Positive Technology Journal

Have a look at this demo of an electric wheelchair under control of an Emotiv EEG/EMG headset. The control system developed by Cuitech, detects when the user winks or smiles, and translates these signals into commands to control the wheelchair.

Neurofeedback-based motor imagery training for brain-computer interface (BCI).

J Neurosci Methods. 2009 Apr 30;179(1):150-6

Authors: Hwang HJ, Kwon K, Im CH

In the present study, we propose a neurofeedback-based motor imagery training system for EEG-based brain-computer interface (BCI). The proposed system can help individuals get the feel of motor imagery by presenting them with real-time brain activation maps on their cortex. Ten healthy participants took part in our experiment, half of whom were trained by the suggested training system and the others did not use any training. All participants in the trained group succeeded in performing motor imagery after a series of trials to activate their motor cortex without any physical movements of their limbs. To confirm the effect of the suggested system, we recorded EEG signals for the trained group around sensorimotor cortex while they were imagining either left or right hand movements according to our experimental design, before and after the motor imagery training. For the control group, we also recorded EEG signals twice without any training sessions. The participants' intentions were then classified using a time-frequency analysis technique, and the results of the trained group showed significant differences in the sensorimotor rhythms between the signals recorded before and after training. Classification accuracy was also enhanced considerably in all participants after motor imagery training, compared to the accuracy before training. On the other hand, the analysis results for the control EEG data set did not show consistent increment in both the number of meaningful time-frequency combinations and the classification accuracy, demonstrating that the suggested system can be used as a tool for training motor imagery tasks in BCI applications. Further, we expect that the motor imagery training system will be useful not only for BCI applications, but for functional brain mapping studies that utilize motor imagery tasks as well.

Neurofeedback and brain-computer interface clinical applications.

Int Rev Neurobiol. 2009;86:107-17

Authors: Birbaumer N, Ramos Murguialday A, Weber C, Montoya P

Most of the research devoted to BMI development consists of methodological studies comparing different online mathematical algorithms, ranging from simple linear discriminant analysis (LDA) (Dornhege et al., 2007) to nonlinear artificial neural networks (ANNs) or support vector machine (SVM) classification. Single cell spiking for the reconstruction of hand movements requires different statistical solutions than electroencephalography (EEG)-rhythm classification for communication. In general, the algorithm for BMI applications is computationally simple and differences in classification accuracy between algorithms used for a particular purpose are small. Only a very limited number of clinical studies with neurological patients are available, most of them single case studies. The clinical target populations for BMI-treatment consist primarily of patients with amyotrophic lateral sclerosis (ALS) and severe CNS damage including spinal cord injuries and stroke resulting in substantial deficits in communication and motor function. However, an extensive body of literature started in the 1970s using neurofeedback training. Such training implemented to control various EEG-measures provided solid evidence of positive effects in patients with otherwise pharmacologically intractable epilepsy, attention deficit disorder, and hyperactivity ADHD. More recently, the successful introduction and testing of real-time fMRI and a NIRS-BMI opened an exciting field of interest in patients with psychopathological conditions.

Via Sentient Development

The BSI-Toyota Collaboration Center (BTCC) is developing a wheelchair that can be navigated in real-time with brain waves. The brain-controlled device can adjust itself to the characteristics of each individual user, thereby improving the efficiency with which it senses the driver's commands. That way, the driver is able to get the system to learn his/her commands (forward/right/left) quickly and efficiently; the system boasts an accuracy rate of 95%.

Improving the performance of brain-computer interface through meditation practicing.

Conf Proc IEEE Eng Med Biol Soc. 2008;1:662-5

Authors: Eskandari P, Erfanian A

Cognitive tasks using motor imagery have been used for generating and controlling EEG activity in most brain-computer interface (BCI). Nevertheless, during the performance of a particular mental task, different factors such as concentration, attention, level of consciousness and the difficulty of the task, may be affecting the changes in the EEG activity. Accordingly, training the subject to consistently and reliably produce and control the changes in the EEG signals is a critical issue in developing a BCI system. In this work, we used meditation practice to enhance the mind controllability during the performance of a mental task in a BCI system. The mental states to be discriminated are the imaginative hand movement and the idle state. The experiments were conducted on two groups of subject, meditation group and control group. The time-frequency analysis of EEG signals for meditation practitioners showed an event-related desynchronization (ERD) of beta rhythm before imagination during resting state. In addition, a strong event-related synchronization (ERS) of beta rhythm was induced in frequency around 25 Hz during hand motor imagery. The results demonstrated that the meditation practice can improve the classification accuracy of EEG patterns. The average classification accuracy was 88.73% in the meditation group, while it was 70.28% in the control group. An accuracy as high as 98.0% was achieved in the meditation group.

Functional network reorganization during learning in a brain-computer interface paradigm.

Proc Natl Acad Sci U S A. 2008 Dec 1;

Authors: Jarosiewicz B, Chase SM, Fraser GW, Velliste M, Kass RE, Schwartz AB

Efforts to study the neural correlates of learning are hampered by the size of the network in which learning occurs. To understand the importance of learning-related changes in a network of neurons, it is necessary to understand how the network acts as a whole to generate behavior. Here we introduce a paradigm in which the output of a cortical network can be perturbed directly and the neural basis of the compensatory changes studied in detail. Using a brain-computer interface, dozens of simultaneously recorded neurons in the motor cortex of awake, behaving monkeys are used to control the movement of a cursor in a three-dimensional virtual-reality environment. This device creates a precise, well-defined mapping between the firing of the recorded neurons and an expressed behavior (cursor movement). In a series of experiments, we force the animal to relearn the association between neural firing and cursor movement in a subset of neurons and assess how the network changes to compensate. We find that changes in neural activity reflect not only an alteration of behavioral strategy but also the relative contributions of individual neurons to the population error signal.

Brain-machine interface via real-time fMRI: Preliminary study on thought-controlled robotic arm.

Neurosci Lett. 2008 Nov 18;

Authors: Lee JH, Ryu J, Jolesz FA, Cho ZH, Yoo SS

Real-time functional MRI (rtfMRI) has been used as a basis for brain-computer interface (BCI) due to its ability to characterize region-specific brain activity in real-time. As an extension of BCI, we present an rtfMRI-based brain-machine interface (BMI) whereby 2-dimensional movement of a robotic arm was controlled by the regulation (and concurrent detection) of regional cortical activations in the primary motor areas. To do so, the subjects were engaged in the right- and/or left-hand motor imagery tasks. The blood oxygenation level dependent (BOLD) signal originating from the corresponding hand motor areas was then translated into horizontal or vertical robotic arm movement. The movement was broadcasted visually back to the subject as a feedback. We demonstrated that real-time control of the robotic arm only through the subjects' thought processes was possible using the rtfMRI-based BMI trials.

Via Textually.org

NeuroSky Inc, a venture company based in San Jose, Calif, prototyped a system that reads brain waves with a sensor and uses them for mobile phone applications.

Software algorithms try to deduce from your brainwaves what you are thinking and pass on the appropriate commands to the cell phone.

Brain motor system function in a patient with complete spinal cord injury following extensive brain-computer interface training.

Exp Brain Res. 2008 Jul 1;

Authors: Enzinger C, Ropele S, Fazekas F, Loitfelder M, Gorani F, Seifert T, Reiter G, Neuper C, Pfurtscheller G, Müller-Putz G

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).