifying the Classic Peak Picking Technique Using a Fuzzy Multi Agent to Have an Accurate P300-based BCI Modifying the Classic Peak Picking Technique Using a Fuzzy Multi Agent to Have an Accurate P3-based BCI Gholamreza Salimi Khorshidi School of cognitive sciences, Institute for studies in theoretical physics

More information

EEG Event-Related Desynchronization of patients with stroke during motor imagery of hand movement

EEG Event-Related Desynchronization of patients with stroke during motor imagery of hand movement Journal of Physics: Conference Series PAPER OPEN ACCESS EEG Event-Related Desynchronization of patients with stroke during motor imagery of hand movement To cite this article: Carolina B Tabernig et al

More information

Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes

Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes Single Trial Classification of Motor Imagination Using 6 Dry EEG Electrodes Florin Popescu 1 *, Siamac Fazli 1, Yakob Badower 1, Benjamin Blankertz 1,2, Klaus-R. Müller 1,2 1 Intelligent Data Analysis

More information

CURRENT TRENDS IN GRAZ BRAIN-COMPUTER INTERFACE (BCI) RESEARCH

CURRENT TRENDS IN GRAZ BRAIN-COMPUTER INTERFACE (BCI) RESEARCH CURRENT TRENDS IN GRAZ BRAINCOMPUTER INTERFACE (BCI) RESEARCH C. Neuper, C. Guger, E. Haselsteiner, B. Obermaier, M. Pregenzer, H. Ramoser, A. Schlogl, G. Pfurtscheller Department of Medical Informatics,

More information

Research Article EEG-Based Brain-Computer Interface for Tetraplegics

Research Article EEG-Based Brain-Computer Interface for Tetraplegics Laura Kauhanen, Pasi Jylänki, Janne Lehtonen, Pekka Rantanen, Hannu Alaranta, and Mikko Sams. 7. EEG based brain computer interface for tetraplegics. Computational Intelligence and Neuroscience, volume

More information

EEG-Based Brain Computer Interface System for Cursor Control Velocity Regression with Recurrent Neural Network

EEG-Based Brain Computer Interface System for Cursor Control Velocity Regression with Recurrent Neural Network EEG-Based Brain Computer Interface System for Cursor Control Velocity Regression with Recurrent Neural Network Haoqi WANG the Hong Kong University of Science and Technology hwangby@connect.ust.hk Abstract

More information

Hybrid Brain-Computer Interfaces

Hybrid Brain-Computer Interfaces Hybrid Brain-Computer Interfaces Lecture by at MPI Leipzig, 16th July 2014 Outline BCI Aim Technology: EEG Method: feature extraction, machine learning Results NIRS Hemodynamics Technology NIRS-EEG-BCI

More information

Novel single trial movement classification based on temporal dynamics of EEG

Novel single trial movement classification based on temporal dynamics of EEG Novel single trial movement classification based on temporal dynamics of EEG Conference or Workshop Item Accepted Version Wairagkar, M., Daly, I., Hayashi, Y. and Nasuto, S. (2014) Novel single trial movement

More information

Decoding covert somatosensory attention by a BCI system calibrated with tactile sensation

Decoding covert somatosensory attention by a BCI system calibrated with tactile sensation This is the author's version of an article that has been published in IEEE Transactions on Biomedical Engineering. Changes may be made to this version by the publisher prior to publication. The final version

More information

Supplementary materials for: Executive control processes underlying multi- item working memory

Supplementary materials for: Executive control processes underlying multi- item working memory Supplementary materials for: Executive control processes underlying multi- item working memory Antonio H. Lara & Jonathan D. Wallis Supplementary Figure 1 Supplementary Figure 1. Behavioral measures of

More information

Neuromotor Rehabilitation by Neurofeedback

Neuromotor Rehabilitation by Neurofeedback Network per la riabilitazione mentale e motoria dell'ictus Združenje za kognitivno in gibalno rehabilitacijo po možganski kapi Neuromotor Rehabilitation by Neurofeedback P. Paolo Battaglini Bibione, 26

More information

Brain-computer interface to transform cortical activity to control signals for prosthetic arm

Brain-computer interface to transform cortical activity to control signals for prosthetic arm Brain-computer interface to transform cortical activity to control signals for prosthetic arm Artificial neural network Spinal cord challenge: getting appropriate control signals from cortical neurons

More information

The effect of feedback presentation on motor imagery performance during BCI-teleoperation of a humanlike robot

The effect of feedback presentation on motor imagery performance during BCI-teleoperation of a humanlike robot 2014 5th IEEE RAS & EMBS International Conference on Biomedical Robotics and Biomechatronics (BioRob) August 12-15, 2014. São Paulo, Brazil The effect of feedback presentation on motor imagery performance

More information

sensors ISSN

sensors ISSN Supplementary Information OPEN ACCESS sensors ISSN 1424-8220 www.mdpi.com/journal/sensors A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users. Sensors

More information

Clinical Neurophysiology

Clinical Neurophysiology Clinical Neurophysiology 121 (2010) 1293 1303 Contents lists available at ScienceDirect Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph Towards a user-friendly brain computer

More information

Error Detection based on neural signals

Error Detection based on neural signals Error Detection based on neural signals Nir Even- Chen and Igor Berman, Electrical Engineering, Stanford Introduction Brain computer interface (BCI) is a direct communication pathway between the brain

More information

Normal EEG of wakeful resting adults of years of age. Alpha rhythm. Alpha rhythm. Alpha rhythm. Normal EEG of the wakeful adult at rest

Normal EEG of wakeful resting adults of years of age. Alpha rhythm. Alpha rhythm. Alpha rhythm. Normal EEG of the wakeful adult at rest Normal EEG of wakeful resting adults of 20-60 years of age Suthida Yenjun, M.D. Normal EEG of the wakeful adult at rest Alpha rhythm Beta rhythm Mu rhythm Vertex sharp transients Intermittent posterior

More information

Hand of Hope. For hand rehabilitation. Member of Vincent Medical Holdings Limited

Hand of Hope. For hand rehabilitation. Member of Vincent Medical Holdings Limited Hand of Hope For hand rehabilitation Member of Vincent Medical Holdings Limited Over 17 Million people worldwide suffer a stroke each year A stroke is the largest cause of a disability with half of all

More information

MOVEMENT related brain activity has been studied for

MOVEMENT related brain activity has been studied for 1 Steady-State Movement Related Potentials for Brain Computer Interfacing Kianoush Nazarpour, Member, IEEE, Peter Praamstra, R. Chris Miall, and Saeid Sanei, Senior Member, IEEE Abstract An approach for

More information

CSE 599E Introduction to Brain-Computer Interfacing

CSE 599E Introduction to Brain-Computer Interfacing CSE 599E Introduction to Brain-Computer Interfacing Instructor: Rajesh Rao TA: Sam Sudar The Matrix (1999) Firefox(1982) Brainstorm (1983) Spiderman 2 (2004) Hollywood fantasy apart, why would we want

More information

Prediction of Difficulty Levels in Video Games from Ongoing EEG

Prediction of Difficulty Levels in Video Games from Ongoing EEG Prediction of Difficulty Levels in Video Games from Ongoing EEG Laura Naumann 1,2(B), Matthias Schultze-Kraft 3,4,SvenDähne 2,5, and Benjamin Blankertz 3,4 1 Bernstein Center for Computational Neuroscience

More information

IEEE. Proof. BRAIN COMPUTER interface (BCI) research has been

IEEE. Proof. BRAIN COMPUTER interface (BCI) research has been TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 14, NO. 2, JUNE 2006 1 EEG and MEG Brain Computer Interface for Tetraplegic Patients Laura Kauhanen, Tommi Nykopp, Janne Lehtonen, Pasi

More information

The Application of EEG related

The Application of EEG related Available online at www.sciencedirect.com Procedia Environmental Sciences 10 (2011 ) 1338 1342 2011 3rd International Conference on Environmental Science and Information ESIAT Application 2011 Technology

More information

ORIGINAL ARTICLES. Motor Area Activation During Dreamed Hand Clenching: A Pilot Study on EEG Alpha Band

ORIGINAL ARTICLES. Motor Area Activation During Dreamed Hand Clenching: A Pilot Study on EEG Alpha Band ORIGINAL ARTICLES Motor Area Activation During Dreamed Hand Clenching: A Pilot Study on EEG Alpha Band Daniel Erlacher, M.A., Michael Schredl, Ph.D., Stephen LaBerge, Ph.D. In a single participant physiological

More information

Brain Computer Interface. Mina Mikhail

Brain Computer Interface. Mina Mikhail Brain Computer Interface Mina Mikhail minamohebn@gmail.com Introduction Ways for controlling computers Keyboard Mouse Voice Gestures Ways for communicating with people Talking Writing Gestures Problem

More information

ANALYSIS OF EEG FOR MOTOR IMAGERY BASED CLASSIFICATION OF HAND ACTIVITIES

ANALYSIS OF EEG FOR MOTOR IMAGERY BASED CLASSIFICATION OF HAND ACTIVITIES ANALYSIS OF EEG FOR MOTOR IMAGERY BASED CLASSIFICATION OF HAND ACTIVITIES A.Sivakami 1 and S.Shenbaga Devi 2 1 PG Student, Department of ECE College of Engineering Guindy, Anna University Chennai-600025,

More information

Neurophysiological Predictor of SMR-Based BCI Performance

Neurophysiological Predictor of SMR-Based BCI Performance Neurophysiological Predictor of SMR-Based BCI Performance Benjamin Blankertz a,b, Claudia Sannelli a, Sebastian Halder c, Eva M. Hammer c, Andrea Kübler c,d, Klaus-Robert Müller a, Gabriel Curio e, Thorsten

More information

Brain computer interfaces (BCIs): Detection instead of classification

Brain computer interfaces (BCIs): Detection instead of classification Journal of Neuroscience Methods 167 (2008) 51 62 Brain computer interfaces (BCIs): Detection instead of classification G. Schalk a,b,, P. Brunner a,c, L.A. Gerhardt b, H. Bischof c, J.R. Wolpaw a,d a Brain-Computer

More information

Development of a New Rehabilitation System Based on a Brain-Computer Interface Using Near-Infrared Spectroscopy

Development of a New Rehabilitation System Based on a Brain-Computer Interface Using Near-Infrared Spectroscopy Development of a New Rehabilitation System Based on a Brain-Computer Interface Using Near-Infrared Spectroscopy Takafumi Nagaoka, Kaoru Sakatani, Takayuki Awano, Noriaki Yokose, Tatsuya Hoshino, Yoshihiro

More information

Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface

Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface Phase Synchrony Rate for the Recognition of Motor Imagery in Brain-Computer Interface Le Song Nation ICT Australia School of Information Technologies The University of Sydney NSW 2006, Australia lesong@it.usyd.edu.au

More information

Supplementary Materials

Supplementary Materials Supplementary Materials Supplementary Figure S1: Data of all 106 subjects in Experiment 1, with each rectangle corresponding to one subject. Data from each of the two identical sub-sessions are shown separately.

More information

Discrimination of EEG-Based Motor Imagery Tasks by Means of a Simple Phase Information Method

Discrimination of EEG-Based Motor Imagery Tasks by Means of a Simple Phase Information Method Discrimination of EEG-Based Motor Tasks by Means of a Simple Phase Information Method Ana Loboda Gabriela Rotariu Alexandra Margineanu Anca Mihaela Lazar Abstract We propose an off-line analysis method

More information

Optimal Spatial Filtering of Single Trial EEG During Imagined Hand Movement

Optimal Spatial Filtering of Single Trial EEG During Imagined Hand Movement IEEE TRANSACTIONS ON REHABILITATION ENGINEERING, VOL. 8, NO. 4, DECEMBER 2000 441 Optimal Spatial Filtering of Single Trial EEG During Imagined Hand Movement Herbert Ramoser, Johannes Müller-Gerking, and

More information

8/20/ Identify the functions of common ECG machines. 3.3 Explain how each ECG machine control is used. 3.4 Recognize common electrodes.

8/20/ Identify the functions of common ECG machines. 3.3 Explain how each ECG machine control is used. 3.4 Recognize common electrodes. 1 2 Electrocardiography for Healthcare Professionals Chapter 3: The Electrocardiograph Learning Outcomes 3.1 Identify three types of leads, and explain how each is recorded. 3.2 Identify the functions

More information

Simultaneous Acquisition of EEG and NIRS during Cognitive Tasks for an Open Access Dataset. Data Acquisition

Simultaneous Acquisition of EEG and NIRS during Cognitive Tasks for an Open Access Dataset. Data Acquisition Simultaneous Acquisition of EEG and NIRS during Cognitive Tasks for an Open Access Dataset We provide an open access multimodal brain-imaging dataset of simultaneous electroencephalography (EEG) and near-infrared

More information

Somatosensory Plasticity and Motor Learning

Somatosensory Plasticity and Motor Learning 5384 The Journal of Neuroscience, April 14, 2010 30(15):5384 5393 Behavioral/Systems/Cognitive Somatosensory Plasticity and Motor Learning David J. Ostry, 1,2 Mohammad Darainy, 1,3 Andrew A. G. Mattar,

More information

Comparison of Adaptive Cancellation and Laplacian Operation in Removing Eye Blinking Artifacts in EEG

Comparison of Adaptive Cancellation and Laplacian Operation in Removing Eye Blinking Artifacts in EEG Journal of Medical and Biological Engineering, 24(1): 9-15 9 Comparison of Adaptive Cancellation and Laplacian Operation in Removing Eye Blinking Artifacts in EEG Chou-Ching K. Lin Shun-Lung Chaung 1 Ming-Shaung

More information

How we study the brain: a survey of methods used in neuroscience

How we study the brain: a survey of methods used in neuroscience How we study the brain: a survey of methods used in neuroscience Preparing living neurons for recording Large identifiable neurons in a leech Rohon-Beard neurons in a frog spinal cord Living slice of a

More information

The Berlin brain computer interface: non-medical uses of BCI technology

The Berlin brain computer interface: non-medical uses of BCI technology Review Article published: 08 December 2010 doi: 10.3389/fnins.2010.00198 The Berlin brain computer interface: non-medical uses of BCI technology Benjamin Blankertz 1,2 *, Michael Tangermann 1, Carmen Vidaurre

More information

EEG Analysis on Brain.fm (Focus)

EEG Analysis on Brain.fm (Focus) EEG Analysis on Brain.fm (Focus) Introduction 17 subjects were tested to measure effects of a Brain.fm focus session on cognition. With 4 additional subjects, we recorded EEG data during baseline and while

More information

The Physiology of the Senses Chapter 8 - Muscle Sense

The Physiology of the Senses Chapter 8 - Muscle Sense The Physiology of the Senses Chapter 8 - Muscle Sense www.tutis.ca/senses/ Contents Objectives... 1 Introduction... 2 Muscle Spindles and Golgi Tendon Organs... 3 Gamma Drive... 5 Three Spinal Reflexes...

More information

Development of an Electroencephalography-Based Brain-Computer Interface Supporting Two- Dimensional Cursor Control

Development of an Electroencephalography-Based Brain-Computer Interface Supporting Two- Dimensional Cursor Control Virginia Commonwealth University VCU Scholars Compass Theses and Dissertations Graduate School 2009 Development of an Electroencephalography-Based Brain-Computer Interface Supporting Two- Dimensional Cursor

More information

Mining the mind: Machine learning in brain research

Mining the mind: Machine learning in brain research Mining the mind: Machine learning in brain research Matthias Treder 2016-12-16 MEASURING BRAIN ACTIVITY image taken from: Astrand E, Wardak C and Ben Hamed S (2014). Selective visual attention to drive

More information

Fractionation of the visuomotor feedback response to directions of movement and perturbation

Fractionation of the visuomotor feedback response to directions of movement and perturbation J Neurophysiol : 8 33,. First published August, ; doi:.5/jn.377.3. Fractionation of the visuomotor feedback response to directions of movement and perturbation David W. Franklin, Sae Franklin, and Daniel

More information

BRAIN Computer Interface (BCI) technology has in recent. Visual Spatial Attention Control in an Independent Brain-Computer Interface

BRAIN Computer Interface (BCI) technology has in recent. Visual Spatial Attention Control in an Independent Brain-Computer Interface 1588 IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 52, NO. 9, SEPTEMBER 2005 Visual Spatial Attention Control in an Independent Brain-Computer Interface Simon P. Kelly*, Edmund C. Lalor, Ciarán Finucane,

More information

EEG changes accompanying learned regulation of 12-Hz EEG activity

EEG changes accompanying learned regulation of 12-Hz EEG activity TNSRE-2002-BCI015 1 EEG changes accompanying learned regulation of 12-Hz EEG activity Arnaud Delorme and Scott Makeig Abstract We analyzed 15 sessions of 64-channel EEG data recorded from a highly trained

More information

Restoring Communication and Mobility

Restoring Communication and Mobility Restoring Communication and Mobility What are they? Artificial devices connected to the body that substitute, restore or supplement a sensory, cognitive, or motive function of the nervous system that has

More information

Encoding and decoding of voluntary movement control in the motor cortex

Encoding and decoding of voluntary movement control in the motor cortex Neuroinformatics and Theoretical Neuroscience Institute of Biology Neurobiology Bernstein Center for Computational Neuroscience Encoding and decoding of voluntary movement control in the motor cortex Martin

More information

Using Motor Imagery to Control Brain-Computer Interfaces for Communication

Using Motor Imagery to Control Brain-Computer Interfaces for Communication Using Motor Imagery to Control Brain-Computer Interfaces for Communication Jonathan S. Brumberg 1,2(B), Jeremy D. Burnison 2, and Kevin M. Pitt 1 1 Department of Speech-Language-Hearing: Sciences & Disorders,

More information

Visual Selection and Attention

Visual Selection and Attention Visual Selection and Attention Retrieve Information Select what to observe No time to focus on every object Overt Selections Performed by eye movements Covert Selections Performed by visual attention 2

More information

An Overview of BMIs. Luca Rossini. Workshop on Brain Machine Interfaces for Space Applications

An Overview of BMIs. Luca Rossini. Workshop on Brain Machine Interfaces for Space Applications An Overview of BMIs Luca Rossini Workshop on Brain Machine Interfaces for Space Applications European Space Research and Technology Centre, European Space Agency Noordvijk, 30 th November 2009 Definition

More information

Classification of EEG signals in an Object Recognition task

Classification of EEG signals in an Object Recognition task Classification of EEG signals in an Object Recognition task Iacob D. Rus, Paul Marc, Mihaela Dinsoreanu, Rodica Potolea Technical University of Cluj-Napoca Cluj-Napoca, Romania 1 rus_iacob23@yahoo.com,

More information

Exclusion criteria and outlier detection

Exclusion criteria and outlier detection 1 Exclusion criteria and outlier detection 1 2 Supplementary Fig. 1 31 subjects complied with the inclusion criteria as tested during the familiarization session. The upper part of the figure (ovals) indicates

More information

ISSN: (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies

ISSN: (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies ISSN: 2321-7782 (Online) Volume 3, Issue 7, July 2015 International Journal of Advance Research in Computer Science and Management Studies Research Article / Survey Paper / Case Study Available online

More information

Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution

Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution Motor Imagery for Severely Motor-Impaired Patients: Evidence for Brain-Computer Interfacing as Superior Control Solution Johannes Höhne 1 *, Elisa Holz 2, Pit Staiger-Sälzer 3, Klaus-Robert Müller 4,5,6

More information

Training For The Triple Jump. The Aston Moore Way

Training For The Triple Jump. The Aston Moore Way Training For The Triple Jump The Aston Moore Way Establish Your Technical Style/Model Go anywhere in the world, the triple jumps is still just a hop, step and a jump So, what do I mean by technical style

More information

Classifying P300 Responses to Vowel Stimuli for Auditory Brain-Computer Interface

Classifying P300 Responses to Vowel Stimuli for Auditory Brain-Computer Interface Classifying P300 Responses to Vowel Stimuli for Auditory Brain-Computer Interface Yoshihiro Matsumoto, Shoji Makino, Koichi Mori and Tomasz M. Rutkowski Multimedia Laboratory, TARA Center, University of

More information

EEG-Based Communication and Control: Speed Accuracy Relationships

EEG-Based Communication and Control: Speed Accuracy Relationships Applied Psychophysiology and Biofeedback, Vol. 28, No. 3, September 2003 ( C 2003) EEG-Based Communication and Control: Speed Accuracy Relationships Dennis J. McFarland 1,2 and Jonathan R. Wolpaw 1 People

More information

11/18/13 ECG SIGNAL ACQUISITION HARDWARE DESIGN. Origin of Bioelectric Signals

11/18/13 ECG SIGNAL ACQUISITION HARDWARE DESIGN. Origin of Bioelectric Signals ECG SIGNAL ACQUISITION HARDWARE DESIGN Origin of Bioelectric Signals 1 Cell membrane, channel proteins Electrical and chemical gradients at the semi-permeable cell membrane As a result, we get a membrane

More information

A Review of Asynchronous Electroencephalogram-based Brain Computer Interface Systems

A Review of Asynchronous Electroencephalogram-based Brain Computer Interface Systems 2011 International Conference on Biomedical Engineering and Technology IPCBEE vol.11 (2011) (2011) IACSIT Press, Singapore A Review of Asynchronous Electroencephalogram-based Brain Computer Interface Systems

More information

Chapter 4. Two Types of Attention. Selective Listening 25/09/2012. Paying Attention. How does selective attention work?

Chapter 4. Two Types of Attention. Selective Listening 25/09/2012. Paying Attention. How does selective attention work? Chapter 4 Paying Attention Two Types of Attention How does selective attention work? How do we block out irrelevant information? What can t we block out? How much control do we have over our attention?

More information

Comparison of SMR and SCP training employing a newly developed discrete-trial based biofeedback system

Comparison of SMR and SCP training employing a newly developed discrete-trial based biofeedback system Comparison of SMR and SCP training employing a newly developed discrete-trial based biofeedback system Implications for Brain-Computer Interfaces, Neurofeedback and the interrelationship between SCP and

More information

Research & Development of Rehabilitation Technology in Singapore

Research & Development of Rehabilitation Technology in Singapore Research & Development of Rehabilitation Technology in Singapore ANG Wei Tech Associate Professor School of Mechanical & Aerospace Engineering wtang@ntu.edu.sg Assistive Technology Technologists / Engineers

More information

Human Anatomy and Physiology - ANAT 14 Sensory System Lab Goals Activities

Human Anatomy and Physiology - ANAT 14 Sensory System Lab Goals Activities Sensory System Human Anatomy and Physiology - ANAT 14 Lab Goals Observe many characteristics of our somatic and special senses. Activity descriptions noted in your lab manual are specified. Activities

More information

FORSKNING INDENFOR NEUROREHABILITERING PÅ INSTITUT FOR MEDICIN OG SUNDHEDSTEKNOLOGI, AAU KIM DREMSTRUP AND NATALIE MRACHACZ-KERSTING

FORSKNING INDENFOR NEUROREHABILITERING PÅ INSTITUT FOR MEDICIN OG SUNDHEDSTEKNOLOGI, AAU KIM DREMSTRUP AND NATALIE MRACHACZ-KERSTING FORSKNING INDENFOR NEUROREHABILITERING PÅ INSTITUT FOR MEDICIN OG SUNDHEDSTEKNOLOGI, AAU KIM DREMSTRUP AND NATALIE MRACHACZ-KERSTING Brain activity to control external devices and activities Brain-Computer-Interface

More information

Brain oscillatory signatures of motor tasks

Brain oscillatory signatures of motor tasks J Neurophysiol 113: 3663 3682, 2015. First published March 25, 2015; doi:10.1152/jn.00467.2013. Brain oscillatory signatures of motor tasks Ander Ramos-Murguialday 1,2 and Niels Birbaumer 1,3 1 Institute

More information

AUDITORY FEEDBACK OF HUMAN EEG FOR DIRECT BRAIN-COMPUTER COMMUNICATION. Thilo Hinterberger, Gerold Baier*, Jürgen Mellinger, Niels Birbaumer

AUDITORY FEEDBACK OF HUMAN EEG FOR DIRECT BRAIN-COMPUTER COMMUNICATION. Thilo Hinterberger, Gerold Baier*, Jürgen Mellinger, Niels Birbaumer AUDITORY FEEDBACK OF HUMAN EEG FOR DIRECT BRAIN-COMPUTER COMMUNICATION Thilo Hinterberger, Gerold Baier*, Jürgen Mellinger, Niels Birbaumer Institute of Medical Psychology and Behavioral Neurobiology Gartenstr.

More information

Supplementary Motor Area exerts Proactive and Reactive Control of Arm Movements

Supplementary Motor Area exerts Proactive and Reactive Control of Arm Movements Supplementary Material Supplementary Motor Area exerts Proactive and Reactive Control of Arm Movements Xiaomo Chen, Katherine Wilson Scangos 2 and Veit Stuphorn,2 Department of Psychological and Brain

More information

BRAIN COMPUTER interfaces (BCIs) [1] attempt to provide

BRAIN COMPUTER interfaces (BCIs) [1] attempt to provide IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 55, NO. 1, JANUARY 2008 273 Generalized Features for Electrocorticographic BCIs Pradeep Shenoy 3, Kai J. Miller, Jeffrey G. Ojemann, and Rajesh P. N. Rao

More information

Emotion Detection from EEG signals with Continuous Wavelet Analyzing

Emotion Detection from EEG signals with Continuous Wavelet Analyzing American Journal of Computing Research Repository, 2014, Vol. 2, No. 4, 66-70 Available online at http://pubs.sciepub.com/ajcrr/2/4/3 Science and Education Publishing DOI:10.12691/ajcrr-2-4-3 Emotion Detection

More information

On the Possible Pitfalls in the Evaluation of Brain Computer Interface Mice

On the Possible Pitfalls in the Evaluation of Brain Computer Interface Mice On the Possible Pitfalls in the Evaluation of Brain Computer Interface Mice Riccardo Poli and Mathew Salvaris Brain-Computer Interfaces Lab, School of Computer Science and Electronic Engineering, University

More information

Bayesian Machine Learning for Decoding the Brain

Bayesian Machine Learning for Decoding the Brain for Decoding the Brain Radboud University Nijmegen, The Netherlands Institute for Computing and Information Sciences Radboud University Nijmegen, The Netherlands June 10, IWANN 2011, Torremolinos Faculty

More information

International Journal of Engineering Science Invention Research & Development; Vol. III, Issue X, April e-issn:

International Journal of Engineering Science Invention Research & Development; Vol. III, Issue X, April e-issn: BRAIN COMPUTER INTERFACING FOR CONTROLLING HOME APPLIANCES ShubhamMankar, Sandeep Shriname, ChetanAtkare, Anjali R,Askhedkar BE Student, Asst.Professor Department of Electronics and Telecommunication,MITCOE,India

More information

Bayesian integration in sensorimotor learning

Bayesian integration in sensorimotor learning Bayesian integration in sensorimotor learning Introduction Learning new motor skills Variability in sensors and task Tennis: Velocity of ball Not all are equally probable over time Increased uncertainty:

More information

Classifying Single Trial EEG: Towards Brain Computer Interfacing

Classifying Single Trial EEG: Towards Brain Computer Interfacing Classifying Single Trial EEG: Towards Brain Computer Interfacing Benjamin Blankertz 1, Gabriel Curio 2 and Klaus-Robert Müller 1,3 1 Fraunhofer-FIRST.IDA, Kekuléstr. 7, 12489 Berlin, Germany 2 Neurophysics

More information

Real-time brain computer interface using imaginary movements

Real-time brain computer interface using imaginary movements Downloaded from orbit.dtu.dk on: Apr 06, 2018 Real-time brain computer interface using imaginary movements El-Madani, Ahmad; Sørensen, Helge Bjarup Dissing; Kjær, Troels W.; Thomsen, Carsten E.; Puthusserypady,

More information

MEMORY MODELS. CHAPTER 5: Memory models Practice questions - text book pages TOPIC 23

MEMORY MODELS. CHAPTER 5: Memory models Practice questions - text book pages TOPIC 23 TOPIC 23 CHAPTER 65 CHAPTER 5: Memory models Practice questions - text book pages 93-94 1) Identify the three main receptor systems used by a performer in sport. Where is the filtering mechanism found

More information

Epilepsy & Behavior 13 (2008) Contents lists available at ScienceDirect. Epilepsy & Behavior. journal homepage:

Epilepsy & Behavior 13 (2008) Contents lists available at ScienceDirect. Epilepsy & Behavior. journal homepage: Epilepsy & Behavior 13 (2008) 300 306 Contents lists available at ScienceDirect Epilepsy & Behavior journal homepage: www.elsevier.com/locate/yebeh Voluntary brain regulation and communication with electrocorticogram

More information

A. PRIMARY MOTOR CORTEX: - responsible for - like somatosensory cortex, primary motor cortex show (motor homunculus) - amount of cortex devoted to

A. PRIMARY MOTOR CORTEX: - responsible for - like somatosensory cortex, primary motor cortex show (motor homunculus) - amount of cortex devoted to CONTROL OF MOVEMENT BY THE BRAIN A. PRIMARY MOTOR CORTEX: - responsible for - like somatosensory cortex, primary motor cortex show (motor homunculus) - amount of cortex devoted to different parts of body

More information

Human-Computer Interfaces and Anticipatory Brain Potentials

Human-Computer Interfaces and Anticipatory Brain Potentials Human-Computer Interfaces and Anticipatory Brain Potentials S.Senthilkumar 1, T.Shanmugapriya 2 Assistant Professor, Department of Electronics and Instrumentation, Bharath University, Chennai, Tamil Nadu,

More information

An auditory brain computer interface (BCI)

An auditory brain computer interface (BCI) Journal of Neuroscience Methods 167 (2008) 43 50 An auditory brain computer interface (BCI) Femke Nijboer a,, Adrian Furdea a, Ingo Gunst a,jürgen Mellinger a, Dennis J. McFarland b, Niels Birbaumer a,c,

More information

Oscillations: From Neuron to MEG

Oscillations: From Neuron to MEG Oscillations: From Neuron to MEG Educational Symposium, MEG UK 2014, Nottingham, Jan 8th 2014 Krish Singh CUBRIC, School of Psychology Cardiff University What are we trying to achieve? Bridge the gap from

More information

Detecting affective covert user states with passive Brain-Computer Interfaces

Detecting affective covert user states with passive Brain-Computer Interfaces Detecting affective covert user states with passive Brain-Computer Interfaces Thorsten O. Zander Team PhyPA TU Berlin, Germany tza@mms.tu-berlin.de Sabine Jatzev Team PhyPA TU Berlin, Germany sja@mms.tu-berlin.de

More information

EEG-based Strategies to Detect Motor Imagery for Control and Rehabilitation

EEG-based Strategies to Detect Motor Imagery for Control and Rehabilitation e This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication. Citation information: DOI 10.1109/TNSRE.2016.2646763,

More information

Towards natural human computer interaction in BCI

Towards natural human computer interaction in BCI Towards natural human computer interaction in BCI Ian Daly 1 (Student) and Slawomir J Nasuto 1 and Kevin Warwick 1 Abstract. BCI systems require correct classification of signals interpreted from the brain

More information

TOWARD A BRAIN INTERFACE FOR TRACKING ATTENDED AUDITORY SOURCES

TOWARD A BRAIN INTERFACE FOR TRACKING ATTENDED AUDITORY SOURCES 216 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, SEPT. 13 16, 216, SALERNO, ITALY TOWARD A BRAIN INTERFACE FOR TRACKING ATTENDED AUDITORY SOURCES Marzieh Haghighi 1, Mohammad

More information

Ernest Nlandu Kamavuako, 1 Mads Jochumsen, 1 Imran Khan Niazi, 1,2,3 and Kim Dremstrup Introduction

Ernest Nlandu Kamavuako, 1 Mads Jochumsen, 1 Imran Khan Niazi, 1,2,3 and Kim Dremstrup Introduction Hindawi Publishing Corporation Computational Intelligence and Neuroscience Volume 215, Article ID 85815, 8 pages http://dx.doi.org/1.1155/215/85815 Research Article Comparison of Features for Movement

More information

Brain-computer interface (BCI) operation: signal and noise during early training sessions

Brain-computer interface (BCI) operation: signal and noise during early training sessions Clinical Neurophysiology 116 (2005) 56 62 www.elsevier.com/locate/clinph Brain-computer interface (BCI) operation: signal and noise during early training sessions Dennis J. McFarland*, William A. Sarnacki,

More information

Erigo User Script 1. Erigo Background Information. 2. Intended use and indications

Erigo User Script 1. Erigo Background Information. 2. Intended use and indications Erigo User Script 1. Erigo Background Information The Erigo was developed in collaboration with the Spinal Cord Injury Center at the Balgrist University Hospital in Zurich, Switzerland and the Orthopaedic

More information

Hybrid BCI for people with Duchenne muscular dystrophy

Hybrid BCI for people with Duchenne muscular dystrophy Hybrid BCI for people with Duchenne muscular dystrophy François Cabestaing Rennes, September 7th 2017 2 / 13 BCI timeline 1929 - Electroencephalogram (Berger) 1965 - Discovery of cognitive evoked potentials

More information

Short-Latency Brain-Computer Interface Using Movement-Related Cortical Potentials

Short-Latency Brain-Computer Interface Using Movement-Related Cortical Potentials Short-Latency Brain-Computer Interface Using Movement-Related Cortical Potentials Dissertation zur Erlangung des mathematisch-naturwissenschaftlichen Doktorgrades Doctor rerum naturalium der Georg-August-Universität

More information