Positive Technology Journal

Recent progress in brain science, especially in non-invasive methods, has enabled quantitative evaluation of human behavior and operation of electronic communication devices by direct brain-derived signals. Neural activities in cerebral cortex and peripheral nerves have been analyzed using imaging techniques, providing us with several models associated with human recognition and action. These advancements have lowered the barrier to realize the seamless communication between human and machine. In view of these circumstances, an interdisciplinary and comprehensive approach incorporating basic research is important to develop future brain-communication networks and to facilitate human communication effectively. The approach should include biosignal-based communication network technology, novel intelligent device technology, and preference-based neuromarketing technology. This special section on Brain Communication is planned to review and mine for relevant research in the IEICE Transactions on Communications.

Topics

• Multiunit recording and analysis technology and its applications to interface
  
• Brain-machine interface and neural prosthesis
  
• Biofeedback control in biomechanical system
  
• Brain functional imaging and signal processing techniques
  
• Communication of thoughts and kansei
  
• Memory and learning models in cerebral cortex and its application to information communication
  
• Analysis of human behavior and its application to information communication engineering
  
• Neurodecoding and its application to communication
  
• Sensor network and its fusion technology
  
• Sensor technology and biomechanics
  
• Neuroinfomatics and retrieval methods
  
• Network management and control incorporating brain computation 
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An efficient P300-based brain-computer interface for disabled subjects.

J Neurosci Methods. 2007 Mar 13;

Authors: Hoffmann U, Vesin JM, Ebrahimi T, Diserens K

A brain-computer interface (BCI) is a communication system that translates brain-activity into commands for a computer or other devices. In other words, a BCI allows users to act on their environment by using only brain-activity, without using peripheral nerves and muscles. In this paper, we present a BCI that achieves high classification accuracy and high bitrates for both disabled and able-bodied subjects. The system is based on the P300 evoked potential and is tested with five severely disabled and four able-bodied subjects. For four of the disabled subjects classification accuracies of 100% are obtained. The bitrates obtained for the disabled subjects range between 10 and 25bits/min. The effect of different electrode configurations and machine learning algorithms on classification accuracy is tested. Further factors that are possibly important for obtaining good classification accuracy in P300-based BCI systems for disabled subjects are discussed.

Electrocorticographically controlled brain-computer interfaces using motor and sensory imagery in patients with temporary subdural electrode implants. Report of four cases.

J Neurosurg. 2007 Mar;106(3):495-500

Authors: Felton EA, Wilson JA, Williams JC, Garell PC

Brain-computer interface (BCI) technology can offer individuals with severe motor disabilities greater independence and a higher quality of life. The BCI systems take recorded brain signals and translate them into real-time actions, for improved communication, movement, or perception. Four patient participants with a clinical need for intracranial electrocorticography (ECoG) participated in this study. The participants were trained over multiple sessions to use motor and/or auditory imagery to modulate their brain signals in order to control the movement of a computer cursor. Participants with electrodes over motor and/or sensory areas were able to achieve cursor control over 2 to 7 days of training. These findings indicate that sensory and other brain areas not previously considered ideal for ECoG-based control can provide additional channels of control that may be useful for a motor BCI.

Breaking the silence: brain-computer interfaces (BCI) for communication and motor control.

Psychophysiology. 2006 Nov;43(6):517-32

Authors: Birbaumer N

Brain-computer interfaces (BCI) allow control of computers or external devices with regulation of brain activity alone. Invasive BCIs, almost exclusively investigated in animal models using implanted electrodes in brain tissue, and noninvasive BCIs using electrophysiological recordings in humans are described. Clinical applications were reserved with few exceptions for the noninvasive approach: communication with the completely paralyzed and locked-in syndrome with slow cortical potentials, sensorimotor rhythm and P300, and restoration of movement and cortical reorganization in high spinal cord lesions and chronic stroke. It was demonstrated that noninvasive EEG-based BCIs allow brain-derived communication in paralyzed and locked-in patients but not in completely locked-in patients. At present no firm conclusion about the clinical utility of BCI for the control of voluntary movement can be made. Invasive multielectrode BCIs in otherwise healthy animals allowed execution of reaching, grasping, and force variations based on spike patterns and extracellular field potentials. The newly developed fMRI-BCIs and NIRS-BCIs, like EEG BCIs, offer promise for the learned regulation of emotional disorders and also disorders of young children.

From Wired 

read the full story on Wired 

Volitional control of neural activity: implications for brain-computer interfaces.

J Physiol. 2007 Jan 18;

Authors: Fetz EE

Successful operation of brain-computer interfaces [BCI] and brain-machine interfaces [BMI] depends significantly on the degree to which neural activity can be volitionally controlled. This paper reviews evidence for such volitional control in a variety of neural signals, with particular emphasis on the activity of cortical neurons. Some evidence comes from conventional experiments that reveal volitional modulation in neural activity related to behaviors, including real and imagined movements, cognitive imagery and shifts of attention. More direct evidence comes from studies on operant conditioning of neural activity using biofeedback, and from BCI/BMI studies in which neural activity controls cursors or peripheral devices. Limits in the degree of accuracy of control in the latter studies can be attributed to several possible factors. Some of these factors, particularly limited practice time, can be addressed with long-term implanted BCIs. Preliminary observations with implanted circuits implementing recurrent BCIs are summarized.

Brain-Computer-Interfaces(BCI): Communication and Restoration of Movement in Paralysis.

J Physiol. 2007 Jan 18;

Authors: Birbaumer N, Cohen LG

The review describes the status of brain-computer- or brain-machine-interface research. We focus on non-invasive brain-computer-interfaces (BCIs) and their clinical utility for direct brain communication in paralysis and motor restoration in stroke. A large gap between the promises of invasive animal and invasive human BCI-preparations and the clinical reality characterises the literature: while intact monkeys learn to execute more or less complex upper limb movements with spike patterns from motor brain regions alone without concomitant peripheral motor activity usually after extensive training, clinical applications in human diseases such as Amyotrophic Lateral Sclerosis and Paralysis from stroke or spinal cord lesions shows only limited success with the exception of verbal communication in paralysed and locked-in patients: BCIs based on electroencephalographic potentials or oscillations are ready to undergo large clinical studies and commercial production as an adjunct or a major assisted communication device for paralysed and locked-in patients. However, attempts to train completely locked-in patients with BCI-communication after entering the complete locked-in state with no remaining eye-movement failed. We propose that a lack of contingencies between goal directed thoughts and intentions may be at the heart of this problem. Experiments with chronically curarised rats support our hypothesis, operant conditioning and voluntary control of autonomic physiological functions turned out to be impossible in this preparation. In addition to assisted communication BCIs consisting of operant learning of EEG slow cortical potentials and sensorimotor rhythm was demonstrated to be successful in drug resistant focal epilepsy and attention deficit disorder. First studies of non-invasive BCIs using sensorimotor rhythm of the EEG and MEG in restoration of paralysed hand movements in chronic stroke and single cases of high spinal cord lesions show some promise but need extensive evaluation in well-controlled experiments. Invasive BMIs based on neuronal spike patterns, local field potentials or electrocorticogram may constitute the strategy of choice in severe cases of stroke and spinal cord paralysis. Future directions of BCI research should include the regulation of brain metabolism and blood flow and electrical and magnetic stimulation of the human brain (invasive and non-invasive). A series of studies using BOLD-response regulation with functional magnetic resonance imaging (fMRI) and near infrared spectroscopy demonstrated a tight correlation between voluntary changes in brain metabolism and behaviour.

Via Mind Hacks

Researchers at the University of Washington Neural Systems Lab have created a humanoid robot you can control with your thoughts via a EEG-based non-invasive brain-computer interface.

Link to Neural Systems Lab robot info page

Temporal classification of multichannel near-infrared spectroscopy signals of motor imagery for developing a brain-computer interface.

Neuroimage. 2006 Dec 28;

Authors: Sitaram R, Zhang H, Guan C, Thulasidas M, Hoshi Y, Ishikawa A, Shimizu K, Birbaumer N

There has been an increase in research interest for brain-computer interface (BCI) technology as an alternate mode of communication and environmental control for the disabled, such as patients suffering from amyotrophic lateral sclerosis (ALS), brainstem stroke and spinal cord injury. Disabled patients with appropriate physical care and cognitive ability to communicate with their social environment continue to live with a reasonable quality of life over extended periods of time. Near-infrared spectroscopy is a non-invasive technique which utilizes light in the near-infrared range (700 to 1000 nm) to determine cerebral oxygenation, blood flow and metabolic status of localized regions of the brain. In this paper, we describe a study conducted to test the feasibility of using multichannel NIRS in the development of a BCI. We used a continuous wave 20-channel NIRS system over the motor cortex of 5 healthy volunteers to measure oxygenated and deoxygenated hemoglobin changes during left-hand and right-hand motor imagery. We present results of signal analysis indicating that there exist distinct patterns of hemodynamic responses which could be utilized in a pattern classifier towards developing a BCI. We applied two different pattern recognition algorithms separately, Support Vector Machines (SVM) and Hidden Markov Model (HMM), to classify the data offline. SVM classified left-hand imagery from right-hand imagery with an average accuracy of 73% for all volunteers, while HMM performed better with an average accuracy of 89%. Our results indicate potential application of NIRS in the development of BCIs. We also discuss here future extension of our system to develop a word speller application based on a cursor control paradigm incorporating online pattern classification of single-trial NIRS data.

EMG and EOG artifacts in brain computer interface systems: A survey.

Clin Neurophysiol. 2006 Dec 12;

Authors: Fatourechi M, Bashashati A, Ward RK, Birch GE

It is widely accepted in the brain computer interface (BCI) research community that neurological phenomena are the only source of control in any BCI system. Artifacts are undesirable signals that can interfere with neurological phenomena. They may change the characteristics of neurological phenomena or even be mistakenly used as the source of control in BCI systems. Electrooculography (EOG) and electromyography (EMG) artifacts are considered among the most important sources of physiological artifacts in BCI systems. Currently, however, there is no comprehensive review of EMG and EOG artifacts in BCI literature. This paper reviews EOG and EMG artifacts associated with BCI systems and the current methods for dealing with them. More than 250 refereed journal and conference papers are reviewed and categorized based on the type of neurological phenomenon used and the methods employed for handling EOG and EMG artifacts. This study reveals weaknesses in BCI studies related to reporting the methods of handling EMG and EOG artifacts. Most BCI papers do not report whether or not they have considered the presence of EMG and EOG artifacts in the brain signals. Only a small percentage of BCI papers report automated methods for rejection or removal of artifacts in their systems. As the lack of dealing with artifacts may result in the deterioration of the performance of a particular BCI system during practical applications, it is necessary to develop automatic methods to handle artifacts or to design BCI systems whose performance is robust to the presence of artifacts.

Re-blogged from infoaesthetics 

Public mood ring is a physical installation inspired by the idea of a ring that translates the bearer's emotional condition into a changeable color hue...

link to the original post

Via ScienceDaily

University of Washington researchers have developed a brain-computer interface that allows humans to control the actions of robots through commands generated by analysis of EEG signals


Link 

Online control of a brain-computer interface using phase synchronization.

IEEE Trans Biomed Eng. 2006 Dec;53(12 Pt 1):2501-6

Authors: Brunner C, Scherer R, Graimann B, Supp G, Pfurtscheller G

Currently, almost all brain-computer interfaces (BCIs) ignore the relationship between phases of electroencephalographic signals detected from different recording sites (i.e., electrodes). The vast majority of BCI systems rely on feature vectors derived from e.g., bandpower or univariate adaptive autoregressive (AAR) parameters. However, ample evidence suggests that additional information is obtained by quantifying the relationship between signals of single electrodes, which might provide innovative features for future BCI systems. This paper investigates one method to extract the degree of phase synchronization between two electroencephalogram (EEG) signals by calculating the so-called phase locking value (PLV). In our offline study, several PLV-based features were acquired and the optimal feature set was selected for each subject individually by a feature selection algorithm. The online sessions with three trained subjects revealed that all subjects were able to control three mental states (motor imagery of left hand, right hand, and foot, respectively) with single-trial accuracies between 60% and 66.7% (33% would be expected by chance) throughout the whole session.

Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation.

J Physiol. 2006 Nov 16;

Authors: Dobkin BH

Brain-computer interfaces are a rehabilitation tool for tetraplegic patients that aim to improve quality of life by augmenting communication, control of the environment, and self-care. The neurobiology of both rehabilitation and BCI control depends upon learning to modify the efficacy of spared neural ensembles that represent movement, sensation, and cognition through progressive practice with feedback and reward. To serve patients, BCI systems must become safe, reliable, cosmetically acceptable, quickly mastered with minimal ongoing technical support, and highly accurate even in the face of mental distractions and the uncontrolled environment beyond a laboratory. BCI technologies may raise ethical concerns if their availability affects the decisions of patients who become locked-in with brain stem stroke or amyotrophic lateral sclerosis to be sustained with ventilator support. If BCI technology becomes flexible and affordable, volitional control of cortical signals could be employed for the rehabilitation of motor and cognitive impairments in hemiplegic or paraplegic patients by offering on-line feedback about cortical activity associated with mental practice, motor intention, and other neural recruitment strategies during progressive task-oriented practice. Clinical trials with measures of quality of life will be necessary to demonstrate the value of near-term and future BCI applications.

Re-blogged from Pink Tentacle

Hitachi has successfully tested a brain-machine interface that allows users to turn power switches on and off with their mind. Relying on optical topography, a neuroimaging technique that uses near-infrared light to map blood concentration in the brain, the system can recognize the changes in brain blood flow associated with mental activity and translate those changes into voltage signals for controlling external devices. In the experiments, test subjects were able to activate the power switch of a model train by performing mental arithmetic and reciting items from memory.

Link to Pink Tentacle post

Via Neurobot 

Brain-machine interfaces promise to aid paralyzed patients by re-routing movement-related signals around damaged parts of the nervous system.

a web focus by Nature

Brain-computer interface technology as a tool to augment plasticity and outcomes for neurological rehabilitation.

J Physiol. 2006 Nov 9

Authors: Dobkin BH

Brain-computer interfaces are a rehabilitation tool for tetraplegic patients that aim to improve quality of life by augmenting communication, control of the environment, and self-care. The neurobiology of both rehabilitation and BCI control depends upon learning to modify the efficacy of spared neural ensembles that represent movement, sensation, and cognition through progressive practice with feedback and reward. To serve patients, BCI systems must become safe, reliable, cosmetically acceptable, quickly mastered with minimal ongoing technical support, and highly accurate even in the face of mental distractions and the uncontrolled environment beyond a laboratory. BCI technologies may raise ethical concerns if their availability affects the decisions of patients who become locked-in with brain stem stroke or amyotrophic lateral sclerosis to be sustained with ventilator support. If BCI technology becomes flexible and affordable, volitional control of cortical signals could be employed for the rehabilitation of motor and cognitive impairments in hemiplegic or paraplegic patients by offering on-line feedback about cortical activity associated with mental practice, motor intention, and other neural recruitment strategies during progressive task-oriented practice. Clinical trials with measures of quality of life will be necessary to demonstrate the value of near-term and future BCI applications.

An extended EM algorithm for joint feature extraction and classification in brain-computer interfaces.

Neural Comput. 2006 Nov;18(11):2730-61

Authors: Li Y, Guan C

For many electroencephalogram (EEG)-based brain-computer interfaces (BCIs), a tedious and time-consuming training process is needed to set parameters. In BCI Competition 2005, reducing the training process was explicitly proposed as a task. Furthermore, an effective BCI system needs to be adaptive to dynamic variations of brain signals; that is, its parameters need to be adjusted online. In this article, we introduce an extended expectation maximization (EM) algorithm, where the extraction and classification of common spatial pattern (CSP) features are performed jointly and iteratively. In each iteration, the training data set is updated using all or part of the test data and the labels predicted in the previous iteration. Based on the updated training data set, the CSP features are reextracted and classified using a standard EM algorithm. Since the training data set is updated frequently, the initial training data set can be small (semi-supervised case) or null (unsupervised case). During the above iterations, the parameters of the Bayes classifier and the CSP transformation matrix are also updated concurrently. In online situations, we can still run the training process to adjust the system parameters using unlabeled data while a subject is using the BCI system. The effectiveness of the algorithm depends on the robustness of CSP feature to noise and iteration convergence, which are discussed in this article. Our proposed approach has been applied to data set IVa of BCI Competition 2005. The data analysis results show that we can obtain satisfying prediction accuracy using our algorithm in the semisupervised and unsupervised cases. The convergence of the algorithm and robustness of CSP feature are also demonstrated in our data analysis.

Neural internet: web surfing with brain potentials for the completely paralyzed.

Neurorehabil Neural Repair. 2006 Dec;20(4):508-15

Authors: Karim AA, Hinterberger T, Richter J, Mellinger J, Neumann N, Flor H, Kübler A, Birbaumer N

Neural Internet is a new technological advancement in brain-computer interface research, which enables locked-in patients to operate a Web browser directly with their brain potentials. Neural Internet was successfully tested with a locked-in patient diagnosed with amyotrophic lateral sclerosis rendering him the first paralyzed person to surf the Internet solely by regulating his electrical brain activity. The functioning of Neural Internet and its clinical implications for motor-impaired patients are highlighted.

Combined optimization of spatial and temporal filters for improving brain-computer interfacing.

IEEE Trans Biomed Eng. 2006 Nov;53(11):2274-81

Authors: Dornhege G, Blankertz B, Krauledat M, Losch F, Curio G, Müller KR

Brain-computer interface (BCI) systems create a novel communication channel from the brain to an output device by bypassing conventional motor output pathways of nerves and muscles. Therefore they could provide a new communication and control option for paralyzed patients. Modern BCI technology is essentially based on techniques for the classification of single-trial brain signals. Here we present a novel technique that allows the simultaneous optimization of a spatial and a spectral filter enhancing discriminability rates of multichannel EEG single-trials. The evaluation of 60 experiments involving 22 different subjects demonstrates the significant superiority of the proposed algorithm over to its classical counterpart: the median classification error rate was decreased by 11%. Apart from the enhanced classification, the spatial and/or the spectral filter that are determined by the algorithm can also be used for further analysis of the data, e.g., for source localization of the respective brain rhythms.