Teegi: Tangible EEG Interface

We introduce Teegi, a Tangible ElectroEncephaloGraphy (EEG) Interface that enables novice users to get to know more about something as complex as brain signals, in an easy, en- gaging and informative way. To this end, we have designed a new system based on a unique combination of spatial aug- mented reality, tangible interaction and real-time neurotech- nologies. With Teegi, a user can visualize and analyze his or her own brain activity in real-time, on a tangible character that can be easily manipulated, and with which it is possible to interact. An exploration study has shown that interacting with Teegi seems to be easy, motivating, reliable and infor- mative. Overall, this suggests that Teegi is a promising and relevant training and mediation tool for the general public.Comment: to appear in UIST-ACM User Interface Software and Technology Symposium, Oct 2014, Honolulu, United State

Brain-Computer Interfaces in Medicine

Brain-computer interfaces (BCIs) acquire brain signals, analyze them, and translate them into commands that are relayed to output devices that carry out desired actions. BCIs do not use normal neuromuscular output pathways. The main goal of BCI is to replace or restore useful function to people disabled by neuromuscular disorders such as amyotrophic lateral sclerosis, cerebral palsy, stroke, or spinal cord injury. From initial demonstrations of electroenceph-alography-based spelling and single-neuron-based device control, researchers have gone on to use electroenceph-alographic, intracortical, electrocorticographic, and other brain signals for increasingly complex control of cursors, robotic arms, prostheses, wheelchairs, and other devices. Brain-computer interfaces may also prove useful for rehabilitation after stroke and for other disorders. In the future, they might augment the performance of surgeons or other medical professionals. Brain-computer interface technology is the focus of a rapidly growing research and development enterprise that is greatly exciting scientists, engineers, clinicians, and the public in general. Its future achievements will depend on advances in 3 crucial areas. Brain-computer interfaces need signal-acquisition hardware that is convenient, portable, safe, and able to function in all environments. Brain-computer interface systems need to be validated in long-term studies of real-world use by people with severe disabilities, and effective and viable models for their widespread dissemination must be implemented. Finally, the day-to-day and moment-to-moment reliability of BCI performance must be improved so that it approaches the reliability of natural muscle-based function

A note on brain actuated spelling with the Berlin brain-computer interface

Brain-Computer Interfaces (BCIs) are systems capable of decoding neural activity in real time, thereby allowing a computer application to be directly controlled by the brain. Since the characteristics of such direct brain-tocomputer interaction are limited in several aspects, one major challenge in BCI research is intelligent front-end design. Here we present the mental text entry application ‘Hex-o-Spell’ which incorporates principles of Human-Computer Interaction research into BCI feedback design. The system utilises the high visual display bandwidth to help compensate for the extremely limited control bandwidth which operates with only two mental states, where the timing of the state changes encodes most of the information. The display is visually appealing, and control is robust. The effectiveness and robustness of the interface was demonstrated at the CeBIT 2006 (world’s largest IT fair) where two subjects operated the mental text entry system at a speed of up to 7.6 char/min

Targeting Neuroplasticity to Improve Motor Recovery after Stroke

After neurological injury, people develop abnormal patterns of neural activity that limit motor recovery. Traditional rehabilitation, which concentrates on practicing impaired skills, is seldom fully effective. New targeted neuroplasticity (TNP) protocols interact with the CNS to induce beneficial plasticity in key sites and thereby enable wider beneficial plasticity. They can complement traditional therapy and enhance recovery. However, their development and validation is difficult because many different TNP protocols are conceivable, and evaluating even one of them is lengthy, laborious, and expensive. Computational models can address this problem by triaging numerous candidate protocols rapidly and effectively. Animal and human empirical testing can then concentrate on the most promising ones. Here we simulate a neural network of corticospinal neurons that control motoneurons eliciting unilateral finger extension. We use this network to (1) study the mechanisms and patterns of cortical reorganization after a stroke, and (2) identify and parameterize a TNP protocol that improves recovery of extension force. After a simulated stroke, standard training produced abnormal bilateral cortical activation and suboptimal force recovery. To enhance recovery, we interdigitated standard trials with trials in which the teaching signal came from a targeted population of sub-optimized neurons. Targeting neurons in secondary motor areas on 5-20% of the total trials restored lateralized cortical activation and improved recovery of extension force. The results illuminate mechanisms underlying suboptimal cortical activity post-stroke; they enable identification and parameterization of the most promising TNP protocols. By providing initial guidance, computational models could facilitate and accelerate realization of new therapies that improve motor recovery

Targeting Neuroplasticity to Improve Motor Recovery after Stroke

After neurological injury, people develop abnormal patterns of neural activity that limit motor recovery. Traditional rehabilitation, which concentrates on practicing impaired skills, is seldom fully effective. New targeted neuroplasticity (TNP) protocols interact with the CNS to induce beneficial plasticity in key sites and thereby enable wider beneficial plasticity. They can complement traditional therapy and enhance recovery. However, their development and validation is difficult because many different TNP protocols are conceivable, and evaluating even one of them is lengthy, laborious, and expensive. Computational models can address this problem by triaging numerous candidate protocols rapidly and effectively. Animal and human empirical testing can then concentrate on the most promising ones. Here we simulate a neural network of corticospinal neurons that control motoneurons eliciting unilateral finger extension. We use this network to (1) study the mechanisms and patterns of cortical reorganization after a stroke, and (2) identify and parameterize a TNP protocol that improves recovery of extension force. After a simulated stroke, standard training produced abnormal bilateral cortical activation and suboptimal force recovery. To enhance recovery, we interdigitated standard trials with trials in which the teaching signal came from a targeted population of sub-optimized neurons. Targeting neurons in secondary motor areas on 5-20% of the total trials restored lateralized cortical activation and improved recovery of extension force. The results illuminate mechanisms underlying suboptimal cortical activity post-stroke; they enable identification and parameterization of the most promising TNP protocols. By providing initial guidance, computational models could facilitate and accelerate realization of new therapies that improve motor recovery

Classification of evoked potentials by Pearson's correlation in a brain-computer interface

International audienceIn this paper, we describe and evaluate the performance of a linear classifier learning technique for use in a brain-computer interface. Electroencephalogram (EEG) signals acquired from individual subjets are analyzed with this technique in order to detect responses to visual stimuli. Signal processing and classification are used for implementing a palliative communication system which allows the individual to spell words. Performance with this technique is evaluated on data collected from eight individuals

Classification des potentiels évoqués par corrélation de Pearson dans une interface cerveau-ordinateur

National audienceDans cette communication, nous décrivons et évaluons les performances d'une technique d'apprentissage des coefficients d'un classifieur linéaire utilisé dans une interface cerveau-ordinateur. Les signaux de l'électroencéphalogramme d'un individu sont analysés au moyen de cette technique afin de mettre en évidence les réponses de ce dernier à des stimuli visuels. Le traitement et la classification des signaux sont utilisés afin d'implanter un système de communication palliative permettant à l'individu d'épeler des mots. Les performances de la méthode de classification ont été évaluées par une expérimentation sur huit personnes

BNCI systems as a potential assistive technology: ethical issues and participatory research in the BrainAble project

This paper highlights aspects related to current research and thinking about ethical issues in relation to Brain Computer Interface (BCI) and Brain-Neuronal Computer Interfaces (BNCI) research through the experience of one particular project, BrainAble, which is exploring and developing the potential of these technologies to enable people with complex disabilities to control computers. It describes how ethical practice has been developed both within the multidisciplinary research team and with participants. Results: The paper presents findings in which participants shared their views of the project prototypes, of the potential of BCI/BNCI systems as an assistive technology, and of their other possible applications. This draws attention to the importance of ethical practice in projects where high expectations of technologies, and representations of “ideal types” of disabled users may reinforce stereotypes or drown out participant “voices”. Conclusions: Ethical frameworks for research and development in emergent areas such as BCI/BNCI systems should be based on broad notions of a “duty of care” while being sufficiently flexible that researchers can adapt project procedures according to participant needs. They need to be frequently revisited, not only in the light of experience, but also to ensure they reflect new research findings and ever more complex and powerful technologies

A comparison of classification techniques for the P300 speller

International audienceThis study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data

Preliminary results using a P300 brain-computer interface speller: a possible interaction effect between presentation paradigm and set of stimuli

Fernández-Rodríguez Á., Medina-Juliá M.T., Velasco-Álvarez F., Ron-Angevin R. (2019) Preliminary Results Using a P300 Brain-Computer Interface Speller: A Possible Interaction Effect Between Presentation Paradigm and Set of Stimuli. In: Rojas I., Joya G., Catala A. (eds) Advances in Computational Intelligence. IWANN 2019. Lecture Notes in Computer Science, vol 11506. Springer, ChamSeveral proposals to improve the performance controlling a P300-based BCI speller have been studied using the standard row-column presentation (RCP) par-adigm. However, this paradigm could not be suitable for those patients with lack of gaze control. To solve that, the rapid serial visual presentation (RSVP) para-digm, which presents the stimuli located in the same position, has been proposed in previous studies. Thus, the aim of the present work is to assess if a stimuli set of pictures that improves the performance in RCP, could also improve the per-formance in a RSVP paradigm. Six participants have controlled four conditions in a calibration task: letters in RCP, pictures in RCP, letters in RSVP and pictures in RSVP. The results showed that pictures in RCP obtained the best accuracy and information transfer rate. The improvement effect given by pictures was greater in the RCP paradigm than in RSVP. Therefore, the improvements reached under RCP may not be directly transferred to the RSVP.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech