Positive Technology Journal

Emotiv Systems has developed a new brain computer interface headset for video games and other uses. Emotiv’s president Tan Le claims that the headset will be on sale around the end of this year ($299).

Brain-computer interfaces in the continuum of consciousness.

Curr Opin Neurol. 2007 Dec;20(6):643-9

Authors: Kübler A, Kotchoubey B

PURPOSE OF REVIEW: To summarize recent developments and look at important future aspects of brain-computer interfaces. RECENT FINDINGS: Recent brain-computer interface studies are largely targeted at helping severely or even completely paralysed patients. The former are only able to communicate yes or no via a single muscle twitch, and the latter are totally nonresponsive. Such patients can control brain-computer interfaces and use them to select letters, words or items on a computer screen, for neuroprosthesis control or for surfing the Internet. This condition of motor paralysis, in which cognition and consciousness appear to be unaffected, is traditionally opposed to nonresponsiveness due to disorders of consciousness. Although these groups of patients may appear to be very alike, numerous transition states between them are demonstrated by recent studies. SUMMARY: All nonresponsive patients can be regarded on a continuum of consciousness which may vary even within short time periods. As overt behaviour is lacking, cognitive functions in such patients can only be investigated using neurophysiological methods. We suggest that brain-computer interfaces may provide a new tool to investigate cognition in disorders of consciousness, and propose a hierarchical procedure entailing passive stimulation, active instructions, volitional paradigms, and brain-computer interface operation.

Brain-computer communication: motivation, aim, and impact of exploring a virtual apartment.

IEEE Trans Neural Syst Rehabil Eng. 2007 Dec;15(4):473-82

Authors: Leeb R, Lee F, Keinrath C, Scherer R, Bischof H, Pfurtscheller G

The step away from a synchronized or cue-based brain-computer interface (BCI) and from laboratory conditions towards real world applications is very important and crucial in BCI research. This work shows that ten naive subjects can be trained in a synchronous paradigm within three sessions to navigate freely through a virtual apartment, whereby at every junction the subjects could decide by their own, how they wanted to explore the virtual environment (VE). This virtual apartment was designed similar to a real world application, with a goal-oriented task, a high mental workload, and a variable decision period for the subject. All subjects were able to perform long and stable motor imagery over a minimum time of 2 s. Using only three electroencephalogram (EEG) channels to analyze these imaginations, we were able to convert them into navigation commands. Additionally, it could be demonstrated that motivation is a very crucial factor in BCI research; motivated subjects perform much better than unmotivated ones.

Towards an independent brain-computer interface using steady state visual evoked potentials.

Clin Neurophysiol. 2008 Feb;119(2):399-408

Authors: Allison BZ, McFarland DJ, Schalk G, Zheng SD, Jackson MM, Wolpaw JR

OBJECTIVE: Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze. METHODS: Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs. RESULTS: Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control. CONCLUSIONS: The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user. SIGNIFICANCE: SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

Towards an independent brain-computer interface using steady state visual evoked potentials.

Clin Neurophysiol. 2007 Dec 10;

Authors: Allison BZ, McFarland DJ, Schalk G, Zheng SD, Jackson MM, Wolpaw JR

OBJECTIVE: Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze. METHODS: Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs. RESULTS: Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control. CONCLUSIONS: The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user. SIGNIFICANCE: SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

Towards an independent brain-computer interface using steady state visual evoked potentials.

Clin Neurophysiol. 2007 Dec 10;

Authors: Allison BZ, McFarland DJ, Schalk G, Zheng SD, Jackson MM, Wolpaw JR

OBJECTIVE: Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze. METHODS: Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs. RESULTS: Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control. CONCLUSIONS: The data demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user. SIGNIFICANCE: SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.

Self-initiation of EEG-based brain-computer communication using the heart rate response.

J Neural Eng. 2007 Dec;4(4):L23-9

Authors: Scherer R, Müller-Putz GR, Pfurtscheller G

Self-initiation, that is the ability of a brain-computer interface (BCI) user to autonomously switch on and off the system, is a very important issue. In this work we analyze whether the respiratory heart rate response, induced by brisk inspiration, can be used as an additional communication channel. After only 20 min of feedback training, ten healthy subjects were able to self-initiate and operate a 4-class steady-state visual evoked potential-based (SSVEP) BCI by using one bipolar ECG and one bipolar EEG channel only. Threshold detection was used to measure a beat-to-beat heart rate increase. Despite this simple method, during a 30 min evaluation period on average only 2.9 non-intentional switches (heart rate changes) were detected.

 

Electrodes attached to the patient's scalp measure the brain's electrical signals, which are amplified and transmitted to a computer. Highly efficient algorithms analyze these signals using a self-learning technique. The software is capable of detecting changes in brain activity that take place even before a movement is carried out. It can recognize and distinguish between the patterns of signals that correspond to an intention to raise the left or right hand, and extract them from the pulses being fired by millions of other neurons in the brain. These neural signal patterns are then converted into control instructions for the computer. "The aim of the project is to help people with severe motor disabilities to carry out everyday tasks. The advantage of our technology is that it is capable of translating an intended action directly into instructions for the computer," says team leader Florin Popescu. The Brain2Robot project has been granted around 1.3 million euros in research funding under the EU's sixth Framework Programme (FP6). Its focus lies on developing medical applications, in particular control systems for prosthetics, personal robots and wheelchairs. The researchers have also developed a "thought-controlled typewriter", a communication device that enables severely paralyzed patients to pick out letters of the alphabet and write texts. The robot arm could be ready for commercialization in just a few years' time.

Press release:Brain2Robot

Project page:Brain2Robot

Virtual reality hardware and graphic display options for brain-machine interfaces.

J Neurosci Methods. 2007 Sep 29;

Authors: Marathe AR, Carey HL, Taylor DM

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)

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.

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.