This is torture and leads to genocide globally. RECENT ADVANCES IN BRAIN COMPUTER INTERFACE SYSTEMS (BCI and EEG two way brain interface) Edited by Reza Fazel-Rezai Brain Computer Interface (BCI) technology, which provides a direct electronic interface and can convey messages and commands directly from the human brain to a computer. BCI technology involves monitoring conscious brain electrical activity via electroencephalogram (EEG) signals and detecting characteristics of EEG patterns via digital signal processing algorithms that the user generates to communicate. It has the potential to enable the physically disabled to perform many activities, thus improving their quality of life and productivity,allowing them more independence and reducing social costs. The challenge with BCI, however, is to extract the relevant patterns from the EEG signals produced by the brain each second. Contents Preface Chapter 1 IX Hardware/Software Components and Applications of BCIs 1 Christoph Guger, Günter Edlinger and Gunther Krausz Applied Advanced Classifiers for Brain Computer Interface 25 José Luis Martínez, Antonio Barrientos Feature Extraction by Mutual Information Based on Minimal-Redundancy-Maximal-Relevance Criterion and Its Application to Classifying EEG Signal for Brain-Computer Interfaces 67 Abbas Erfanian, Farid Oveisi and Ali Shadvar P300-based Brain-Computer Interface Paradigm Design 83 Reza Fazel-Rezai and Waqas Ahmad Brain Computer Interface Based on the Flash Onset and Offset Visual Evoked Potentials 99 Po-Lei Lee, Yu-Te Wu, Kuo-Kai Shyu and Jen-Chuen Hsieh Usability of Transient VEPs in BCIs 119 Natsue Yoshimura and Naoaki Itakura Visuo-Motor Tasks in a Brain-Computer Interface Analysis 135 Vito Logar and Aleš Belič A Two-Dimensional Brain-Computer Interface Associated With Human Natural Motor Control Dandan Huang, Xuedong Chen, Ding-Yu Fei and Ou Bai Advances in Non-Invasive Brain-Computer Interfaces for Control and Biometry 171 Nuno Figueiredo, Filipe Silva, Pétia Georgieva and Ana Tomé State of the Art in BCI Research: BCI Award 2010 193 Christoph Guger, Guangyu Bin, Xiaorong Gao, Jing Guo, Bo Hong, Tao Liu, Shangkai Gao, Cuntai Guan, Kai Keng Ang, Kok Soon Phua, Chuanchu Wang, Zheng Yang Chin, Haihong Zhang, Rongsheng Lin, Karen Sui Geok Chua, Christopher Kuah, Beng Ti Ang, Harry George, Andrea Kübler, Sebastian Halder, Adi Hösle, Jana Münßinger, Mark Palatucci, Dean Pomerleau, Geoff Hinton, Tom Mitchell, David B. Ryan, Eric W. Sellers, George Townsend, Steven M. Chase, Andrew S. Whitford, Andrew B. Schwartz, Kimiko Kawashima, Keiichiro Shindo, Junichi Ushiba, Meigen Liu and Gerwin Schalk Chapter 10 Preface Communication and the ability to interact with the environment are basic human needs. Millions of people worldwide suffer from such severe physical disabilities that they cannot even meet these basic needs. Even though they may have no motor mobility, however, the sensory and cognitive functions of the physically disabled are usually intact. This makes them good candidates for Brain Computer Interface (BCI) technology, which provides a direct electronic interface and can convey messages and commands directly from the human brain to a computer. BCI technology involves monitoring conscious brain electrical activity via electroencephalogram (EEG) signals and detecting characteristics of EEG patterns via digital signal processing algorithms that the user generates to communicate. It has the potential to enable the physically disabled to perform many activities, thus improving their quality of life and productivity, allowing them more independence and reducing social costs. The challenge with BCI, however, is to extract the relevant patterns from the EEG signals produced by the brain each second. A BCI system has an input, output and a signal processing algorithm that maps the inputs to the output. The following four major strategies are considered for the input of a BCI system: 1) the P300 wave of event related potentials (ERP), 2) steady state visual evoked potential (SSVEP), 3) slow cortical potentials and 4) motor imaginary. Recently, there has been a great progress in the development of novel paradigms for EEG signal recording, advanced methods for processing them, new applications for BCI systems and complete software and hardware packages used for BCI applications. In this book a few recent advances in these areas are discussed. In the first chapter hardware and software components along with several applications of BCI systems are discussed. In chapters 2 and 3 several signal processing methods for classifying EEG signals are presented. In chapter 4 a new paradigm for P300 BCI is compared with traditional P300 BCI paradigms. Chapters 5 and 6 show how a visual evoked potential (VEP)-based BCI works. In chapters 7 and 8 a visuo-motor-based and natural motor control-based BCI systems are discussed, respectively. New applications of BCI systems for control and biometry are discussed in chapter 9. Finally, the recent competition in BCI held in 2010 along with a short summary of the submitted projects are presented in Chapter 10. X Preface As the editor, I would like to thank all the authors of different chapters. Without your contributions, it would not be possible to have a quality book, help in growth of BCI systems and utilize them in real-world applications. Dr. Reza Fazel-Rezai University of North Dakota Grand Forks, ND, USA [email protected] 1 Hardware/Software Components and Applications of BCIs Christoph Guger, Günter Edlinger and Gunther Krausz g.tec medical engineering GmbH/ Guger Technologies OG Austria 1. Introduction Human-Computer interfaces can use different signals from the body in order to control external devices. Beside muscle activity (EMG-Electromyogram), eye movements (EOGElectrooculogram) and respiration also brain activity (EEG-Electroencephalogram) can be used as input signal. EEG-based brain-computer interface (BCI) systems are realized either with (i) slow cortical potentials, (ii) the P300 response, (iii) steady-state visual evoked potentials (SSVEP) or (iv) motor imagery. Potential shift of the scalp EEG over 0.5 – 10 s are called slow cortical potentials (SCPs). Reduced cortical activation goes ahead with positive SCPs, while negative SCPs are associated with movement and other functions involving cortical activation (Birbaumer, 2000). People are able to learn how to control these potentials, hence it is possible to use them for BCIs as Birbaumer and his colleagues did (Birbaumer, 2000, Elbert, 1980). The main disadvantage of this method is the extensive training time to learn how to control the SCPs. Users need to train in several 1-2 h sessions/week over weeks or months. The P300 wave was first discovered by Sutton (Sutton, 1965). It elicits when an unlikely event occurs randomly between events with high probability. In the EEG signal the P300 appears as a positive wave about 300 ms after stimulus onset. Its main usage in BCIs is for spelling devices, but one can also use it for control tasks (for example games (Finkea, 2009) or navigation (e.g. to move a computer-mouse (Citi, 2008)). When using P300 as a spelling device, a matrix of characters is shown to the subject. Now the rows and columns (or in some paradigms the single characters) of the matrix are flashing in random order, while the person concentrates only on the character he/she wants to spell. For better concentration, it is recommended to count how many times the character flashes. Every time the desired character flashes, a P300 wave occurs. As the detection of one single event would be imprecise, more than one trial (flashing of each character) has to be carried out to achieve a proper accuracy. Krusienski et al. (Krusienski, 2006) evaluated different classification techniques for the P300 speller, wherein the stepwise linear discriminant analysis (SWLDA) and the Fisher’s linear discriminant analysis provided the best overall performance and implementation characteristics. A recent study (Guger 2009), performed on 100 subjects, revealed an average accuracy level of 91.1%, with a spelling time of 28.8 s for one single character. Each character was selected out of a matrix of 36 characters. 2 Recent Advances in Brain-Computer Interface Systems Steady state visual evoked potentials (SSVEP)-based BCIs use several stationary flashing sources (e.g. flickering LEDs, or phase-reversing checkerboards), each of them flashing with another constant frequency. When a person gazes at one of these sources, the specific frequency component will increase in the measured EEG, over the occipital lobe. Hence, when using different light sources, each of them representing a predefined command, the person gives this command by gazing onto the source. The classification is either done by FFT-based spectrum comparison, preferably including also the harmonics (Müller-Putz, 2005), or via the canonical correlation analysis (CCA) (Lin, 2006). A third possibility is via the minimum energy approach which was published by O. Friman et.al. in 2007 (Friman, 2007) and requires no training. Typical SSVEP applications are made for navigation, for example Middendorf et al. (Middendorf, 2000) used SSVEPs to control the roll position of a flight simulator. The number of classes varies between two and eight, although Gao et al. (Gao, 2003) established an experiment with even 48 targets. Bakardijan et al. (Bakardijan, 2010) investigated SSVEP responses for frequencies between 5 and 84 Hz to find the strongest response between 5.6 Hz and 15.3 Hz peaking at 12 Hz. With their frequency-optimized-eight-command BCI they achieved a mean success rate of 98 % and an information transfer rate (ITR) of 50 bits/min. Bin et al. (Bin, 2009) reports of a six-target BCI with an average accuracy of 95.3% and an information transfer rate of 58 ± 9.6 bits/min. Although most SSVEP-based BCIs work with gaze shifting towards a source, recent studies (Allison, 2009, Zhang, 2010) proofed that only selective attention onto a pattern alone is sufficient for control. The latter paper achieved an overall classification accuracy of 72.6 +/16.1% after 3 training days. Therefore also severely disabled people, who are not able to move their eyes, can control an SSVEP-based BCI. When subjects perform or only imagine motor tasks, an event related desynchronization (ERD) (Pfurtscheller & Neuper, 1997) and an event related synchronization (ERS) is detectable by changes of EEG rhythms on electrodes close to the respective sensorimotor areas. The ERD is indicated by a decrease of power in the upper alpha band and lower beta band, starting 2 seconds before movement onset on the contra lateral hemisphere and becomes bilaterally symmetrical immediately before execution of movement (Pfurtscheller, 1999). An ERS appears either after termination of the movement, or simultaneously to the ERD, but in other areas of the cortex. The decrease/increase is always measured in comparison to the power in a reference interval, for example a few seconds before the movement occurs. For classification there are several approaches used. The simplest one is by calculating the bandpower in a specific frequency band and consecutive discrimination via a Fisher linear discriminant analysis. Other classification strategies are support vector machines (SVM) (Solis-Escalante, 2008), principal component analysis (PCA) (Vallabhaneni, 2004), or common spatial patterns (CSP) (Guger, 2003) 2. Components and signals For BCI experiments the subject or the patient is connected via electrodes or sensors to a biosignal amplifier and a data acquisition unit (DAQ board) containing the analog-to-digital conversion (as shown in Figure 1). Then the data are passed to the real-time system to perform the feature extraction and classification. Important is that the real-time system Hardware/Software Components and Applications of BCIs 3 works fast enough to present feedback to the subject via a stimulation unit. The feedback represents the BCI output and allows the subject to learn the BCI control faster. RECENT ADVANCES IN BRAIN COMPUTER INTERFACE SYSTEMS Edited by Reza Fazel-Rezai Brain Computer Interface (BCI) technology, which provides a direct electronic interface and can convey messages and commands directly from the human brain to a computer. BCI technology involves monitoring conscious brain electrical activity via electroencephalogram (EEG) signals and detecting characteristics of EEG patterns via digital signal processing algorithms that the user generates to communicate. It has the potential to enable the physically disabled to perform many activities, thus improving their quality of life and productivity,allowing them more independence and reducing social costs. The challenge with BCI, however, is to extract the relevant patterns from the EEG signals produced by the brain each second. Contents Preface Chapter 1 IX Hardware/Software Components and Applications of BCIs 1 Christoph Guger, Günter Edlinger and Gunther Krausz Applied Advanced Classifiers for Brain Computer Interface 25 José Luis Martínez, Antonio Barrientos Feature Extraction by Mutual Information Based on Minimal-Redundancy-Maximal-Relevance Criterion and Its Application to Classifying EEG Signal for Brain-Computer Interfaces 67 Abbas Erfanian, Farid Oveisi and Ali Shadvar P300-based Brain-Computer Interface Paradigm Design 83 Reza Fazel-Rezai and Waqas Ahmad Brain Computer Interface Based on the Flash Onset and Offset Visual Evoked Potentials 99 Po-Lei Lee, Yu-Te Wu, Kuo-Kai Shyu and Jen-Chuen Hsieh Usability of Transient VEPs in BCIs 119 Natsue Yoshimura and Naoaki Itakura Visuo-Motor Tasks in a Brain-Computer Interface Analysis 135 Vito Logar and Aleš Belič A Two-Dimensional Brain-Computer Interface Associated With Human Natural Motor Control Dandan Huang, Xuedong Chen, Ding-Yu Fei and Ou Bai Advances in Non-Invasive Brain-Computer Interfaces for Control and Biometry 171 Nuno Figueiredo, Filipe Silva, Pétia Georgieva and Ana Tomé State of the Art in BCI Research: BCI Award 2010 193 Christoph Guger, Guangyu Bin, Xiaorong Gao, Jing Guo, Bo Hong, Tao Liu, Shangkai Gao, Cuntai Guan, Kai Keng Ang, Kok Soon Phua, Chuanchu Wang, Zheng Yang Chin, Haihong Zhang, Rongsheng Lin, Karen Sui Geok Chua, Christopher Kuah, Beng Ti Ang, Harry George, Andrea Kübler, Sebastian Halder, Adi Hösle, Jana Münßinger, Mark Palatucci, Dean Pomerleau, Geoff Hinton, Tom Mitchell, David B. Ryan, Eric W. Sellers, George Townsend, Steven M. Chase, Andrew S. Whitford, Andrew B. Schwartz, Kimiko Kawashima, Keiichiro Shindo, Junichi Ushiba, Meigen Liu and Gerwin Schalk Chapter 10 Preface Communication and the ability to interact with the environment are basic human needs. Millions of people worldwide suffer from such severe physical disabilities that they cannot even meet these basic needs. Even though they may have no motor mobility, however, the sensory and cognitive functions of the physically disabled are usually intact. This makes them good candidates for Brain Computer Interface (BCI) technology, which provides a direct electronic interface and can convey messages and commands directly from the human brain to a computer. BCI technology involves monitoring conscious brain electrical activity via electroencephalogram (EEG) signals and detecting characteristics of EEG patterns via digital signal processing algorithms that the user generates to communicate. It has the potential to enable the physically disabled to perform many activities, thus improving their quality of life and productivity, allowing them more independence and reducing social costs. The challenge with BCI, however, is to extract the relevant patterns from the EEG signals produced by the brain each second. A BCI system has an input, output and a signal processing algorithm that maps the inputs to the output. The following four major strategies are considered for the input of a BCI system: 1) the P300 wave of event related potentials (ERP), 2) steady state visual evoked potential (SSVEP), 3) slow cortical potentials and 4) motor imaginary. Recently, there has been a great progress in the development of novel paradigms for EEG signal recording, advanced methods for processing them, new applications for BCI systems and complete software and hardware packages used for BCI applications. In this book a few recent advances in these areas are discussed. In the first chapter hardware and software components along with several applications of BCI systems are discussed. In chapters 2 and 3 several signal processing methods for classifying EEG signals are presented. In chapter 4 a new paradigm for P300 BCI is compared with traditional P300 BCI paradigms. Chapters 5 and 6 show how a visual evoked potential (VEP)-based BCI works. In chapters 7 and 8 a visuo-motor-based and natural motor control-based BCI systems are discussed, respectively. New applications of BCI systems for control and biometry are discussed in chapter 9. Finally, the recent competition in BCI held in 2010 along with a short summary of the submitted projects are presented in Chapter 10. X Preface As the editor, I would like to thank all the authors of different chapters. Without your contributions, it would not be possible to have a quality book, help in growth of BCI systems and utilize them in real-world applications. Dr. Reza Fazel-Rezai University of North Dakota Grand Forks, ND, USA [email protected] 1 Hardware/Software Components and Applications of BCIs Christoph Guger, Günter Edlinger and Gunther Krausz g.tec medical engineering GmbH/ Guger Technologies OG Austria 1. Introduction Human-Computer interfaces can use different signals from the body in order to control external devices. Beside muscle activity (EMG-Electromyogram), eye movements (EOGElectrooculogram) and respiration also brain activity (EEG-Electroencephalogram) can be used as input signal. EEG-based brain-computer interface (BCI) systems are realized either with (i) slow cortical potentials, (ii) the P300 response, (iii) steady-state visual evoked potentials (SSVEP) or (iv) motor imagery. Potential shift of the scalp EEG over 0.5 – 10 s are called slow cortical potentials (SCPs). Reduced cortical activation goes ahead with positive SCPs, while negative SCPs are associated with movement and other functions involving cortical activation (Birbaumer, 2000). People are able to learn how to control these potentials, hence it is possible to use them for BCIs as Birbaumer and his colleagues did (Birbaumer, 2000, Elbert, 1980). The main disadvantage of this method is the extensive training time to learn how to control the SCPs. Users need to train in several 1-2 h sessions/week over weeks or months. The P300 wave was first discovered by Sutton (Sutton, 1965). It elicits when an unlikely event occurs randomly between events with high probability. In the EEG signal the P300 appears as a positive wave about 300 ms after stimulus onset. Its main usage in BCIs is for spelling devices, but one can also use it for control tasks (for example games (Finkea, 2009) or navigation (e.g. to move a computer-mouse (Citi, 2008)). When using P300 as a spelling device, a matrix of characters is shown to the subject. Now the rows and columns (or in some paradigms the single characters) of the matrix are flashing in random order, while the person concentrates only on the character he/she wants to spell. For better concentration, it is recommended to count how many times the character flashes. Every time the desired character flashes, a P300 wave occurs. As the detection of one single event would be imprecise, more than one trial (flashing of each character) has to be carried out to achieve a proper accuracy. Krusienski et al. (Krusienski, 2006) evaluated different classification techniques for the P300 speller, wherein the stepwise linear discriminant analysis (SWLDA) and the Fisher’s linear discriminant analysis provided the best overall performance and implementation characteristics. A recent study (Guger 2009), performed on 100 subjects, revealed an average accuracy level of 91.1%, with a spelling time of 28.8 s for one single character. Each character was selected out of a matrix of 36 characters. 2 Recent Advances in Brain-Computer Interface Systems Steady state visual evoked potentials (SSVEP)-based BCIs use several stationary flashing sources (e.g. flickering LEDs, or phase-reversing checkerboards), each of them flashing with another constant frequency. When a person gazes at one of these sources, the specific frequency component will increase in the measured EEG, over the occipital lobe. Hence, when using different light sources, each of them representing a predefined command, the person gives this command by gazing onto the source. The classification is either done by FFT-based spectrum comparison, preferably including also the harmonics (Müller-Putz, 2005), or via the canonical correlation analysis (CCA) (Lin, 2006). A third possibility is via the minimum energy approach which was published by O. Friman et.al. in 2007 (Friman, 2007) and requires no training. Typical SSVEP applications are made for navigation, for example Middendorf et al. (Middendorf, 2000) used SSVEPs to control the roll position of a flight simulator. The number of classes varies between two and eight, although Gao et al. (Gao, 2003) established an experiment with even 48 targets. Bakardijan et al. (Bakardijan, 2010) investigated SSVEP responses for frequencies between 5 and 84 Hz to find the strongest response between 5.6 Hz and 15.3 Hz peaking at 12 Hz. With their frequency-optimized-eight-command BCI they achieved a mean success rate of 98 % and an information transfer rate (ITR) of 50 bits/min. Bin et al. (Bin, 2009) reports of a six-target BCI with an average accuracy of 95.3% and an information transfer rate of 58 ± 9.6 bits/min. Although most SSVEP-based BCIs work with gaze shifting towards a source, recent studies (Allison, 2009, Zhang, 2010) proofed that only selective attention onto a pattern alone is sufficient for control. The latter paper achieved an overall classification accuracy of 72.6 +/16.1% after 3 training days. Therefore also severely disabled people, who are not able to move their eyes, can control an SSVEP-based BCI. When subjects perform or only imagine motor tasks, an event related desynchronization (ERD) (Pfurtscheller & Neuper, 1997) and an event related synchronization (ERS) is detectable by changes of EEG rhythms on electrodes close to the respective sensorimotor areas. The ERD is indicated by a decrease of power in the upper alpha band and lower beta band, starting 2 seconds before movement onset on the contra lateral hemisphere and becomes bilaterally symmetrical immediately before execution of movement (Pfurtscheller, 1999). An ERS appears either after termination of the movement, or simultaneously to the ERD, but in other areas of the cortex. The decrease/increase is always measured in comparison to the power in a reference interval, for example a few seconds before the movement occurs. For classification there are several approaches used. The simplest one is by calculating the bandpower in a specific frequency band and consecutive discrimination via a Fisher linear discriminant analysis. Other classification strategies are support vector machines (SVM) (Solis-Escalante, 2008), principal component analysis (PCA) (Vallabhaneni, 2004), or common spatial patterns (CSP) (Guger, 2003) 2. Components and signals For BCI experiments the subject or the patient is connected via electrodes or sensors to a biosignal amplifier and a data acquisition unit (DAQ board) containing the analog-to-digital conversion (as shown in Figure 1). Then the data are passed to the real-time system to perform the feature extraction and classification. Important is that the real-time system Hardware/Software Components and Applications of BCIs 3 works fast enough to present feedback to the subject via a stimulation unit. The feedback represents the BCI output and allows the subject to learn the BCI control faster.
Posted on: Sat, 04 Oct 2014 10:27:28 +0000
Trending Topics
Recently Viewed Topics
© 2015