Positive Technology Journal

A Low Cost Human Computer Interface based on Eye Tracking.

Conf Proc IEEE Eng Med Biol Soc. 2006;1:3226-3229

Authors: Hiley JB, Redekopp AH, Fazel-Rezai R

This paper describes the implementation of a human computer interface based on eye tracking. Current commercially available systems exist, but have limited use due mainly to their large cost. The system described in this paper was designed to be a low cost and unobtrusive. The technique was video-oculography assisted by corneal reflections. An off-the shelf CCD webcam was used to capture images. The images were analyzed in software to extract key features of the eye. The users gaze point was then calculated based on the relative position of these features. The system is capable of calculating eye-gaze in real-time to provide a responsive interaction. A throughput of eight gaze points per second was achieved. The accuracy of the fixations based on the calculated eye-gazes were within 1 cm of the on-screen gaze location. By developing a low-cost system, this technology is made accessible to a wider range of applications.

A brain-computer interface with vibrotactile biofeedback for haptic information.

J Neuroengineering Rehabil. 2007 Oct 17;4(1):40

Authors: Chatterjee A, Aggarwal V, Ramos A, Acharya S, Thakor NV

ABSTRACT: BACKGROUND: It has been suggested that Brain-Computer Interfaces (BCI) may one day be suitable for controlling a neuroprosthesis. For closed-loop operation of BCI, a tactile feedback channel that is compatible with neuroprosthetic applications is desired. Operation of an EEG-based BCI using only vibrotactile feedback, a commonly used method to convey haptic senses of contact and pressure, is demonstrated with a high level of accuracy. METHODS: A Mu-rhythm based BCI using a motor imagery paradigm was used to control the position of a virtual cursor. The cursor position was shown visually as well as transmitted haptically by modulating the intensity of a vibrotactile stimulus to the upper limb. A total of six subjects operated the BCI in a two-stage targeting task, receiving only vibrotactile biofeedback of performance. The location of the vibration was also systematically varied between the left and right arms to investigate location-dependent effects on performance. RESULTS AND CONCLUSIONS: Subjects are able to control the BCI using only vibrotactile feedback with an average accuracy of 56% and as high as 72%. These accuracies are significantly higher than the 15% predicted by random chance if the subject had no voluntary control of their Mu-rhythm. The results of this study demonstrate that vibrotactile feedback is an effective biofeedback modality to operate a BCI using motor imagery. In addition, the study shows that placement of the vibrotactile stimulation on the biceps ipsilateral or contralateral to the motor imagery introduces a significant bias in the BCI accuracy. This bias is consistent with a drop in performance generated by stimulation of the contralateral limb. Users demonstrated the capability to overcome this bias with training.

Via NewScientist Tech

Microsoft plans to use EEG signals for task classification and activity recognition of users. The software giant has applied a new patent for a method that will allow to separate useful cognitive information from EEG artifacts and noise.

Read the full Microsoft mind reading patent application

Great catch by Pink Tentacle: researchers at Keio University Biomedical Engineering Laboratory have developed a brain-computer interface that allows the user controlling his avatar in Second Life by thinking about movements — the avatar walks forward when the user thinks about moving his/her own feet, and it turns right and left when the user imagines moving his/her right and left arms. A future goal is to improve the system and make Second Life avatars perform more complex movements and gestures.

video (14,9 MB)

A Low Cost Human Computer Interface based on Eye Tracking.

Conf Proc IEEE Eng Med Biol Soc. 2006;1(1):3226-3229

Authors: Hiley JB, Redekopp AH, Fazel-Rezai R

This paper describes the implementation of a human computer interface based on eye tracking. Current commercially available systems exist, but have limited use due mainly to their large cost. The system described in this paper was designed to be a low cost and unobtrusive. The technique was video-oculography assisted by corneal reflections. An off-the shelf CCD webcam was used to capture images. The images were analyzed in software to extract key features of the eye. The users gaze point was then calculated based on the relative position of these features. The system is capable of calculating eye-gaze in real-time to provide a responsive interaction. A throughput of eight gaze points per second was achieved. The accuracy of the fixations based on the calculated eye-gazes were within 1 cm of the on-screen gaze location. By developing a low-cost system, this technology is made accessible to a wider range of applications.

Via NewScientist.com

US company Ambient has unveiled a motorized wheelchair that moves when the operator thinks of particular words. The wheelchair works by intercepting signals sent from their brain to their voice box, even when no sound is actually produced.

The wheelchair was developed in collaboration with the Rehabilitation Institute of Chicago. It could help people with spinal injuries, or neurological problems like cerebral palsy or motor neuron disease, operate computers and other equipment despite serious problems with muscle control.

Brain-computer interface: a reciprocal self-regulated neuromodulation.

Acta Neurochir Suppl. 2007;97(Pt 2):555-9

Authors: Angelakis E, Hatzis A, Panourias IG, Sakas DE

Brain-computer interface (BCI) is a system that records brain activity and process it through a computer, allowing the individual whose activity is recorded to monitor this activity at the same time. Applications of BCIs include assistive modules for severely paralyzed patients to help them control external devices or to communicate, as well as brain biofeedback to self regulate brain activity for treating epilepsy, attention-deficit hyperactivity disorder (ADHD), anxiety, and other psychiatric conditions, or to enhance cognitive performance in healthy individuals. The vast majority of BCIs utilizes non-invasive scalp recorded electroencephalographic (EEG) signals, but other techniques like invasive intracortical EEG, or near-infrared spectroscopy measuring brain blood oxygenation are tried experimentally.

An MEG-based brain-computer interface (BCI).

Neuroimage. 2007 Jul 1;36(3):581-93

Authors: Mellinger J, Schalk G, Braun C, Preissl H, Rosenstiel W, Birbaumer N, Kübler A

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

EEG-based assessment of driver cognitive responses in a dynamic virtual-reality driving environment.

IEEE Trans Biomed Eng. 2007 Jul;54(7):1349-52

Authors: Lin CT, Chung IF, Ko LW, Chen YC, Liang SF, Duann JR

Accidents caused by errors and failures in human performance among traffic fatalities have a high death rate and become an important issue in public security. They are mainly caused by the failures of the drivers to perceive the changes of the traffic lights or the unexpected conditions happening accidentally on the roads. In this paper, we devised a quantitative analysis for assessing driver's cognitive responses by investigating the neurobiological information underlying electroencephalographic (EEG) brain dynamics in traffic-light experiments in a virtual-reality (VR) dynamic driving environment. The VR technique allows subjects to interact directly with the moving virtual environment instead of monotonic auditory and visual stimuli, thereby provides interactive and realistic tasks without the risk of operating on an actual machine. Independent component analysis (ICA) is used to separate and extract noise-free ERP signals from the multi-channel EEG signals. A temporal filter is used to solve the time-alignment problem of ERP features and principle component analysis (PCA) is used to reduce feature dimensions. The dimension-reduced features are then input to a self-constructing neural fuzzy inference network (SONFIN) to recognize different brain potentials stimulated by red/green/yellow traffic events, the accuracy can be reached 87% in average eight subjects in this visual-stimuli ERP experiment. It demonstrates the feasibility of detecting and analyzing multiple streams of ERP signals that represent operators' cognitive states and responses to task events.

Brain-computer interface systems: progress and prospects.

Expert Rev Med Devices. 2007 Jul;4(4):463-474

Authors: Allison BZ, Wolpaw EW, Wolpaw JR

Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.

Via Networked Performance

From Pentagon to Merge Next-Gen Binoculars With Soldiers' Brains by Sharon Weinberger, Wired:

"U.S. Special Forces may soon have a strange and powerful new weapon in their arsenal: a pair of high-tech binoculars 10 times more powerful than anything available today, augmented by an alerting system that literally taps the wearer's prefrontal cortex to warn of furtive threats detected by the soldier's subconscious.

In a new effort dubbed "Luke's Binoculars" -- after the high-tech binoculars Luke Skywalker uses in Star Wars -- the Defense Advanced Research Projects Agency is setting out to create its own version of this science-fiction hardware. And while the Pentagon's R&D arm often focuses on technologies 20 years out, this new effort is dramatically different -- Darpa says it expects to have prototypes in the hands of soldiers in three years...

The most far-reaching component of the binocs has nothing to do with the optics: it's Darpa's aspirations to integrate EEG electrodes that monitor the wearer's neural signals, cueing soldiers to recognize targets faster than the unaided brain could on its own. The idea is that EEG can spot "neural signatures" for target detection before the conscious mind becomes aware of a potential threat or target." 

A MEG-based brain-computer interface (BCI).

Neuroimage. 2007 Mar 27;

Authors: Mellinger J, Schalk G, Braun C, Preissl H, Rosenstiel W, Birbaumer N, Kübler A

Brain-computer interfaces (BCIs) allow for communicating intentions by mere brain activity, not involving muscles. Thus, BCIs may offer patients who have lost all voluntary muscle control the only possible way to communicate. Many recent studies have demonstrated that BCIs based on electroencephalography (EEG) can allow healthy and severely paralyzed individuals to communicate. While this approach is safe and inexpensive, communication is slow. Magnetoencephalography (MEG) provides signals with higher spatiotemporal resolution than EEG and could thus be used to explore whether these improved signal properties translate into increased BCI communication speed. In this study, we investigated the utility of an MEG-based BCI that uses voluntary amplitude modulation of sensorimotor mu and beta rhythms. To increase the signal-to-noise ratio, we present a simple spatial filtering method that takes the geometric properties of signal propagation in MEG into account, and we present methods that can process artifacts specifically encountered in an MEG-based BCI. Exemplarily, six participants were successfully trained to communicate binary decisions by imagery of limb movements using a feedback paradigm. Participants achieved significant mu rhythm self control within 32 min of feedback training. For a subgroup of three participants, we localized the origin of the amplitude modulated signal to the motor cortex. Our results suggest that an MEG-based BCI is feasible and efficient in terms of user training.

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.

Classification of motor imagery by means of cortical current density estimation and Von Neumann entropy.

J Neural Eng. 2007 Jun;4(2):17-25

Authors: Kamousi B, Amini AN, He B

The goal of the present study is to employ the source imaging methods such as cortical current density estimation for the classification of left- and right-hand motor imagery tasks, which may be used for brain-computer interface (BCI) applications. The scalp recorded EEG was first preprocessed by surface Laplacian filtering, time-frequency filtering, noise normalization and independent component analysis. Then the cortical imaging technique was used to solve the EEG inverse problem. Cortical current density distributions of left and right trials were classified from each other by exploiting the concept of Von Neumann entropy. The proposed method was tested on three human subjects (180 trials each) and a maximum accuracy of 91.5% and an average accuracy of 88% were obtained. The present results confirm the hypothesis that source analysis methods may improve accuracy for classification of motor imagery tasks. The present promising results using source analysis for classification of motor imagery enhances our ability of performing source analysis from single trial EEG data recorded on the scalp, and may have applications to improved BCI systems.

Via pasta & vinegar 

An article in The Economist about brain-controlled devices and games.

From the article:

At the moment, EEG's uses are mostly medical. Though the output of the electrodes is a set of crude brain waves, enough is now known about the healthy patterns of these waves for changes in them to be used to diagnose unhealthy abnormalities. Yet, because parts of a person's grey matter exhibit increased electric activity when they respond to stimuli or prepare for movements, there has always been the lingering hope that EEG might also manifest someone's thoughts in a machine-readable form that could be used for everyday purposes.

To realise that hope means solving two problems—one of hardware and one of software. The hardware problem is that existing EEG requires a helmet with as many as 120 electrodes in it, and that these electrodes have to be affixed to the scalp with a gel. The software problem is that many different types of brain waves have to be interpreted simultaneously and instantly. That is no mean computing task.

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.

From Practical Neurotechnology 

Scientists at the Robot Engineering Technology Research Center of east China's Shandong University of Science and Technology claim to have implanted micro electrodes in the brain of a pigeon so they can command it to fly right or left or up or down.

The implants stimulated different areas of the pigeon's brain according to signals sent by the scientists via computer, and forced the bird to comply with their commands.

http://blog.wired.com/defense/2007/02/cyborg_flying_r.html
http://tenementpalm.blogspot.com/2007/02/psb-buys-tiny-ge...
http://english.people.com.cn/200702/27/eng20070227_352761...

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.