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Progress in Brain Research

Damien Coyle
No abstract text is available yet for this article.
2016: Progress in Brain Research
D J McFarland, T M Vaughan
Brain-computer interfaces are systems that use signals recorded from the brain to enable communication and control applications for individuals who have impaired function. This technology has developed to the point that it is now being used by individuals who can actually benefit from it. However, there are several outstanding issues that prevent widespread use. These include the ease of obtaining high-quality recordings by home users, the speed, and accuracy of current devices and adapting applications to the needs of the user...
2016: Progress in Brain Research
A Riccio, F Pichiorri, F Schettini, J Toppi, M Risetti, R Formisano, M Molinari, L Astolfi, F Cincotti, D Mattia
Communication and control of the external environment can be provided via brain-computer interfaces (BCIs) to replace a lost function in persons with severe diseases and little or no chance of recovery of motor abilities (ie, amyotrophic lateral sclerosis, brainstem stroke). BCIs allow to intentionally modulate brain activity, to train specific brain functions, and to control prosthetic devices, and thus, this technology can also improve the outcome of rehabilitation programs in persons who have suffered from a central nervous system injury (ie, stroke leading to motor or cognitive impairment)...
2016: Progress in Brain Research
R Beveridge, S Wilson, D Coyle
A brain-computer interface (BCI) offers movement-free control of a computer application and is achieved by reading and translating the cortical activity of the brain into semantic control signals. Motion-onset visual evoked potentials (mVEP) are neural potentials employed in BCIs and occur when motion-related stimuli are attended visually. mVEP dynamics are correlated with the position and timing of the moving stimuli. To investigate the feasibility of utilizing the mVEP paradigm with video games of various graphical complexities including those of commercial quality, we conducted three studies over four separate sessions comparing the performance of classifying five mVEP responses with variations in graphical complexity and style, in-game distractions, and display parameters surrounding mVEP stimuli...
2016: Progress in Brain Research
P Aricò, G Borghini, G Di Flumeri, A Colosimo, S Pozzi, F Babiloni
In the last decades, it has been a fast-growing concept in the neuroscience field. The passive brain-computer interface (p-BCI) systems allow to improve the human-machine interaction (HMI) in operational environments, by using the covert brain activity (eg, mental workload) of the operator. However, p-BCI technology could suffer from some practical issues when used outside the laboratories. In particular, one of the most important limitations is the necessity to recalibrate the p-BCI system each time before its use, to avoid a significant reduction of its reliability in the detection of the considered mental states...
2016: Progress in Brain Research
R M Gibson, A M Owen, D Cruse
The disorders of consciousness refer to clinical conditions that follow a severe head injury. Patients diagnosed as in a vegetative state lack awareness, while patients diagnosed as in a minimally conscious state retain fluctuating awareness. However, it is a challenge to accurately diagnose these disorders with clinical assessments of behavior. To improve diagnostic accuracy, neuroimaging-based approaches have been developed to detect the presence or absence of awareness in patients who lack overt responsiveness...
2016: Progress in Brain Research
M R Hohmann, T Fomina, V Jayaram, N Widmann, C Förster, J Just, M Synofzik, B Schölkopf, L Schöls, M Grosse-Wentrup
Brain-computer interfaces (BCIs) are often based on the control of sensorimotor processes, yet sensorimotor processes are impaired in patients suffering from amyotrophic lateral sclerosis (ALS). We devised a new paradigm that targets higher-level cognitive processes to transmit information from the user to the BCI. We instructed five ALS patients and twelve healthy subjects to either activate self-referential memories or to focus on a process without mnemonic content while recording a high-density electroencephalogram (EEG)...
2016: Progress in Brain Research
H Yang, K K Ang, C Wang, K S Phua, C Guan
Swallowing is an essential function in our daily life; nevertheless, stroke or other neurodegenerative diseases can cause the malfunction of swallowing function, ie, dysphagia. The objectives of this review are to understand the neural and cortical basis of swallowing and tongue, and review the latest techniques on the detection of motor imagery of swallow (MI-SW) and motor imagery of tongue movements (MI-TM), so that a practical system can be developed for the rehabilitation of poststroke dysphagia patients...
2016: Progress in Brain Research
J Ushiba, S R Soekadar
Noninvasive brain-machine interfaces (BMIs) are typically associated with neuroprosthetic applications or communication aids developed to assist in daily life after loss of motor function, eg, in severe paralysis. However, BMI technology has recently been found to be a powerful tool to promote neural plasticity facilitating motor recovery after brain damage, eg, due to stroke or trauma. In such BMI paradigms, motor cortical output and input are simultaneously activated, for instance by translating motor cortical activity associated with the attempt to move the paralyzed fingers into actual exoskeleton-driven finger movements, resulting in contingent visual and somatosensory feedback...
2016: Progress in Brain Research
U Chaudhary, N Birbaumer, A Ramos-Murguialday
Brain-computer interfaces (BCIs) use brain activity to control external devices, facilitating paralyzed patients to interact with the environment. In this chapter, we discuss the historical perspective of development of BCIs and the current advances of noninvasive BCIs for communication in patients with amyotrophic lateral sclerosis and for restoration of motor impairment after severe stroke. Distinct techniques have been explored to control a BCI in patient population especially electroencephalography (EEG) and more recently near-infrared spectroscopy (NIRS) because of their noninvasive nature and low cost...
2016: Progress in Brain Research
H A Agashe, A Y Paek, J L Contreras-Vidal
Upper limb amputation results in a severe reduction in the quality of life of affected individuals due to their inability to easily perform activities of daily living. Brain-machine interfaces (BMIs) that translate grasping intent from the brain's neural activity into prosthetic control may increase the level of natural control currently available in myoelectric prostheses. Current BMI techniques demonstrate accurate arm position and single degree-of-freedom grasp control but are invasive and require daily recalibration...
2016: Progress in Brain Research
A Korik, R Sosnik, N Siddique, D Coyle
A motion trajectory prediction (MTP) - based brain-computer interface (BCI) aims to reconstruct the three-dimensional (3D) trajectory of upper limb movement using electroencephalography (EEG). The most common MTP BCI employs a time series of bandpass-filtered EEG potentials (referred to here as the potential time-series, PTS, model) for reconstructing the trajectory of a 3D limb movement using multiple linear regression. These studies report the best accuracy when a 0.5-2Hz bandpass filter is applied to the EEG...
2016: Progress in Brain Research
G R Müller-Putz, A Schwarz, J Pereira, P Ofner
In this chapter, we give an overview of the Graz-BCI research, from the classic motor imagery detection to complex movement intentions decoding. We start by describing the classic motor imagery approach, its application in tetraplegic end users, and the significant improvements achieved using coadaptive brain-computer interfaces (BCIs). These strategies have the drawback of not mirroring the way one plans a movement. To achieve a more natural control-and to reduce the training time-the movements decoded by the BCI need to be closely related to the user's intention...
2016: Progress in Brain Research
C Jeunet, B N'Kaoua, F Lotte
While being very promising for a wide range of applications, mental-imagery-based brain-computer interfaces (MI-BCIs) remain barely used outside laboratories, notably due to the difficulties users encounter when attempting to control them. Indeed, 10-30% of users are unable to control MI-BCIs (so-called BCI illiteracy) while only a small proportion reach acceptable control abilities. This huge interuser variability has led the community to investigate potential predictors of performance related to users' personality and cognitive profile...
2016: Progress in Brain Research
Marinella Cappelletti, Wim Fias
No abstract text is available yet for this article.
2016: Progress in Brain Research
C Y Looi, R Cohen Kadosh
Mathematical abilities that are correlated with various life outcomes vary across individuals. One approach to improve mathematical abilities is by understanding the underlying cognitive functions. Theoretical and experimental evidence suggest that mathematical abilities are subserved by "core" and "noncore" skills. Core skills are commonly regarded as the "innate" capacity to attend to and process numerical information, while noncore skills are those that are important for mathematical cognition, but are not exclusive to the mathematical domain such as executive functions, spatial skills, and attention...
2016: Progress in Brain Research
D C Hyde, I Berteletti, Y Mou
Humans have the ability to nonverbally represent the approximate numerosity of sets of objects. The cognitive system that supports this ability, often referred to as the approximate number system (ANS), is present in early infancy and continues to develop in precision over the life span. It has been proposed that the ANS forms a foundation for uniquely human symbolic number and mathematics learning. Recent work has brought two types of evidence to bear on the relationship between the ANS and human mathematics: correlational studies showing individual differences in approximate numerical abilities correlate with individual differences in mathematics achievement and experimental studies showing enhancing effects of nonsymbolic approximate numerical training on exact, symbolic mathematical abilities...
2016: Progress in Brain Research
T Iuculano
Numbers are one of the most pervasive stimulus categories in our environment and an integral foundation of modern society. Yet, up to 20% of individuals fail to understand, represent, and manipulate numbers and form the basis of arithmetic, a condition termed developmental dyscalculia (DD). Multiple cognitive and neural systems including those that serve numerical, mnemonic, visuospatial, and cognitive control functions have independently been implicated in the etiology of DD, yet most studies have not taken a comprehensive or dynamic view of the disorder...
2016: Progress in Brain Research
D Szűcs
A large body of research suggests that mathematical learning disability (MLD) is related to working memory impairment. Here, I organize part of this literature through a meta-analysis of 36 studies with 665 MLD and 1049 control participants. I demonstrate that one subtype of MLD is associated with reading problems and weak verbal short-term and working memory. Another subtype of MLD does not have associated reading problems and is linked to weak visuospatial short-term and working memory. In order to better understand MLD we need to precisely define potentially modality-specific memory subprocesses and supporting executive functions, relevant for mathematical learning...
2016: Progress in Brain Research
T Hinault, P Lemaire
In this review, we provide an overview of how age-related changes in executive control influence aging effects in arithmetic processing. More specifically, we consider the role of executive control in strategic variations with age during arithmetic problem solving. Previous studies found that age-related differences in arithmetic performance are associated with strategic variations. That is, when they accomplish arithmetic problem-solving tasks, older adults use fewer strategies than young adults, use strategies in different proportions, and select and execute strategies less efficiently...
2016: Progress in Brain Research
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